sinkhorn GPU implementation

28 files changed, 22495 insertions, 0 deletions

diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py
new file mode 100644
index 0000000..9319208
--- /dev/null
+++ b/ot/gpu/__init__.py
@@ -0,0 +1,6 @@
+# -*- coding: utf-8 -*-
+
+from . import bregman
+from . import da
+
+__all__ = ["bregman", "da"]
diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py
new file mode 100644
index 0000000..3fdf11b
--- /dev/null
+++ b/ot/gpu/bregman.py
@@ -0,0 +1,83 @@
+# -*- coding: utf-8 -*-
+"""
+Bregman projections for regularized OT with GPU
+"""
+
+import numpy as np
+
+
+def sinkhornGPU(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False,
+                log=False, cudamat=None):
+    # init data
+    Nini = len(a)
+    Nfin = len(b)
+
+    if log:
+        log = {'err': []}
+
+    # we assume that no distances are null except those of the diagonal of
+    # distances
+    u = (np.ones(Nini)/Nini).reshape((Nini, 1))
+    u_GPU = cudamat.CUDAMatrix(u)
+    a_GPU = cudamat.CUDAMatrix(a.reshape((Nini, 1)))
+    ones_GPU = cudamat.empty(u_GPU.shape).assign(1)
+    v = (np.ones(Nfin)/Nfin).reshape((Nfin, 1))
+    v_GPU = cudamat.CUDAMatrix(v)
+    b_GPU = cudamat.CUDAMatrix(b.reshape((Nfin, 1)))
+
+    M_GPU.divide(-reg)
+
+    K_GPU = cudamat.exp(M_GPU)
+
+    ones_GPU.divide(a_GPU, target=a_GPU)
+    Kp_GPU = cudamat.empty(K_GPU.shape)
+    K_GPU.mult_by_col(a_GPU, target=Kp_GPU)
+
+    tmp_GPU = cudamat.empty(K_GPU.shape)
+
+    cpt = 0
+    err = 1
+    while (err > stopThr and cpt < numItermax):
+        uprev_GPU = u_GPU.copy()
+        vprev_GPU = v_GPU.copy()
+
+        KtransposeU_GPU = K_GPU.transpose().dot(u_GPU)
+        b_GPU.divide(KtransposeU_GPU, target=v_GPU)
+        ones_GPU.divide(Kp_GPU.dot(v_GPU), target=u_GPU)
+
+        if (np.any(KtransposeU_GPU.asarray() == 0) or
+           not u_GPU.allfinite() or not v_GPU.allfinite()):
+            # we have reached the machine precision
+            # come back to previous solution and quit loop
+            print('Warning: numerical errors at iteration', cpt)
+            u_GPU = uprev_GPU.copy()
+            v_GPU = vprev_GPU.copy()
+            break
+        if cpt % 10 == 0:
+            # we can speed up the process by checking for the error only all
+            # the 10th iterations
+            K_GPU.mult_by_col(u_GPU, target=tmp_GPU)
+            tmp_GPU.mult_by_row(v_GPU.transpose(), target=tmp_GPU)
+
+            bcopy_GPU = b_GPU.copy().transpose()
+            bcopy_GPU.add_sums(tmp_GPU, axis=0, beta=-1)
+            err = bcopy_GPU.euclid_norm()**2
+            if log:
+                log['err'].append(err)
+
+            if verbose:
+                if cpt % 200 == 0:
+                    print('{:5s}|{:12s}'.format('It.', 'Err')+'\n'+'-'*19)
+                print('{:5d}|{:8e}|'.format(cpt, err))
+        cpt += 1
+    if log:
+        log['u'] = u_GPU.asarray()
+        log['v'] = v_GPU.asarray()
+
+    # print('err=',err,' cpt=',cpt)
+    K_GPU.mult_by_col(u_GPU, target=K_GPU)
+    K_GPU.mult_by_row(v_GPU.transpose(), target=K_GPU)
+    if log:
+        return K_GPU.asarray(), log
+    else:
+        return K_GPU.asarray()
diff --git a/ot/gpu/cudamat/.gitignore b/ot/gpu/cudamat/.gitignore
new file mode 100644
index 0000000..7a10978
--- /dev/null
+++ b/ot/gpu/cudamat/.gitignore
@@ -0,0 +1,38 @@
+*.py[cod]
+
+# C extensions
+*.so
+
+# Packages
+*.egg
+*.egg-info
+dist
+build
+eggs
+parts
+bin
+var
+sdist
+develop-eggs
+.installed.cfg
+lib
+lib64
+
+# Installer logs
+pip-log.txt
+
+# Unit test / coverage reports
+.coverage
+.tox
+nosetests.xml
+
+# Translations
+*.mo
+
+# Mr Developer
+.mr.developer.cfg
+.project
+.pydevproject
+
+# Vim
+*.swp
diff --git a/ot/gpu/cudamat/CHANGELOG b/ot/gpu/cudamat/CHANGELOG
new file mode 100644
index 0000000..446df0f
--- /dev/null
+++ b/ot/gpu/cudamat/CHANGELOG
@@ -0,0 +1,41 @@
+Version 0.3
+- Added equality testing. Contributed by Ryan P. Adams.
+- Added flag that determines whether threads are synced after each call.
+- Added direct blas calls for two elementwise operations. Contributed by Vincent Vanhoucke.
+- Added the set_selected_columns. Contributed by Tijmen Tieleman.
+- Added tanh, abs, and log_1_plus_exp. Contributed by Ilya Sutskever.
+- Some bug fixes. Contributed by Jonathan Taylor.
+- Added the select_columns method. Code contributed by Tijmen Tieleman.
+- on_device should now work as intended.
+- allocate_device_memory now returns an error when cublasAlloc fails.
+- Fixed bug in max that showed up when an entire column was negative.
+- Fixed bug in activation computations in examples/rbm_numpy.py.
+- Added get_col_slice and set_col_slice methods.
+- Added init and shutdown methods to shorten cublas_init and cublas_shutdown.
+- Added bound checking to the various slicing methods.
+- Fixed problem with pow and negative numbers.
+- Added support for matrix powers in pow.
+
+Version 0.2
+- Methods add, subtract, mult, divide can now take scalars as well as instances of CUDAMatrix.
+- Deprecated add_scalar, mult_by_scalar, div_by_scalar.
+- Methods now return target or self to make chaining operations easier.
+- Added asarray method.
+- Added transpose method.
+- Added sqrt and pow functions.
+- Added the sigmoid method to the module level.
+- Added add_row_vec.
+- Added empty. Now when you don't provide a target or pre-allocated temporary storage cudamat methods will not take up CPU RAM or transfer anything between the CPU and GPU.
+- Added get_row_slice and set_row_slice.
+- Added less_than_scalar, greater_than, greater_than_scalar.
+- Added max (axis=1 is currently not supported.)
+
+Version 0.1.5
+- Added shape attribute and reshape method.
+
+Version 0.1
+- Most methods now throw python exceptions instead of exiting after encountering an error.
+- The CUDAMatrix constructor now automatically converts ndarray objects to float32 in FORTRAN order.
+- Renamed scalar_mult to mult_by_scalar and scalar_div to div_by_scalar.
+- Added log and exp functions.
+- Removed add_row_sums and sum_rows.
diff --git a/ot/gpu/cudamat/CONTRIBUTE.md b/ot/gpu/cudamat/CONTRIBUTE.md
new file mode 100644
index 0000000..a98a39f
--- /dev/null
+++ b/ot/gpu/cudamat/CONTRIBUTE.md
@@ -0,0 +1,74 @@
+Development installation
+------------------------
+
+If you want to develop cudamat, you should clone the github repository instead
+of downloading a release. Furthermore, it is useful to install it in editable
+mode. Instead of copying the files somewhere Python can find them, this will
+point Python directly to the directory you install it from. Either of the
+following commands will do:
+
+```bash
+# a) Install for your user in editable mode:
+python setup.py develop --prefix=~/.local
+# b) Install for your user in editable mode, but with pip:
+pip install --user --editable .
+```
+
+As for the [standard installation](INSTALL.md), you can set the `NVCC_FLAGS`
+environment variable to compile for a specific architecture.
+
+Update after local changes
+--------------------------
+
+Your changes to `.py` files will show up immediately the next time you import
+cudamat. Changes to `.cu` and `.cuh` files require a recompilation triggered
+by just running the above installation command again.
+
+Update after remote changes
+---------------------------
+
+To obtain the latest version, just pull in the remote changes:
+
+```bash
+git checkout master
+git fetch origin
+git merge origin/master
+```
+
+Then recompile as per the instructions in the previous section.
+
+Contribute back
+---------------
+
+If you created a great new feature that is useful to the rest of the world,
+and it even comes with docstrings and updated tests, we will gladly incorporate
+it into cudamat. To do that, you will need to send us a pull request from your
+fork.
+
+If you haven't forked cudamat yet, log in to your github account, go to
+https://github.com/cudamat/cudamat and hit the "Fork" button.
+Now instead of having `origin` point to `cudamat/cudamat`, you will want to have
+it point to your fork, and have `upstream` point to the official project:
+
+```bash
+git remote rename origin upstream
+git remote add origin git@github.com:yourusername/cudamat
+git fetch origin
+```
+
+Create a branch to house your changes:
+
+```bash
+git checkout -b my-new-feature
+```
+
+Hack away, then add your changes, commit and push:
+
+```bash
+git add all.py my.py changes.py
+git commit -m 'Added a new feature that does this and that'
+git push origin my-new-feature
+```
+
+Now send us a pull request asking to merge `yourusername:my-new-feature` into
+`cudamat:master` and we will come back to you!
diff --git a/ot/gpu/cudamat/INSTALL.md b/ot/gpu/cudamat/INSTALL.md
new file mode 100644
index 0000000..95cfef3
--- /dev/null
+++ b/ot/gpu/cudamat/INSTALL.md
@@ -0,0 +1,53 @@
+Prerequisites
+-------------
+
+cudamat needs the following to be installed first:
+
+* Python 2.x or 3.x and numpy
+* The CUDA SDK
+* nose for running the tests (optional)
+
+Installation
+------------
+
+Once you have installed the prerequisites and downloaded cudamat, switch to the
+cudamat directory and run either of the following commands to install it:
+
+```bash
+# a) Install for your user:
+python setup.py install --user
+# b) Install for your user, but with pip:
+pip install --user .
+# c) Install system-wide:
+sudo python setup.py install
+# d) Install system-wide, but with pip:
+sudo pip install .
+```
+
+If your Nvidia GPU supports a higher Compute Capability than the default one of
+your CUDA toolkit, you can set the `NVCCFLAGS` environment variable when
+installing cudamat to compile it for your architecture. For example, to install
+for your user for a GTX 780 Ti (Compute Capability 3.5), you would run:
+
+```bash
+NVCCFLAGS=-arch=sm_35 python setup.py install --user
+```
+
+To compile for both Compute Capability 2.0 and 3.5, you would run:
+
+```bash
+NVCCFLAGS="-gencode arch=compute_20,code=sm_20 -gencode arch=compute_35,code=sm_35" ...
+```
+
+Testing
+-------
+
+To test your setup, run the included unit tests and optionally the benchmark:
+
+```bash
+cd test  # so it doesn't try importing cudamat from the source directory
+# Run tests
+nosetests
+# Run benchmark
+python ../examples/bench_cudamat.py
+```
diff --git a/ot/gpu/cudamat/LICENSE b/ot/gpu/cudamat/LICENSE
new file mode 100644
index 0000000..43b4816
--- /dev/null
+++ b/ot/gpu/cudamat/LICENSE
@@ -0,0 +1,21 @@
+Copyright (c) 2009-2013, Volodymyr Mnih
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that 
+the following conditions are met:
+
+    * Redistributions of source code must retain the above copyright notice, this list of conditions and the 
+following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and 
+the following disclaimer in the documentation and/or other materials provided with the distribution.
+    * Neither the name of the <ORGANIZATION> nor the names of its contributors may be used to endorse or 
+promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED 
+WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A 
+PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY 
+DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 
+PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR 
+OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH 
+DAMAGE.
diff --git a/ot/gpu/cudamat/README.md b/ot/gpu/cudamat/README.md
new file mode 100644
index 0000000..9d2dd6f
--- /dev/null
+++ b/ot/gpu/cudamat/README.md
@@ -0,0 +1,65 @@
+CUDAMat
+=======
+
+The aim of the cudamat project is to make it easy to perform basic matrix calculations on CUDA-enabled GPUs from Python. cudamat provides a Python matrix class that performs calculations on a GPU. At present, some of the operations our GPU matrix class supports include:
+
+* Easy conversion to and from instances of `numpy.ndarray`.
+* Limited slicing support.
+* Matrix multiplication and transpose.
+* Elementwise addition, subtraction, multiplication, and division.
+* Elementwise application of exp, log, pow, sqrt.
+* Summation, maximum and minimum along rows or columns.
+* Conversion of CUDA errors into Python exceptions.
+
+The current feature set of cudamat is biased towards features needed for implementing some common machine learning algorithms. We have included implementations of feedforward neural networks and restricted Boltzmann machines in the examples that come with cudamat.
+
+Example:
+
+```python
+import numpy as np
+import cudamat as cm
+
+cm.cublas_init()
+
+# create two random matrices and copy them to the GPU
+a = cm.CUDAMatrix(np.random.rand(32, 256))
+b = cm.CUDAMatrix(np.random.rand(256, 32))
+
+# perform calculations on the GPU
+c = cm.dot(a, b)
+d = c.sum(axis = 0)
+
+# copy d back to the host (CPU) and print
+print(d.asarray())
+```
+
+Documentation
+-------------
+
+An overview of the main features of cudamat can be found in the technical report:
+
+[CUDAMat: A CUDA-based matrix class for Python](http://www.cs.toronto.edu/~vmnih/docs/cudamat_tr.pdf), Volodymyr Mnih, UTML TR 2009-004.
+
+Download
+--------
+
+You can obtain the latest release from the repository by typing:
+
+```bash
+git clone https://github.com/cudamat/cudamat.git
+```
+
+You can also download one of the releases from the [releases](https://github.com/cudamat/cudamat/releases) section.
+
+Installation
+------------
+
+cudamat uses setuptools and can be installed via pip.
+For details, please see [INSTALL.md](INSTALL.md).
+
+Development
+-----------
+
+If you want to contribute new features or improvements, you're welcome to fork
+cudamat on github and send us your pull requests!
+Please see [CONTRIBUTE.md](CONTRIBUTE.md) if you need any help with that.
diff --git a/ot/gpu/cudamat/cudamat/__init__.py b/ot/gpu/cudamat/cudamat/__init__.py
new file mode 100644
index 0000000..13b072d
--- /dev/null
+++ b/ot/gpu/cudamat/cudamat/__init__.py
@@ -0,0 +1,2 @@
+from .cudamat import *
+from . import learn
diff --git a/ot/gpu/cudamat/cudamat/cudamat.cu b/ot/gpu/cudamat/cudamat/cudamat.cu
new file mode 100644
index 0000000..522f9cc
--- /dev/null
+++ b/ot/gpu/cudamat/cudamat/cudamat.cu
@@ -0,0 +1,1633 @@
+#include <stdio.h>
+#include <errno.h>
+#include <string.h>
+#include <stdlib.h>
+#include <cublas.h>
+#include "cudamat_kernels.cuh"
+#include "cudamat.cuh"
+
+extern "C" {
+
+/* ------------------------------ CUBLAS init/shutdown ------------------------------ */
+
+inline bool check_cublas_error() {
+    cublasStatus status = cublasGetError();
+
+    return status != CUBLAS_STATUS_SUCCESS;
+}
+
+inline bool checkCUDAError() {
+    cudaError_t err = cudaGetLastError();
+
+    if (cudaSuccess != err)
+        printf("%s\n", cudaGetErrorString( err));
+    return cudaSuccess != err;
+}
+
+EXPORT const char* get_last_cuda_error() {
+    cudaError_t err = cudaGetLastError();
+
+    return cudaGetErrorString( err);
+}
+
+EXPORT const char* get_last_clib_error() {
+    return strerror(errno);
+}
+
+EXPORT int cublas_init() {
+    cublasInit();
+    if (check_cublas_error())
+        return CUBLAS_ERROR;
+    else
+        return 0;
+}
+
+EXPORT int cublas_shutdown() {
+    cublasShutdown();
+    cudaThreadExit();
+
+    return 0;
+}
+
+
+EXPORT int cuda_set_device(int deviceId) {
+    cudaSetDevice(deviceId);
+    
+    if (checkCUDAError())
+        return CUDA_ERROR;
+    else
+        return 0;
+}
+
+EXPORT int init_random(rnd_struct* rnd_state, int seed, char* cudamatpath) {
+    unsigned int * host_mults;
+    host_mults = (unsigned int*)malloc(NUM_RND_STREAMS * sizeof(unsigned int));
+    FILE * pFile;
+
+    pFile = fopen (cudamatpath,"r");
+    if (pFile == NULL) {
+        return ERROR_FILE_OPEN;
+    }
+
+    for (int i = 0; i < NUM_RND_STREAMS; i++) {
+        if (fscanf (pFile, "%u", &host_mults[i]) != 1) {
+            return ERROR_FILE_SCAN;
+        }
+    }
+    fclose (pFile);
+
+    cublasAlloc(NUM_RND_STREAMS, sizeof(unsigned int), (void**)&rnd_state->dev_mults);
+    cublasAlloc(NUM_RND_STREAMS, sizeof(unsigned long long), (void**)&rnd_state->dev_words);
+    cublasSetVector(NUM_RND_STREAMS, sizeof(unsigned int), host_mults, 1, rnd_state->dev_mults, 1);
+    //cudaMalloc((void **)&rnd_state->dev_mults, NUM_RND_STREAMS * sizeof(unsigned int));
+    //cudaMalloc((void **)&rnd_state->dev_words, NUM_RND_STREAMS * sizeof(unsigned long long));
+    //cudaMemcpy(rnd_state->dev_mults, host_mults, NUM_RND_STREAMS * sizeof(unsigned int), cudaMemcpyHostToDevice);
+    cudaThreadSynchronize();
+
+    kSeedRandom<<<NUM_RND_BLOCKS, NUM_RND_THREADS_PER_BLOCK>>>(rnd_state->dev_mults, rnd_state->dev_words, seed);
+ 
+    cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+    else
+        return 0;
+}
+
+/* ------------------------------ Utility routines ------------------------------ */
+
+EXPORT int get_leading_dimension(cudamat* mat) {
+    return mat->is_trans ? mat->size[1] : mat->size[0];
+}
+
+EXPORT int get_nonleading_dimension(cudamat* mat) {
+    return mat->is_trans ? mat->size[0] : mat->size[1];
+}
+
+EXPORT void set_transpose(cudamat* mat, int is_trans) {
+    mat->is_trans = is_trans;
+}
+
+inline char get_transpose_char(cudamat* mat) {
+    return mat->is_trans ? 't' : 'n';
+}
+
+EXPORT void cuda_sync_threads() {
+    cudaThreadSynchronize();
+}
+
+/* ------------------------------ Allocating/moving data ------------------------------ */
+
+EXPORT int allocate_device_memory(cudamat* mat) {
+    int len = mat->size[0]*mat->size[1];
+
+    cublasStatus stat;
+
+    stat = cublasAlloc(len, sizeof(mat->data_device[0]), (void**)&mat->data_device);
+
+    if (stat != CUBLAS_STATUS_SUCCESS || check_cublas_error()) {
+        checkCUDAError();
+        return CUBLAS_ERROR;
+    }
+
+    mat->on_device = 1;
+    return 0;
+}
+
+EXPORT int copy_to_host(cudamat* mat) {
+    int len = mat->size[0]*mat->size[1];
+
+    if (mat->on_device) {
+            cublasGetVector(len, sizeof(mat->data_host[0]), mat->data_device, 1, mat->data_host, 1);
+
+        if (check_cublas_error())
+            return CUBLAS_ERROR;
+    } else
+       return ERROR_NOT_ON_DEVICE;
+ 
+    return 0;
+}
+
+EXPORT int copy_to_device(cudamat* mat) {
+    int len = mat->size[0]*mat->size[1];
+    int err_code = 0;
+
+    //if (!mat->owns_data)
+    //    return VIEW_ERROR;
+
+    if (!mat->on_device) {
+        err_code = allocate_device_memory(mat);
+        if (err_code)
+            return err_code;
+    }
+
+    cublasSetVector(len, sizeof(mat->data_host[0]), mat->data_host, 1, mat->data_device, 1);
+    
+    if (check_cublas_error())
+        return CUBLAS_ERROR;
+
+    return 0;
+}
+
+EXPORT int copy_on_device(cudamat* mat1, cudamat* mat2) {
+    int len = mat1->size[0]*mat1->size[1];
+
+    if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    cublasDcopy(len, mat1->data_device, 1, mat2->data_device, 1);
+
+    if (check_cublas_error())
+        return CUBLAS_ERROR;
+    else
+        return 0;
+}
+
+EXPORT int get_row_slice(cudamat* source, cudamat* target, unsigned int start, unsigned int end) {
+    int height = source->size[0];
+    int width = source->size[1];
+
+    if ((end - start) != target->size[0] || source->size[1] != target->size[1] || start >= end || end > height)
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    dim3 kernelBlockGrid((int)ceil((end - start)/32.), (int)ceil(width/32.), 1);
+    dim3 kernelBlockDim(32, 1, 1);
+
+    kGetRowSlice<<<kernelBlockGrid,kernelBlockDim>>>(source->data_device, target->data_device, start, end, width, height);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+    else
+        return 0;
+}
+
+EXPORT int set_row_slice(cudamat* source, cudamat* target, unsigned int start, unsigned int end) {
+    int height = target->size[0];
+    int width = target->size[1];
+
+    if ((end - start) != source->size[0] || source->size[1] != target->size[1] || start >= end || end > height)
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    dim3 kernelBlockGrid((int)ceil((end - start)/32.), (int)ceil(width/32.), 1);
+    dim3 kernelBlockDim(32, 1, 1);
+
+    kSetRowSlice<<<kernelBlockGrid,kernelBlockDim>>>(source->data_device, target->data_device, start, end, width, height);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+    else
+        return 0;
+}
+
+EXPORT int copy_transpose(cudamat* source, cudamat* target) {
+    unsigned int height = source->size[0];
+    unsigned int width = source->size[1];
+
+    if (source->size[0] != target->size[1] || source->size[1] != target->size[0])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    // setup execution parameters
+    unsigned int grid_x = height / COPY_BLOCK_SIZE;
+    if (height % COPY_BLOCK_SIZE)
+        grid_x++;
+
+    unsigned int grid_y = width / COPY_BLOCK_SIZE;
+    if (width % COPY_BLOCK_SIZE)
+        grid_y++;
+
+    dim3 grid(grid_x, grid_y, 1);
+    dim3 threads(COPY_BLOCK_SIZE, COPY_BLOCK_SIZE, 1);
+
+    kTranspose<<< grid, threads >>>(target->data_device, source->data_device, height, width);
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+    else
+        return 0;
+}
+
+EXPORT int free_device_memory(cudamat* mat) {
+    if (mat->owns_data && mat->on_device) {
+        cublasStatus stat;
+
+        stat = cublasFree(mat->data_device);
+        mat->on_device = 0;
+
+        if (stat != CUBLAS_STATUS_SUCCESS || check_cublas_error())
+            return CUBLAS_ERROR;
+    }
+
+    return 0;
+}
+
+EXPORT int reshape(cudamat* mat, unsigned int m, unsigned int n) {
+    if (mat->size[0] * mat->size[1] != m * n)
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    mat->size[0] = m;
+    mat->size[1] = n;
+
+    return 0;
+}
+
+EXPORT int get_slice(cudamat* source, cudamat* target, unsigned int first_col, unsigned int last_col) {
+    if (source->is_trans)
+        return ERROR_TRANSPOSED;
+
+    if (!source->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (last_col > source->size[1] || (first_col >= last_col))
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    int num_rows = source->size[0];
+
+    target->data_host = 0;
+    target->data_device = source->data_device + first_col * num_rows;
+    target->on_device = 1;
+    target->on_host = 0;
+    target->size[0] = source->size[0];
+    target->size[1] = last_col - first_col;
+    target->is_trans = 0;
+    target->owns_data = 0;
+
+    return 0;
+}
+
+EXPORT int get_vector_slice(cudamat* source, cudamat* target, unsigned int first_ind, unsigned int last_ind) {
+    // source must be a vector
+    if (source->size[0] > 1 && source->size[1] > 1)
+        return ERROR_GENERIC;
+
+    if (source->is_trans)
+        return ERROR_TRANSPOSED;
+
+    if (!source->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (first_ind >= last_ind)
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    int num_rows = source->size[0];
+
+    target->data_host = 0;
+    target->data_device = source->data_device + first_ind * num_rows;
+    target->on_device = 1;
+    target->on_host = 0;
+    target->is_trans = 0;
+    target->owns_data = 0;
+
+    if (source->size[0] > 1) {
+        if (last_ind > source->size[0])
+            return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+        target->size[0] = last_ind - first_ind;
+        target->size[1] = 1;
+    } else {
+        if (last_ind > source->size[1])
+            return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+        target->size[0] = 1;
+        target->size[1] = last_ind - first_ind;
+    }
+
+    return 0;
+}
+
+/* ------------------------------ Initialization routines ------------------------------ */
+
+EXPORT void init_from_array(cudamat* mat, double* data, int m, int n) {
+    mat->data_host = data;
+    mat->size[0] = m;
+    mat->size[1] = n;
+    mat->on_device = 0;
+    mat->on_host = 1;
+    mat->is_trans = 0;
+    mat->owns_data = 1;
+}
+
+EXPORT int init_empty(cudamat* mat, int m, int n) {
+    mat->size[0] = m;
+    mat->size[1] = n;
+    mat->on_device = 0;
+    mat->on_host = 0;
+    mat->is_trans = 0;
+    mat->owns_data = 1;
+
+    return allocate_device_memory(mat);
+}
+
+/* ------------------------------ Random number generation ------------------------------ */
+EXPORT int fill_with_rand(rnd_struct* rnd_state, cudamat* mat) {
+    int len = mat->size[0] * mat->size[1];
+
+    if (!mat->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    kRandomUniform<<<NUM_RND_BLOCKS,NUM_RND_THREADS_PER_BLOCK>>>(rnd_state->dev_mults, rnd_state->dev_words, mat->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+    else
+        return 0;
+}
+
+EXPORT int fill_with_randn(rnd_struct* rnd_state, cudamat* mat) {
+    int len = mat->size[0] * mat->size[1];
+
+    if (!mat->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    kRandomGaussian<<<NUM_RND_BLOCKS,NUM_RND_THREADS_PER_BLOCK>>>(rnd_state->dev_mults, rnd_state->dev_words, mat->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+    else
+        return 0;
+}
+/* ------------------------------ Algebraic operations ------------------------------ */
+
+EXPORT int add_col_vec(cudamat* mat, cudamat* vec, cudamat* target) {
+    unsigned int h = mat->size[0],
+                 w = mat->size[1];
+
+    if (!mat->on_device || !vec->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans)
+        return ERROR_TRANSPOSED;
+
+    if (mat->size[0] != vec->size[0] || vec->size[1] != 1 ||
+        mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kAddColVector<<<NUM_VECTOR_OP_BLOCKS(w*h),NUM_VECTOR_OP_THREADS_PER_BLOCK(w*h)>>>(mat->data_device, vec->data_device, target->data_device, w, h);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError()) {
+        return CUDA_ERROR;
+    }
+
+    return 0;
+}
+
+EXPORT int add_col_mult(cudamat* mat, cudamat* vec, cudamat* target, double mult) {
+    unsigned int h = mat->size[0],
+                 w = mat->size[1];
+
+    if (!mat->on_device || !vec->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans)
+        return ERROR_TRANSPOSED;
+
+    if (mat->size[0] != vec->size[0] || vec->size[1] != 1 ||
+        mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kAddColMult<<<NUM_VECTOR_OP_BLOCKS(w*h),NUM_VECTOR_OP_THREADS_PER_BLOCK(w*h)>>>(mat->data_device, vec->data_device, target->data_device, mult, w, h);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int add_row_vec(cudamat* mat, cudamat* vec, cudamat* target) {
+    unsigned int h = mat->size[0],
+                 w = mat->size[1];
+
+    if (!mat->on_device || !vec->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans)
+        return ERROR_TRANSPOSED;
+
+    if (mat->size[1] != vec->size[1] || vec->size[0] != 1 ||
+        mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kAddRowVector<<<NUM_VECTOR_OP_BLOCKS(w*h),NUM_VECTOR_OP_THREADS_PER_BLOCK(w*h)>>>(mat->data_device, vec->data_device, target->data_device, w, h);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int mult_by_col_vec(cudamat* mat, cudamat* vec, cudamat* target) {
+    unsigned int h = mat->size[0],
+                 w = mat->size[1];
+
+    if (!mat->on_device || !vec->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans)
+        return ERROR_TRANSPOSED;
+
+    if (mat->size[0] != vec->size[0] || vec->size[1] != 1 ||
+        mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kMultByColVector<<<NUM_VECTOR_OP_BLOCKS(w*h),NUM_VECTOR_OP_THREADS_PER_BLOCK(w*h)>>>(mat->data_device, vec->data_device, target->data_device, w, h);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int mult_by_row_vec(cudamat* mat, cudamat* vec, cudamat* target) {
+    unsigned int h = mat->size[0],
+                 w = mat->size[1];
+
+    if (!mat->on_device || !vec->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans)
+        return ERROR_TRANSPOSED;
+
+    if (mat->size[1] != vec->size[1] || vec->size[0] != 1 ||
+        mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kMultByRowVector<<<NUM_VECTOR_OP_BLOCKS(w*h),NUM_VECTOR_OP_THREADS_PER_BLOCK(w*h)>>>(mat->data_device, vec->data_device, target->data_device, w, h);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int divide_by_col_vec(cudamat* mat, cudamat* vec, cudamat* target) {
+    unsigned int h = mat->size[0],
+                 w = mat->size[1];
+
+    if (!mat->on_device || !vec->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans)
+        return ERROR_TRANSPOSED;
+
+    if (mat->size[0] != vec->size[0] || vec->size[1] != 1 ||
+        mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kDivByColVector<<<NUM_VECTOR_OP_BLOCKS(w*h),NUM_VECTOR_OP_THREADS_PER_BLOCK(w*h)>>>(mat->data_device, vec->data_device, target->data_device, w, h);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int divide_by_row_vec(cudamat* mat, cudamat* vec, cudamat* target) {
+    unsigned int h = mat->size[0],
+                 w = mat->size[1];
+
+    if (!mat->on_device || !vec->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans)
+        return ERROR_TRANSPOSED;
+
+    if (mat->size[1] != vec->size[1] || vec->size[0] != 1 ||
+        mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kDivByRowVector<<<NUM_VECTOR_OP_BLOCKS(w*h),NUM_VECTOR_OP_THREADS_PER_BLOCK(w*h)>>>(mat->data_device, vec->data_device, target->data_device, w, h);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int less_than(cudamat* mat1, cudamat* mat2, cudamat* target) {
+    int len = mat1->size[0]*mat1->size[1];
+
+    if (!mat1->on_device || !mat2->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat1->is_trans != mat2->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] ||
+        mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kLessThan<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat1->data_device, mat2->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int less_than_scalar(cudamat* mat, double val, cudamat* target) {
+    int len = mat->size[0]*mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans != target->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kLessThanScalar<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, val, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int greater_than(cudamat* mat1, cudamat* mat2, cudamat* target) {
+    int len = mat1->size[0]*mat1->size[1];
+
+    if (!mat1->on_device || !mat2->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat1->is_trans != mat2->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] ||
+        mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kGreaterThan<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat1->data_device, mat2->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int greater_than_scalar(cudamat* mat, double val, cudamat* target) {
+    int len = mat->size[0]*mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans != target->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kGreaterThanScalar<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, val, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int equals(cudamat* mat1, cudamat* mat2, cudamat* target) {
+    int len = mat1->size[0]*mat1->size[1];
+
+    if (!mat1->on_device || !mat2->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat1->is_trans != mat2->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] ||
+        mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kEquals<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat1->data_device, mat2->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int equals_scalar(cudamat* mat, double val, cudamat* target) {
+    int len = mat->size[0]*mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans != target->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kEqualsScalar<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, val, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int minimum(cudamat* mat1, cudamat* mat2, cudamat* target) {
+    int len = mat1->size[0]*mat1->size[1];
+
+    if (!mat1->on_device || !mat2->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat1->is_trans != mat2->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] ||
+        mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kMinimum<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat1->data_device, mat2->data_device, target->data_device, len);
+
+    cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int minimum_scalar(cudamat* mat, double val, cudamat* target) {
+    int len = mat->size[0]*mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans != target->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kMinimumScalar<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, val, target->data_device, len);
+
+    cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int maximum(cudamat* mat1, cudamat* mat2, cudamat* target) {
+    int len = mat1->size[0]*mat1->size[1];
+
+    if (!mat1->on_device || !mat2->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat1->is_trans != mat2->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] ||
+        mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kMaximum<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat1->data_device, mat2->data_device, target->data_device, len);
+
+    cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int maximum_scalar(cudamat* mat, double val, cudamat* target) {
+    int len = mat->size[0]*mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans != target->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kMaximumScalar<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, val, target->data_device, len);
+
+    cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int min_by_axis(cudamat* mat, cudamat* target, int axis) {
+    unsigned int h = mat->size[0],
+                 w = mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans)
+        return ERROR_TRANSPOSED;
+
+    if (axis == 0) {
+        if (target->size[0] != 1 || target->size[1] != mat->size[1])
+            return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+        kMinColumnwise<<<w,32>>>(mat->data_device, target->data_device, w, h);
+    } else {
+        if (target->size[1] != 1 || target->size[0] != mat->size[0])
+            return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+        kMinRowwise<<<h,32>>>(mat->data_device, target->data_device, w, h);
+    }
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int max_by_axis(cudamat* mat, cudamat* target, int axis) {
+    unsigned int h = mat->size[0],
+                 w = mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans)
+        return ERROR_TRANSPOSED;
+
+    if (axis == 0) {
+        if (target->size[0] != 1 || target->size[1] != mat->size[1])
+            return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+        kMaxColumnwise<<<w,32>>>(mat->data_device, target->data_device, w, h);
+    } else {
+        if (target->size[1] != 1 || target->size[0] != mat->size[0])
+            return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+        kMaxRowwise<<<h,32>>>(mat->data_device, target->data_device, w, h);
+    }
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int argmin_by_axis(cudamat* mat, cudamat* target, int axis) {
+    unsigned int h = mat->size[0],
+                 w = mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans)
+        return ERROR_TRANSPOSED;
+
+    if (axis == 0) {
+        if (target->size[0] != 1 || target->size[1] != mat->size[1])
+            return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+        kArgMinColumnwise<<<w,32>>>(mat->data_device, target->data_device, w, h);
+    } else {
+        if (target->size[1] != 1 || target->size[0] != mat->size[0])
+            return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+        kArgMinRowwise<<<h,32>>>(mat->data_device, target->data_device, w, h);
+    }
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int argmax_by_axis(cudamat* mat, cudamat* target, int axis) {
+    unsigned int h = mat->size[0],
+                 w = mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans)
+        return ERROR_TRANSPOSED;
+
+    if (axis == 0) {
+        if (target->size[0] != 1 || target->size[1] != mat->size[1])
+            return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+        kArgMaxColumnwise<<<w,32>>>(mat->data_device, target->data_device, w, h);
+    } else {
+        if (target->size[1] != 1 || target->size[0] != mat->size[0])
+            return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+        kArgMaxRowwise<<<h,32>>>(mat->data_device, target->data_device, w, h);
+    }
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int sign(cudamat* mat, cudamat* target) {
+    int len = mat->size[0]*mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->is_trans != target->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kSign<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int apply_sigmoid(cudamat* mat, cudamat* target) {
+    unsigned int len = mat->size[0] * mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kApplySigmoid<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int apply_tanh(cudamat* mat, cudamat* target) {
+    unsigned int len = mat->size[0] * mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kApplyTanh<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int apply_soft_threshold(cudamat* mat, double alpha, cudamat* target) {
+    unsigned int len = mat->size[0] * mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kApplySoftThreshold<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, alpha, target->data_device, len);
+
+    cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int apply_abs(cudamat* mat, cudamat* target) {
+    unsigned int len = mat->size[0] * mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kApplyAbs<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int apply_log_1_plus_exp(cudamat* mat, cudamat* target) {
+    unsigned int len = mat->size[0] * mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kApplyLog1PlusExp<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int apply_log(cudamat* mat, cudamat* target) {
+    unsigned int len = mat->size[0] * mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kLog<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int apply_exp(cudamat* mat, cudamat* target) {
+    unsigned int len = mat->size[0] * mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kExp<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int apply_gamma(cudamat* mat, cudamat* target) {
+    unsigned int len = mat->size[0] * mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kGamma<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int apply_lgamma(cudamat* mat, cudamat* target) {
+    unsigned int len = mat->size[0] * mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kLogGamma<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int apply_sqrt(cudamat* mat, cudamat* target) {
+    unsigned int len = mat->size[0] * mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kSqrt<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int apply_pow(cudamat* mat, double pow, cudamat* target) {
+    unsigned int len = mat->size[0] * mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kPow<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, pow, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int apply_pow_matrix(cudamat* mat, cudamat* pow, cudamat* target) {
+    unsigned int len = mat->size[0] * mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    if (mat->size[0] != pow->size[0] || mat->size[1] != pow->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kPowMatrix<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, pow->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int reciprocal(cudamat* mat, cudamat* target) {
+    unsigned int len = mat->size[0] * mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kReciprocal<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int dot(cudamat* mat1, cudamat* mat2, cudamat* target, double beta, double alpha) {
+    if (!mat1->on_device || !mat2->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (get_leading_dimension(mat1) != get_leading_dimension(target) ||
+        get_nonleading_dimension(mat2) != get_nonleading_dimension(target) ||
+        get_nonleading_dimension(mat1) != get_leading_dimension(mat2)) {
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+    }
+    int m = get_leading_dimension(mat1),
+        k = get_leading_dimension(mat2),
+        n = get_nonleading_dimension(mat2);
+
+    // gemv if second matrix is a (column) vector
+    if (n == 1) {
+        cublasDgemv(get_transpose_char(mat1), mat1->size[0], mat1->size[1],
+                    alpha, mat1->data_device, mat1->size[0],
+                    mat2->data_device, 1,
+                    beta, target->data_device, 1);
+    }
+    // gemv if first matrix is a (row) vector
+    else if (m == 1) {
+        cublasDgemv(mat2->is_trans ? 'n' : 't', mat2->size[0], mat2->size[1],
+                    alpha, mat2->data_device, mat2->size[0],
+                    mat1->data_device, 1,
+                    beta, target->data_device, 1);
+    }
+    // gemm otherwise
+    else {
+        cublasDgemm(get_transpose_char(mat1), get_transpose_char(mat2),
+                    m, n, k,
+                    alpha, mat1->data_device, mat1->size[0],
+                    mat2->data_device, mat2->size[0],
+                    beta, target->data_device, target->size[0]);
+    }
+
+    if (check_cublas_error())
+        return CUBLAS_ERROR;
+
+    if (SYNC_THREADS) 
+        cudaThreadSynchronize();
+
+    return 0;
+}
+
+EXPORT double vdot(cudamat* mat1, cudamat* mat2, int* err_code) {
+    int len = mat1->size[0]*mat1->size[1];
+    double res;
+
+    if (!mat1->on_device || !mat2->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat1->is_trans != mat2->is_trans) {
+        *err_code = ERROR_TRANSPOSEDNESS;
+        return 0;
+    }
+
+    if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1]) { 
+        *err_code = ERROR_INCOMPATIBLE_DIMENSIONS;
+        return 0;
+    }
+
+    res = cublasDdot(len, mat1->data_device, 1, mat2->data_device, 1);
+
+    if (check_cublas_error()) {
+        *err_code = CUBLAS_ERROR;
+        return -1.;
+    } else {
+        *err_code = 0;
+        return res;
+    }
+}
+
+/* Perform the operation mat1 = mat1 + alpha * mat2. mat1 and mat2 must
+   have the same transposedness. */
+EXPORT int add_mult(cudamat* mat1, cudamat* mat2, double alpha) {
+    int len = mat1->size[0]*mat1->size[1];
+
+    if (!mat1->on_device || !mat2->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat1->is_trans != mat2->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    cublasDaxpy(len, alpha, mat2->data_device, 1, mat1->data_device, 1);
+
+    if (check_cublas_error())
+        return CUBLAS_ERROR;
+
+    return 0;
+}
+
+EXPORT int add_elementwise(cudamat* mat1, cudamat* mat2, cudamat* target) {
+    int len = mat1->size[0]*mat1->size[1];
+
+    if (!mat1->on_device || !mat2->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat1->is_trans != mat2->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] ||
+        mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    if (mat1 == target) {
+        cublasDaxpy(len, 1, mat2->data_device, 1, mat1->data_device, 1);
+ 
+        if (check_cublas_error())
+            return CUBLAS_ERROR;
+
+    } else {
+        kAdd<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat1->data_device, mat2->data_device, target->data_device, len);
+ 
+        if (SYNC_THREADS)
+            cudaThreadSynchronize();
+
+        if (checkCUDAError())
+            return CUDA_ERROR;
+    }
+ 
+     return 0;
+}
+
+EXPORT int subtract_elementwise(cudamat* mat1, cudamat* mat2, cudamat* target) {
+    int len = mat1->size[0]*mat1->size[1];
+
+    if (!mat1->on_device || !mat2->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat1->is_trans != mat2->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] ||
+        mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kSubtract<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat1->data_device, mat2->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int divide_elementwise(cudamat* mat1, cudamat* mat2, cudamat* target) {
+    int len = mat1->size[0]*mat1->size[1];
+
+    if (!mat1->on_device || !mat2->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat1->is_trans != mat2->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] ||
+        mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kDivide<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat1->data_device, mat2->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+/* Elementwise multiplication of 2 matrices */
+EXPORT int mult_elementwise(cudamat* mat1, cudamat* mat2, cudamat* target) {
+    int len = mat1->size[0]*mat1->size[1];
+
+    if (!mat1->on_device || !mat2->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat1->is_trans != mat2->is_trans)
+        return ERROR_TRANSPOSEDNESS;
+
+    if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] ||
+        mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kMult<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat1->data_device, mat2->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int assign_scalar(cudamat* mat, double alpha) {
+    int len = mat->size[0]*mat->size[1];
+
+    if (!mat->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    kAssignScalar<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, alpha, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int mult_by_scalar(cudamat* mat, double alpha, cudamat* target) {
+    int len = mat->size[0]*mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    if (mat == target) {
+        cublasDscal(len, alpha, mat->data_device, 1);
+ 
+        if (check_cublas_error())
+            return CUBLAS_ERROR;
+
+    } else {
+        kMultScalar<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, alpha, target->data_device, len);
+
+        if (SYNC_THREADS) 
+            cudaThreadSynchronize();
+
+        if (checkCUDAError())
+            return CUDA_ERROR;
+    }
+ 
+    return 0;
+}
+
+EXPORT int divide_by_scalar(cudamat* mat, double alpha, cudamat* target) {
+    int len = mat->size[0]*mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kDivideScalar<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, alpha, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int add_scalar(cudamat* mat, double alpha, cudamat* target) {
+    int len = mat->size[0]*mat->size[1];
+
+    if (!mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kAddScalar<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(mat->data_device, alpha, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT double euclid_norm(cudamat* mat, int* err_code) {
+    int len = mat->size[0]*mat->size[1];
+
+    if (!mat->on_device) {
+        *err_code = ERROR_NOT_ON_DEVICE;    
+        return -1.;
+    }
+
+    double res = cublasDnrm2(len, mat->data_device, 1);
+
+    if (check_cublas_error()) {
+        *err_code = CUBLAS_ERROR;
+        return -1.;
+    } else {
+        *err_code = 0;
+        return res;
+    }
+}
+
+EXPORT double manhattan_norm(cudamat* mat, int* err_code) {
+    int len = mat->size[0]*mat->size[1];
+
+    if (!mat->on_device) {
+        *err_code = ERROR_NOT_ON_DEVICE;    
+        return -1.;
+    }
+
+    double res = cublasDasum(len, mat->data_device, 1);
+
+    if (check_cublas_error()) {
+        *err_code = CUBLAS_ERROR;
+        return -1.;
+    } else {
+        *err_code = 0;
+        return res;
+    }
+}
+
+EXPORT int selectRows(cudamat* source, cudamat* target, cudamat* indices){
+    const int nRetRows = indices->size[1];
+
+    if (nRetRows==0) return 0;
+
+    dim3 gridDim((nRetRows+31)/32);
+    dim3 blockDim(32);
+
+    kSelectRows<<<gridDim, blockDim>>>(source->data_device, target->data_device, indices->data_device, nRetRows, source->size[0], source->size[1]);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+    else
+        return 0;
+}
+
+EXPORT int setSelectedRows(cudamat* target, cudamat* source, cudamat* indices){
+    const int nSetRows = indices->size[1];
+
+    if (nSetRows==0)
+        return 0;
+
+    dim3 gridDim((nSetRows+31)/32);
+    dim3 blockDim(32);
+
+    kSetSelectedRows<<<gridDim, blockDim>>>(target->data_device, source->data_device, indices->data_device, nSetRows, target->size[0], target->size[1]);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+    else
+        return 0;
+}
+
+EXPORT int where(cudamat* condition_mat, cudamat* if_mat, cudamat* else_mat, cudamat* target) {
+    unsigned int len = condition_mat->size[0] * condition_mat->size[1];
+
+    if (!condition_mat->on_device || !target->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (condition_mat->size[0] != target->size[0] || condition_mat->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    if (condition_mat->size[0] != if_mat->size[0] || condition_mat->size[1] != if_mat->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+        
+    if (condition_mat->size[0] != else_mat->size[0] || condition_mat->size[1] != else_mat->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kWhere<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(condition_mat->data_device,
+        if_mat->data_device, else_mat->data_device, target->data_device, len);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+EXPORT int correlate(cudamat* source, cudamat* kernel, cudamat* dest) {
+    int len = source->size[0] * source->size[1];
+
+    if (!source->on_device || !kernel->on_device || !dest->on_device)
+        return ERROR_NOT_ON_DEVICE;
+
+    if (source->size[0] != dest->size[0] || source->size[1] != dest->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    if (kernel->size[0] % 2 == 0 || kernel->size[1] % 2 == 0 ||
+        kernel->size[0] > source->size[0] || kernel->size[1] > source->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kCorrelate<<<NUM_VECTOR_OP_BLOCKS(len),NUM_VECTOR_OP_THREADS_PER_BLOCK(len)>>>(source->data_device, 
+        kernel->data_device, dest->data_device, source->size[1], source->size[0], 
+        kernel->size[1], kernel->size[0]);
+
+    if (SYNC_THREADS)
+        cudaThreadSynchronize();
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+     return 0;
+}
+
+}
diff --git a/ot/gpu/cudamat/cudamat/cudamat.cuh b/ot/gpu/cudamat/cudamat/cudamat.cuh
new file mode 100644
index 0000000..e4e2e34
--- /dev/null
+++ b/ot/gpu/cudamat/cudamat/cudamat.cuh
@@ -0,0 +1,35 @@
+#if defined(_WIN32) || defined(__CYGWIN__)
+  #define EXPORT __declspec(dllexport)
+#else
+  #define EXPORT __attribute__ ((visibility("default")))
+#endif
+
+#define SYNC_THREADS 1
+
+#define ERROR_INCOMPATIBLE_DIMENSIONS -1
+#define CUBLAS_ERROR -2
+#define CUDA_ERROR -3
+#define VIEW_ERROR -4
+#define ERROR_TRANSPOSED -5
+#define ERROR_GENERIC -6
+#define ERROR_TRANSPOSEDNESS -7
+#define ERROR_NOT_ON_DEVICE -8
+#define ERROR_UNSUPPORTED -9
+#define ERROR_FILE_OPEN -10
+#define ERROR_FILE_SCAN -11
+
+struct cudamat {
+    double* data_host;
+    double* data_device;
+    int on_device;
+    int on_host;
+    int size[2];
+    int is_trans; // 0 or 1
+    int owns_data;
+};
+
+struct rnd_struct {
+    unsigned int* dev_mults;
+    unsigned long long* dev_words;
+};
+
diff --git a/ot/gpu/cudamat/cudamat/cudamat.py b/ot/gpu/cudamat/cudamat/cudamat.py
new file mode 100644
index 0000000..f855ed2
--- /dev/null
+++ b/ot/gpu/cudamat/cudamat/cudamat.py
@@ -0,0 +1,1575 @@
+import os
+import platform
+import warnings
+import sysconfig
+import sys
+
+import ctypes as ct
+import numpy as np
+
+def load_library(basename):
+    if platform.system() == 'Windows':
+       ext = '.dll'
+    else:
+       ext = sysconfig.get_config_var('SO')
+    return ct.cdll.LoadLibrary(os.path.join(
+        os.path.dirname(__file__) or os.path.curdir,
+        basename + ext))
+
+_cudamat = load_library('libcudamat')
+
+_cudamat.get_last_cuda_error.restype = ct.c_char_p
+_cudamat.get_last_clib_error.restype = ct.c_char_p
+_cudamat.cublas_init.restype = ct.c_int
+_cudamat.cublas_shutdown.restype = ct.c_int
+_cudamat.cuda_set_device.restype = ct.c_int
+_cudamat.init_random.restype = ct.c_int
+
+_cudamat.init_empty.restype = ct.c_int
+_cudamat.reshape.restype = ct.c_int
+_cudamat.copy_to_host.restype = ct.c_int
+_cudamat.allocate_device_memory = ct.c_int
+_cudamat.copy_to_device.restype = ct.c_int
+_cudamat.copy_on_device.restype = ct.c_int
+_cudamat.free_device_memory.restype = ct.c_int
+
+_cudamat.get_slice.restype = ct.c_int
+_cudamat.get_row_slice.restype = ct.c_int
+_cudamat.set_row_slice.restype = ct.c_int
+_cudamat.copy_transpose.restype = ct.c_int
+_cudamat.get_vector_slice.restype = ct.c_int
+_cudamat.fill_with_rand.restype = ct.c_int
+_cudamat.fill_with_randn.restype = ct.c_int
+
+_cudamat.add_col_vec.restype = ct.c_int
+_cudamat.add_col_mult.restype = ct.c_int
+_cudamat.add_row_vec.restype = ct.c_int
+_cudamat.mult_by_col_vec.restype = ct.c_int
+_cudamat.mult_by_row_vec.restype = ct.c_int
+_cudamat.divide_by_col_vec.restype = ct.c_int
+_cudamat.divide_by_row_vec.restype = ct.c_int
+
+_cudamat.less_than.restype = ct.c_int
+_cudamat.less_than_scalar.restype = ct.c_int
+_cudamat.greater_than.restype = ct.c_int
+_cudamat.greater_than_scalar.restype = ct.c_int
+_cudamat.equals.restype = ct.c_int
+_cudamat.equals_scalar.restype = ct.c_int
+_cudamat.minimum.restype = ct.c_int
+_cudamat.minimum_scalar.restype = ct.c_int
+_cudamat.maximum.restype = ct.c_int
+_cudamat.maximum_scalar.restype = ct.c_int
+_cudamat.min_by_axis.restype = ct.c_int
+_cudamat.max_by_axis.restype = ct.c_int
+_cudamat.argmin_by_axis.restype = ct.c_int
+_cudamat.argmax_by_axis.restype = ct.c_int
+_cudamat.sign.restype = ct.c_int
+_cudamat.apply_sigmoid.restype = ct.c_int
+_cudamat.apply_tanh.restype = ct.c_int
+_cudamat.apply_soft_threshold.restype = ct.c_int
+_cudamat.apply_abs.restype = ct.c_int
+_cudamat.apply_log_1_plus_exp.restype = ct.c_int
+_cudamat.apply_log.restype = ct.c_int
+_cudamat.apply_exp.restype = ct.c_int
+_cudamat.apply_gamma.restype = ct.c_int
+_cudamat.apply_lgamma.restype = ct.c_int
+_cudamat.apply_sqrt.restype = ct.c_int
+_cudamat.apply_pow.restype = ct.c_int
+_cudamat.apply_pow_matrix.restype = ct.c_int
+_cudamat.reciprocal.restype = ct.c_int
+
+_cudamat.add_elementwise.restype = ct.c_int
+_cudamat.subtract_elementwise.restype = ct.c_int
+_cudamat.divide_elementwise.restype = ct.c_int
+_cudamat.mult_elementwise.restype = ct.c_int
+_cudamat.assign_scalar.restype = ct.c_int
+_cudamat.mult_by_scalar.restype = ct.c_int
+_cudamat.divide_by_scalar.restype = ct.c_int
+_cudamat.add_scalar.restype = ct.c_int
+
+_cudamat.euclid_norm.restype = ct.c_double
+_cudamat.manhattan_norm.restype = ct.c_double
+_cudamat.selectRows.restype = ct.c_int
+_cudamat.setSelectedRows.restype = ct.c_int
+_cudamat.vdot.restype = ct.c_double
+_cudamat.dot.restype = ct.c_int
+
+_cudamat.where.restype = ct.c_int
+
+_cudamat.correlate.restype = ct.c_int
+
+
+def deprecated(func):
+    """This is a decorator which can be used to mark functions
+    as deprecated. It will result in a warning being emmitted
+    when the function is used."""
+
+    def newFunc(*args, **kwargs):
+        warnings.warn("Call to deprecated function %s." % func.__name__,
+                      category=DeprecationWarning)
+        return func(*args, **kwargs)
+    newFunc.__name__ = func.__name__
+    newFunc.__doc__ = func.__doc__
+    newFunc.__dict__.update(func.__dict__)
+    return newFunc
+
+
+class CUDAMatException(Exception):
+    pass
+
+
+def get_last_cuda_error():
+    errmsg = _cudamat.get_last_cuda_error()
+    if sys.version_info >= (3,):
+        return bytes(errmsg).decode()
+    else:
+        return str(errmsg)
+
+
+def get_last_clib_error():
+    errmsg = _cudamat.get_last_clib_error()
+    if sys.version_info >= (3,):
+        return bytes(errmsg).decode()
+    else:
+        return str(errmsg)
+
+
+def generate_exception(err_code, **kwargs):
+    """
+    Return a CUDAMatException object based on the error code err_code.
+    Additional arguments are error-specific and optional.
+    """
+
+    if err_code == -1:
+        return CUDAMatException("Incompatible matrix dimensions.")
+    elif err_code == -2:
+        return CUDAMatException("CUBLAS error.")
+    elif err_code == -3:
+        return CUDAMatException("CUDA error: " + get_last_cuda_error())
+    elif err_code == -4:
+        return CUDAMatException("Operation not supported on views.")
+    elif err_code == -5:
+        return CUDAMatException("Operation not supported on "
+                                "transposed matrices.")
+    elif err_code == -6:
+        return CUDAMatException("")
+    elif err_code == -7:
+        return CUDAMatException("Incompatible transposedness.")
+    elif err_code == -8:
+        return CUDAMatException("Matrix is not in device memory.")
+    elif err_code == -9:
+        return CUDAMatException("Operation not supported.")
+    elif err_code == -10:
+        filepath = kwargs.get("filepath","");
+        if filepath:
+            filepath = ": '%s'" % filepath
+        return CUDAMatException("Cannot open file%s: %s" % (filepath,get_last_clib_error()))
+    elif err_code == -11:
+        filepath = kwargs.get("filepath","");
+        if filepath:
+            filepath = ": '%s'" % filepath
+        return CUDAMatException("Cannot parse file%s." % filepath)
+    else:
+        return CUDAMatException("")
+
+
+class cudamat(ct.Structure):
+    _fields_ = [('data_host', ct.POINTER(ct.c_double)),
+                ('data_device', ct.POINTER(ct.c_double)),
+                ('on_device', ct.c_int),
+                ('on_host', ct.c_int),
+                ('size', ct.c_int * 2),
+                ('is_trans', ct.c_int),
+                ('owns_data', ct.c_int)]
+
+
+class rnd_struct(ct.Structure):
+    _fields_ = [('dev_rnd_mults', ct.POINTER(ct.c_uint)),
+                ('dev_rnd_words', ct.POINTER(ct.c_longlong))]
+
+
+class TransposedCUDAMatrix(object):
+    def __init__(self, mat):
+        self.mat = cudamat()
+        ct.memmove(ct.pointer(self.mat), ct.pointer(mat), ct.sizeof(self.mat))
+        self.mat.is_trans = 1
+        self.p_mat = ct.pointer(self.mat)
+
+
+class CUDAMatrix(object):
+    """
+    A CUDAMatrix object represents a matrix of single precision floating point
+    numbers on a GPU.
+    """
+
+    def __init__(self, array, copy_to_device=True, copy_on_host=True):
+        """
+        Initializes a new matrix object in one of two ways. If array is a numpy
+        ndarray, memory for a matrix with the same dimensions is allocated on
+        the GPU. If the copy_to_device flag is set to True, the GPU matrix is
+        initialized with the given ndarray. If the copy_on_host flag is set to
+        True, a copy of the matrix will be created in host memory even if the
+        matrix is of the correct type (float64, Fortran-contiguous order).
+        If array is not an ndarray, it must be a cudamat structure (typically
+        the user will never use this way of calling __init__).
+        """
+
+        if type(array) in [np.ndarray, np.memmap]:
+            # Convert array to float64 in FORTRAN order
+            array = reformat(array, copy=copy_on_host)
+
+            # Initialize as a ndarray-tied matrix.
+            self.mat = cudamat()
+            self.size = self.mat.size
+            self.p_mat = ct.pointer(self.mat)
+            self.numpy_array = array
+
+            _cudamat.init_from_array(
+                self.p_mat,
+                array.ctypes.data_as(ct.POINTER(ct.c_double)),
+                ct.c_int(array.shape[0]),
+                ct.c_int(array.shape[1]))
+
+            if copy_to_device:
+                err_code = _cudamat.copy_to_device(self.p_mat)
+                if err_code:
+                    raise generate_exception(err_code)
+
+        else:
+            # Initialize based on existing cudamat structure.
+            mat = array
+            self.mat = mat
+            self.p_mat = ct.pointer(self.mat)
+
+        self.T = TransposedCUDAMatrix(self.mat)
+
+        # Keep a reference to free device memory in case of a crash.
+        self.__free_device_memory = _cudamat.free_device_memory
+
+    def __del__(self):
+        try:
+            if 'p_mat' in self.__dict__:
+                err_code = self.__free_device_memory(self.p_mat)
+                if err_code:
+                    raise generate_exception(err_code)
+        except AttributeError:
+            pass
+
+    @staticmethod
+    def init_random(seed=0):
+        """
+        Initialize and seed the random number generator.
+        """
+
+        CUDAMatrix.rndInitialized = 1
+        CUDAMatrix.rnd_state = rnd_struct()
+        CUDAMatrix.rnd_state_p = ct.pointer(CUDAMatrix.rnd_state)
+
+        cudamat_path = os.path.join(os.path.abspath(os.path.dirname(__file__)),
+                                    'rnd_multipliers_32bit.txt')
+
+        if sys.version_info >= (3,):
+            cudamat_path = cudamat_path.encode(sys.getfilesystemencoding())
+
+        err_code = _cudamat.init_random(CUDAMatrix.rnd_state_p,
+                                        ct.c_int(seed),
+                                        cudamat_path)
+        if err_code:
+            if sys.version_info >= (3,):
+                cudamat_path = cudamat_path.decode(sys.getfilesystemencoding())
+            raise generate_exception(err_code, filepath=cudamat_path)
+
+    @property
+    def shape(self):
+        return (self.mat.size[0], self.mat.size[1])
+
+    def reshape(self, shape):
+        """
+        Reshapes self to have the given shape. The number of elements cannot
+        change as this only changes how the contents are interpreted.
+        """
+
+        m = ct.c_uint(shape[0])
+        n = ct.c_uint(shape[1])
+
+        # Reshape the default matrix
+        err_code = _cudamat.reshape(self.p_mat, m, n)
+        if err_code:
+            raise generate_exception(err_code)
+        # Reshape the transposed matrix
+        err_code = _cudamat.reshape(self.T.p_mat, m, n)
+        if err_code:
+            raise generate_exception(err_code)
+        # Reshape the CPU matrix
+        if self.mat.on_host:
+            self.numpy_array = np.reshape(self.numpy_array, shape, order='F')
+
+        return self
+
+    def asarray(self):
+        """
+        Copies the matrix to an ndarray on the CPU and returns it.
+        """
+
+        self.copy_to_host()
+
+        return self.numpy_array
+
+    def copy_to_device(self):
+        """
+        Copy the matrix to the GPU.
+        """
+
+        err_code = _cudamat.copy_to_device(self.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+    def copy_to_host(self):
+        """
+        Copy the matrix to the CPU.
+        """
+
+        if not self.mat.on_host:
+            # allocate host storage if necessary
+            m = self.mat.size[0]
+            n = self.mat.size[1]
+
+            self.numpy_array = np.empty((m, n), dtype=np.float64, order='F')
+            self.mat.data_host = \
+                self.numpy_array.ctypes.data_as(ct.POINTER(ct.c_double))
+
+            self.mat.on_host = 1
+
+        err_code = _cudamat.copy_to_host(self.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+    def copy(self, include_host=False):
+        """
+        Create a copy of the matrix on GPU. If include_host is True, also
+        creates a copy of the matrix on CPU if there was any.
+        """
+
+        new_mat = empty(self.shape).assign(self)
+
+        if include_host and self.mat.on_host:
+            new_mat.numpy_array = self.numpy_array.copy()
+            new_mat.mat.data_host = \
+                new_mat.numpy_array.ctypes.data_as(ct.POINTER(ct.c_double))
+            new_mat.mat.on_host = 1
+
+        return new_mat
+
+    def assign(self, val):
+        """Assign val to self, where val can be a scalar or a CUDAMatrix
+        with the same dimensions as self. """
+
+        if isinstance(val, CUDAMatrix):
+            err_code = _cudamat.copy_on_device(val.p_mat, self.p_mat)
+        elif isinstance(val, (int, float)):
+            err_code = _cudamat.assign_scalar(self.p_mat, ct.c_double(val))
+        else:
+            raise ValueError("Assigned value must be of type"
+                             "CUDAMatrix, int, or float.")
+
+        if err_code:
+            raise generate_exception(err_code)
+
+        return self
+
+    def free_device_memory(self):
+        """
+        Free memory used up by the matrix on the GPU.
+        """
+
+        err_code = _cudamat.free_device_memory(self.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+    def set_trans(self, is_trans):
+        """
+        Set the transposedness flag to is_trans.
+        """
+
+        _cudamat.set_transpose(self.p_mat, ct.c_int(1 * is_trans))
+
+    def slice(self, first_col, last_col, include_host=False):
+        """
+        Creates a view into a consecutive range of columns of an existing
+        matrix on GPU. If include_host is set to True, also creates a view
+        into the CPU copy of the matrix (i.e., the numpy_array).
+        """
+        mat = cudamat()
+
+        if self.mat.size[0] == 1 or self.mat.size[1] == 1:
+            err_code = _cudamat.get_vector_slice(self.p_mat,
+                                                 ct.pointer(mat),
+                                                 ct.c_int(first_col),
+                                                 ct.c_int(last_col))
+        else:
+            err_code = _cudamat.get_slice(self.p_mat,
+                                          ct.pointer(mat),
+                                          ct.c_int(first_col),
+                                          ct.c_int(last_col))
+
+        if err_code:
+            raise generate_exception(err_code)
+
+        new_mat = CUDAMatrix(mat)
+
+        try:
+            new_mat.sliceof = self.sliceof
+        except:
+            new_mat.sliceof = self
+
+        # reproduce the slice on the host as well (if requested)
+        if include_host and self.mat.on_host:
+            new_mat.numpy_array = self.numpy_array[:, first_col:last_col]
+            new_mat.mat.data_host = \
+                new_mat.numpy_array.ctypes.data_as(ct.POINTER(ct.c_double))
+            new_mat.mat.on_host = 1
+
+        return new_mat
+
+    def get_col_slice(self, first_col, last_col, target=None):
+        """
+        Get the columns with indices first_col through last_col. If a target
+        is provided, columns are copied into the target. Otherwise, returns a
+        view into the existing memory on GPU.
+        """
+        col_slice = self.slice(first_col, last_col)
+
+        if target:
+            target.assign(col_slice)
+            return target
+        else:
+            return col_slice
+
+    def set_col_slice(self, first_col, last_col, mat):
+        """
+        Assign the contents of mat to the columns with indices first_col
+        through last_col.
+        """
+        self.slice(first_col, last_col).assign(mat)
+
+        return self
+
+    def get_row_slice(self, start, end, target=None):
+        """
+        Get the rows with indices start through end. If target is not provided
+        memory for a new matrix will be allocated.
+        """
+
+        width = self.shape[1]
+
+        if not target:
+            target = empty((end-start, width))
+
+        err_code = _cudamat.get_row_slice(self.p_mat,
+                                          target.p_mat,
+                                          ct.c_int(start),
+                                          ct.c_int(end))
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def set_row_slice(self, start, end, mat):
+        """
+        Assign the contents of mat to the rows with indices start through end.
+        """
+
+        err_code = _cudamat.set_row_slice(mat.p_mat, self.p_mat,
+                                          ct.c_int(start), ct.c_int(end))
+        if err_code:
+            raise generate_exception(err_code)
+
+        return self
+
+    def transpose(self, target=None):
+        """
+        Return a transposed copy of the matrix.
+        """
+        if not target:
+            target = empty((self.shape[1], self.shape[0]))
+
+        err_code = _cudamat.copy_transpose(self.p_mat, target.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def fill_with_rand(self):
+        """
+        Fill matrix on the GPU with random numbers drawn from the uniform
+        distribution over the (0,1) interval.
+        """
+
+        err_code = _cudamat.fill_with_rand(CUDAMatrix.rnd_state_p, self.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+        return self
+
+    def fill_with_randn(self):
+        """
+        Fill matrix on the GPU with random numbers drawn from
+        the standard normal distribution.
+        """
+
+        err_code = _cudamat.fill_with_randn(CUDAMatrix.rnd_state_p, self.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+        return self
+
+    def add_col_vec(self, vec, target=None):
+        """
+        Add vector vec to every column of the matrix. If a target is provided,
+        it is used to store the result instead of self.
+        """
+
+        if not target:
+            target = self
+
+        err_code = _cudamat.add_col_vec(self.p_mat, vec.p_mat, target.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def add_col_mult(self, vec, mult, target=None):
+        """
+        Add a multiple of vector vec to every column of the matrix. If a target
+        is provided, it is used to store the result instead of self.
+        """
+
+        if not target:
+            target = self
+
+        err_code = _cudamat.add_col_mult(self.p_mat, vec.p_mat,
+                                         target.p_mat, ct.c_double(mult))
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def add_row_vec(self, vec, target=None):
+        """
+        Add vector vec to every row of the matrix. If a target is provided,
+        it is used to store the result instead of self.
+        """
+
+        if not target:
+            target = self
+
+        err_code = _cudamat.add_row_vec(self.p_mat, vec.p_mat, target.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def mult_by_col(self, vec, target=None):
+        """
+        Multiply vector vec into every column of the matrix. If a target is
+        provided, it is used to store the result instead of self.
+        """
+
+        if not target:
+            target = self
+
+        err_code = _cudamat.mult_by_col_vec(self.p_mat, vec.p_mat, target.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def mult_by_row(self, vec, target=None):
+        """
+        Multiply vector vec into every row of the matrix. If a target is
+        provided, it is used to store the result instead of self.
+        """
+
+        if not target:
+            target = self
+
+        err_code = _cudamat.mult_by_row_vec(self.p_mat, vec.p_mat, target.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def div_by_col(self, vec, target=None):
+        """
+        Divide every column of the matrix by vector vec. If a target is
+        provided, it is used to store the result instead of self.
+        """
+
+        if not target:
+            target = self
+
+        err_code = _cudamat.divide_by_col_vec(self.p_mat, vec.p_mat,
+                                              target.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def div_by_row(self, vec, target=None):
+        """
+        Divide every row of the matrix by vector vec. If a target is
+        provided, it is used to store the result instead of self.
+        """
+
+        if not target:
+            target = self
+
+        err_code = _cudamat.divide_by_row_vec(self.p_mat, vec.p_mat,
+                                              target.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def sum(self, axis, target=None, mult=1.):
+        """
+        Sum the matrix along the given dimension, where 0 represents the leading
+        dimension and 1 represents the non-leading dimension. If a target is
+        not provided, a new vector is created for storing the result. The result
+        is multiplied by the given factor mult (defaults to 1).
+        """
+
+        return sum(self, axis, target, mult)
+
+    def mean(self, axis, target=None):
+        """
+        Compute the mean of the matrix along the given dimension, where 0
+        represents the leading dimension and 1 represents the non-leading
+        dimension. If a target is not provided, a new vector is created for
+        storing the result.
+        """
+
+        return mean(self, axis, target)
+
+    def add_sums(self, mat, axis, mult=1., beta=1.):
+        """
+        Add a multiple of the sums of the matrix mat along the given dimension
+        to self. Self is scaled by beta before adding anything.
+        """
+
+        m = _cudamat.get_leading_dimension(mat.p_mat)
+        n = _cudamat.get_nonleading_dimension(mat.p_mat)
+
+        if axis == 0:
+            # sum along leading dimension
+            check_ones_matrix(m)
+            left = CUDAMatrix.ones.slice(0, m)
+            left.set_trans(True)
+            right = mat
+
+        elif axis == 1:
+            # sum along non-leading dimension
+            left = mat
+            check_ones_matrix(n)
+            right = CUDAMatrix.ones.slice(0, n)
+
+        err_code = _cudamat.dot(left.p_mat, right.p_mat, self.p_mat,
+                                ct.c_double(beta), ct.c_double(mult))
+        if err_code:
+            raise generate_exception(err_code)
+
+        return self
+
+    def less_than(self, val, target=None):
+        """
+        Perform the operation target = 1. * (self < val),
+        where val can be a matrix or a scalar.
+        """
+
+        if not target:
+            target = self
+
+        if isinstance(val, (int, float)):
+            err_code = _cudamat.less_than_scalar(self.p_mat, ct.c_double(val),
+                                                 target.p_mat)
+        else:
+            err_code = _cudamat.less_than(self.p_mat, val.p_mat, target.p_mat)
+
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def greater_than(self, val, target=None):
+        """
+        Perform the operation target = 1. * (self > val),
+        where val can be a matrix or a scalar.
+        """
+
+        if not target:
+            target = self
+
+        if isinstance(val, (int, float)):
+            err_code = _cudamat.greater_than_scalar(self.p_mat,
+                                                    ct.c_double(val),
+                                                    target.p_mat)
+        else:
+            err_code = _cudamat.greater_than(self.p_mat, val.p_mat,
+                                             target.p_mat)
+
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def equals(self, val, target=None):
+        """
+        Perform the operation target = 1. * (self == val),
+        where val can be a matrix or a scalar.
+        """
+
+        if not target:
+            target = self
+
+        if isinstance(val, (int, float)):
+            err_code = _cudamat.equals_scalar(self.p_mat, ct.c_double(val),
+                                              target.p_mat)
+        else:
+            err_code = _cudamat.equals(self.p_mat, val.p_mat, target.p_mat)
+
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def minimum(self, val, target=None):
+        """
+        Perform the element-wise operation target = min(self, val), where
+        val can be a matrix or a scalar.
+        """
+
+        if not target:
+            target = self
+
+        if isinstance(val, (int, float)):
+            err_code = _cudamat.minimum_scalar(self.p_mat, ct.c_double(val),
+                                               target.p_mat)
+        else:
+            err_code = _cudamat.minimum(self.p_mat, val.p_mat, target.p_mat)
+
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def maximum(self, val, target=None):
+        """
+        Perform the element-wise operation target = max(self, val), where
+        val can be a matrix or a scalar.
+        """
+
+        if not target:
+            target = self
+
+        if isinstance(val, (int, float)):
+            err_code = _cudamat.maximum_scalar(self.p_mat, ct.c_double(val),
+                                               target.p_mat)
+        else:
+            err_code = _cudamat.maximum(self.p_mat, val.p_mat, target.p_mat)
+
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def min(self, axis, target=None):
+        """
+        Find the minimum value along the given dimension, where 0 represents the
+        leading dimension and 1 represents the non-leading dimension. If a
+        target is not prvided, a new vector is created for storing the result.
+        """
+
+        m, n = self.shape
+
+        if axis == 0:
+            if not target:
+                target = empty((1, n))
+
+        elif axis == 1:
+            if not target:
+                target = empty((m, 1))
+
+        err_code = _cudamat.min_by_axis(self.p_mat, target.p_mat,
+                                        ct.c_int(axis))
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def max(self, axis, target=None):
+        """
+        Find the maximum value along the given dimension, where 0 represents the
+        leading dimension and 1 represents the non-leading dimension. If a
+        target is not prvided, a new vector is created for storing the result.
+        """
+
+        m, n = self.shape
+
+        if axis == 0:
+            if not target:
+                target = empty((1, n))
+
+        elif axis == 1:
+            if not target:
+                target = empty((m, 1))
+
+        err_code = _cudamat.max_by_axis(self.p_mat, target.p_mat,
+                                        ct.c_int(axis))
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def argmin(self, axis, target=None):
+        """
+        Find the index of the minimum value along the given dimension, where 0
+        represents the leading dimension and 1 represents the non-leading
+        dimension. If a target is not provided, a new vector is created for
+        storing the result.
+        """
+
+        m, n = self.shape
+
+        if axis == 0:
+            if not target:
+                target = empty((1, n))
+
+        elif axis == 1:
+            if not target:
+                target = empty((m, 1))
+
+        err_code = _cudamat.argmin_by_axis(self.p_mat, target.p_mat,
+                                           ct.c_int(axis))
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def argmax(self, axis, target=None):
+        """
+        Find the index of the maximum value along the given dimension, where 0
+        represents the leading dimension and 1 represents the non-leading
+        dimension. If a target is not provided, a new vector is created for
+        storing the result.
+        """
+
+        m, n = self.shape
+
+        if axis == 0:
+            if not target:
+                target = empty((1, n))
+
+        elif axis == 1:
+            if not target:
+                target = empty((m, 1))
+
+        err_code = _cudamat.argmax_by_axis(self.p_mat, target.p_mat,
+                                           ct.c_int(axis))
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def sign(self, target=None):
+        """
+        Find the sign of each element of the matrix.
+        """
+
+        if not target:
+            target = empty((self.mat.size[0], self.mat.size[1]))
+
+        err_code = _cudamat.sign(self.p_mat, target.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def apply_sigmoid(self, target=None):
+        """
+        Apply the logistic sigmoid to each element of the matrix.
+        """
+
+        return sigmoid(self, target)
+
+    def apply_tanh(self, target=None):
+        """
+        Apply the tanh to each element of the matrix.
+        """
+
+        return tanh(self, target)
+
+    def apply_soft_threshold(self, alpha, target=None):
+        """
+        Apply the soft threshold function to each element of the matrix:
+
+        x = sign(x) * max(0, abs(x) - alpha)
+        """
+
+        return soft_threshold(self, alpha, target)
+
+    def reciprocal(self, target=None):
+        """
+        Find the reciprocal of each element of the matrix.
+        """
+
+        if not target:
+            target = self
+
+        err_code = _cudamat.reciprocal(self.p_mat, target.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def dot(self, mat2, target=None):
+        """
+        Multiply the matrix by mat2 from the right.
+        """
+
+        return dot(self, mat2, target)
+
+    def add_dot(self, m1, m2, mult=1., beta=1.):
+        """
+        Add the dot product of m1 and m2 to the matrix, scaled by mult.
+        Self is scaled by beta before adding anything.
+        """
+
+        err_code = _cudamat.dot(m1.p_mat, m2.p_mat, self.p_mat,
+                                ct.c_double(beta), ct.c_double(mult))
+        if err_code:
+            raise generate_exception(err_code)
+
+        return self
+
+    def subtract_dot(self, m1, m2, mult=1., beta=1.):
+        """
+        Subtract the dot product of m1 and m2 from the matrix, scaled by mult.
+        Self is scaled by beta before subtracting anything.
+        """
+
+        return self.add_dot(m1, m2, mult=-1. * mult, beta=beta)
+
+    def add_mult(self, mat2, alpha=1.):
+        """
+        Add multiple of mat2 to the matrix.
+        """
+
+        err_code = _cudamat.add_mult(self.p_mat, mat2.p_mat, ct.c_double(alpha))
+        if err_code:
+            raise generate_exception(err_code)
+
+        return self
+
+    def subtract_mult(self, mat2, alpha=1.):
+        """
+        Subtract a multiple of mat2 from the matrix.
+        """
+
+        err_code = _cudamat.add_mult(self.p_mat, mat2.p_mat,
+                                     ct.c_double(-1. * alpha))
+        if err_code:
+            raise generate_exception(err_code)
+
+        return self
+
+    def add(self, val, target=None):
+        """Add val to self, where val can be a scalar or a CUDAMatrix with the
+        same dimensions as self. """
+
+        if not target:
+            target = self
+
+        if isinstance(val, CUDAMatrix):
+            err_code = _cudamat.add_elementwise(self.p_mat, val.p_mat,
+                                                target.p_mat)
+        elif isinstance(val, (int, float)):
+            err_code = _cudamat.add_scalar(self.p_mat, ct.c_double(val),
+                                           target.p_mat)
+        else:
+            raise ValueError("Value must be of type CUDAMatrix, int, or float.")
+
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def subtract(self, val, target=None):
+        """Subtract val from self, where val can be a scalar or a CUDAMatrix with
+        the same dimensions as self. """
+
+        if not target:
+            target = self
+
+        if isinstance(val, CUDAMatrix):
+            err_code = _cudamat.subtract_elementwise(self.p_mat, val.p_mat,
+                                                     target.p_mat)
+        elif isinstance(val, (int, float)):
+            err_code = _cudamat.add_scalar(self.p_mat, ct.c_double(-1*val),
+                                           target.p_mat)
+        else:
+            raise ValueError("Value must be of type CUDAMatrix, int, or float.")
+
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def divide(self, val, target=None):
+        """Divide self by val, where val can be a scalar or
+        a CUDAMatrix with the same dimensions as self. """
+
+        if not target:
+            target = self
+
+        if isinstance(val, CUDAMatrix):
+            err_code = _cudamat.divide_elementwise(self.p_mat, val.p_mat,
+                                                   target.p_mat)
+        elif isinstance(val, (int, float)):
+            err_code = _cudamat.divide_by_scalar(self.p_mat, ct.c_double(val),
+                                                 target.p_mat)
+        else:
+            raise ValueError("Value must be of type CUDAMatrix, int, or float.")
+
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def mult(self, val, target=None):
+        """Multiply self by val, where val can be a scalar or a CUDAMatrix with
+        the same dimensions as self. """
+
+        if not target:
+            target = self
+
+        if isinstance(val, CUDAMatrix):
+            err_code = _cudamat.mult_elementwise(self.p_mat, val.p_mat,
+                                                 target.p_mat)
+        elif isinstance(val, (int, float)):
+            err_code = _cudamat.mult_by_scalar(self.p_mat, ct.c_double(val),
+                                               target.p_mat)
+        else:
+            raise ValueError("Value must be of type CUDAMatrix, int, or float.")
+
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    @deprecated
+    def assign_scalar(self, alpha):
+        """
+        Assign scalar alpha to every element of the matrix.
+        """
+
+        err_code = _cudamat.assign_scalar(self.p_mat, ct.c_double(alpha))
+        if err_code:
+            raise generate_exception(err_code)
+
+        return self
+
+    @deprecated
+    def mult_by_scalar(self, alpha, target=None):
+        """
+        Multiply the matrix by a scalar.
+        """
+
+        if not target:
+            target = self
+
+        err_code = _cudamat.mult_by_scalar(self.p_mat, ct.c_double(alpha),
+                                           target.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    @deprecated
+    def div_by_scalar(self, alpha, target=None):
+        """
+        Divide the matrix by a scalar.
+        """
+
+        if not target:
+            target = self
+
+        err_code = _cudamat.divide_by_scalar(self.p_mat, ct.c_double(alpha),
+                                             target.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    @deprecated
+    def add_scalar(self, alpha, target=None):
+        """
+        Increment the matrix by a scalar.
+        """
+
+        if not target:
+            target = self
+
+        err_code = _cudamat.add_scalar(self.p_mat, ct.c_double(alpha),
+                                       target.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def euclid_norm(self):
+        """
+        Returns the L2 norm of the matrix flattened to a vector.
+        """
+        err_code = ct.c_int(0)
+        res = _cudamat.euclid_norm(self.p_mat, ct.byref(err_code))
+
+        if err_code:
+            raise generate_exception(err_code.value)
+
+        return res
+
+    def manhattan_norm(self):
+        """
+        Returns the L1 norm of the matrix flattened to a vector.
+        """
+        err_code = ct.c_int(0)
+        res = _cudamat.manhattan_norm(self.p_mat, ct.byref(err_code))
+
+        if err_code:
+            raise generate_exception(err_code.value)
+
+        return res
+
+    def allfinite(self):
+        """
+        Checks if all entries in this matrix are finite, i.e., there is no
+        NaN and no positive or negative infinity.
+        """
+        # Caveat: For a very large matrix of very large finite numbers, the
+        # manhattan norm may overflow and allfinite() may return False.
+        return np.isfinite(self.manhattan_norm())
+
+    def select_columns(self, indices, target):
+        """
+        Copies some columns of self into target.
+        <indices> must be a row vector. Its elements are float64's representing
+        integers, e.g. "34.0" means the integer "34".
+        After this call, for all r,c, target[r,c]=self[r,indices[c]].
+        This returns target.
+        Negative indices are interpreted in the usual Python way: all
+        elements of <indices> had better be in the range
+        [-self.shape[1], self.shape[1]-1].
+        This does bounds checking, but out of bounds indices do not raise an
+        exception (because the programmer was lazy). Instead, they result
+        in NaN values in <target>.
+        """
+
+        err_code = _cudamat.selectRows(self.p_mat, target.p_mat, indices.p_mat)
+
+        if err_code:
+            raise generate_exception(err_code)
+
+        return target
+
+    def set_selected_columns(self, indices, source):
+        """
+        copies all columns of source into some columns of self.
+        <indices> must be a row vector. Its elements are float64's representing
+        integers, e.g. "34.0" means the integer "34". after this call, for all
+        r,c, self[r,indices[c]]=source[r,c]. This returns self.
+        Negative indices are interpreted in the usual Python way: all elements
+        of <indices> had better be in the range
+        [-self.shape[1], self.shape[1]-1].
+        This does bounds checking, but out of bounds indices do not raise an
+        exception (because the programmer was lazy). Instead, they result in NaN
+        values in <self>.
+        """
+
+        err_code = _cudamat.setSelectedRows(self.p_mat, source.p_mat,
+                                            indices.p_mat)
+        if err_code:
+            raise generate_exception(err_code)
+
+        return self
+
+
+def empty(shape):
+    """
+    Creates and returns a new CUDAMatrix with the given shape.
+    """
+
+    mat = cudamat()
+    err_code = _cudamat.init_empty(ct.pointer(mat), ct.c_int(shape[0]),
+                                   ct.c_int(shape[1]))
+
+    if err_code:
+        raise generate_exception(err_code)
+
+    return CUDAMatrix(mat)
+
+
+def check_ones_matrix(min_size):
+    if min_size > CUDAMatrix.ones.shape[0]:
+        raise CUDAMatException(
+            'Not enough memory allocated for reduction. '
+            '({} needed, {} actual), use cudamat.init() '
+            'to allocate more'.format(min_size, CUDAMatrix.ones.shape[0]))
+
+
+def sum(mat, axis, target=None, mult=1.):
+    """
+    Sum the matrix along the given dimension, where 0 represents the leading
+    dimension and 1 represents the non-leading dimension. If a target is
+    not provided, a new vector is created for storing the result. The result
+    is multiplied by the given factor mult (defaults to 1).
+    """
+
+    m = _cudamat.get_leading_dimension(mat.p_mat)
+    n = _cudamat.get_nonleading_dimension(mat.p_mat)
+
+    if axis == 0:
+        # sum along leading dimension
+        check_ones_matrix(m)
+        left = CUDAMatrix.ones.slice(0, m)
+        left.set_trans(True)
+        right = mat
+
+        if not target:
+            target = empty((1, n))
+
+    elif axis == 1:
+        # sum along non-leading dimension
+        left = mat
+        check_ones_matrix(n)
+        right = CUDAMatrix.ones.slice(0, n)
+
+        if not target:
+            target = empty((m, 1))
+
+    err_code = _cudamat.dot(left.p_mat, right.p_mat, target.p_mat,
+                            ct.c_double(0.), ct.c_double(mult))
+    if err_code:
+        raise generate_exception(err_code)
+
+    return target
+
+
+def mean(mat, axis, target=None):
+    """
+    Compute the mean of the matrix along the given dimension, where 0 represents
+    the leading dimension and 1 represents the non-leading dimension. If a
+    target is not provided, a new vector is created for storing the result.
+    """
+
+    return sum(mat, axis, target=target, mult=1. / mat.shape[axis])
+
+
+def dot(m1, m2, target=None, beta=0., alpha=1.):
+    """
+    Find the dot product between m1 and m2 and store in target:
+    target = beta*target + alpha*(m1 m2)
+    If no target is given, it will be created automatically, but not
+    initialized -- so beta should be left at its default value zero.
+    """
+
+    if not target:
+        m = _cudamat.get_leading_dimension(m1.p_mat)
+        n = _cudamat.get_nonleading_dimension(m2.p_mat)
+
+        target = empty((m, n))
+
+    err_code = _cudamat.dot(m1.p_mat, m2.p_mat,
+                            target.p_mat, ct.c_double(beta),
+                            ct.c_double(alpha))
+    if err_code:
+        raise generate_exception(err_code)
+
+    return target
+
+
+def vdot(m1, m2):
+    """
+    Compute the vector dot product of matrices m1 and m2.
+    """
+
+    err_code = ct.c_int(0)
+    res = _cudamat.vdot(m1.p_mat, m2.p_mat, ct.byref(err_code))
+
+    if err_code:
+        raise generate_exception(err_code.value)
+
+    return res
+
+
+def sigmoid(mat, target=None):
+    """
+    Apply the logistic sigmoid to each element of the matrix mat.
+    """
+
+    if not target:
+        target = mat
+
+    err_code = _cudamat.apply_sigmoid(mat.p_mat, target.p_mat)
+    if err_code:
+        raise generate_exception(err_code)
+
+    return target
+
+
+def tanh(mat, target=None):
+    """
+    Apply the tanh to each element of the matrix mat.
+    """
+
+    if not target:
+        target = mat
+
+    err_code = _cudamat.apply_tanh(mat.p_mat, target.p_mat)
+    if err_code:
+        raise generate_exception(err_code)
+
+    return target
+
+
+def soft_threshold(mat, alpha, target=None):
+    """
+    Apply the soft threshold function to each element of the matrix:
+
+    mat = sign(mat) * max(0, abs(mat) - alpha)
+    """
+
+    if not target:
+        target = mat
+
+    err_code = _cudamat.apply_soft_threshold(mat.p_mat, ct.c_double(alpha),
+                                             target.p_mat)
+    if err_code:
+        raise generate_exception(err_code)
+
+    return target
+
+
+def abs(mat, target=None):
+    """
+    Apply abs to each element of the matrix mat.
+    """
+
+    if not target:
+        target = mat
+
+    err_code = _cudamat.apply_abs(mat.p_mat, target.p_mat)
+    if err_code:
+        raise generate_exception(err_code)
+
+    return target
+
+
+def log_1_plus_exp(mat, target=None):
+    """
+    Apply log(1+exp(x)) to each element of the matrix mat.
+    """
+
+    if not target:
+        target = mat
+
+    err_code = _cudamat.apply_log_1_plus_exp(mat.p_mat, target.p_mat)
+    if err_code:
+        raise generate_exception(err_code)
+
+    return target
+
+
+def log(mat, target=None):
+    """
+    Find the natural logarithm of each element of the matrix mat.
+    """
+
+    if not target:
+        target = mat
+
+    err_code = _cudamat.apply_log(mat.p_mat, target.p_mat)
+    if err_code:
+        raise generate_exception(err_code)
+
+    return target
+
+
+def exp(mat, target=None):
+    """
+    Apply the exponential function to each element of the matrix mat.
+    """
+
+    if not target:
+        target = mat
+
+    err_code = _cudamat.apply_exp(mat.p_mat, target.p_mat)
+    if err_code:
+        raise generate_exception(err_code)
+
+    return target
+
+
+def gamma(mat, target=None):
+    """
+    Apply the gamma function to each element of the matrix mat.
+    """
+
+    if not target:
+        target = mat
+
+    err_code = _cudamat.apply_gamma(mat.p_mat, target.p_mat)
+    if err_code:
+        raise generate_exception(err_code)
+
+    return target
+
+
+def lgamma(mat, target=None):
+    """
+    Apply the log gamma function to each element of the matrix mat.
+    """
+
+    if not target:
+        target = mat
+
+    err_code = _cudamat.apply_lgamma(mat.p_mat, target.p_mat)
+    if err_code:
+        raise generate_exception(err_code)
+
+    return target
+
+
+def sqrt(mat, target=None):
+    """
+    Compute the square root of each element of the matrix mat.
+    """
+
+    if not target:
+        target = mat
+
+    err_code = _cudamat.apply_sqrt(mat.p_mat, target.p_mat)
+    if err_code:
+        raise generate_exception(err_code)
+
+    return target
+
+
+def pow(mat, p, target=None):
+    """
+    If p is a scalar, compute the 'p'th power of each element of the matrix mat,
+    otherwise raise each element of the matrix mat to the power given by the
+    corresponding element of the matrix p.
+    """
+
+    if not target:
+        target = mat
+
+    if isinstance(p, CUDAMatrix):
+        err_code = _cudamat.apply_pow_matrix(mat.p_mat, p.p_mat, target.p_mat)
+    elif isinstance(p, (int, float)):
+        err_code = _cudamat.apply_pow(mat.p_mat, ct.c_double(p), target.p_mat)
+    else:
+        raise ValueError("Value must be of type CUDAMatrix, int, or float.")
+
+    if err_code:
+        raise generate_exception(err_code)
+
+    return target
+
+
+def where(condition_mat, if_mat, else_mat, target=None):
+    """
+    For each element i, j, store if_math[i, j] in target[i,j] if
+    condition_mat[i, j] is True, and else_mat[i, j] otherwise.
+    """
+    if not target:
+        target = condition_mat
+
+    err_code = _cudamat.where(condition_mat.p_mat, if_mat.p_mat,
+                              else_mat.p_mat, target.p_mat)
+    if err_code:
+        raise generate_exception(err_code)
+
+    return target
+
+
+def correlate(mat, kernel, target=None):
+    """
+    Cross-correlate a matrix with a kernel matrix.
+    The kernel matrix is centered over each element of the matrix mat.
+    Width and height of the kernel matrix must be an odd integer.
+    If a target is not provided, a new matrix is created for storing the result.
+    Note that this function cannot operate in-place.
+    """
+    if not target:
+        m = _cudamat.get_leading_dimension(mat.p_mat)
+        n = _cudamat.get_nonleading_dimension(mat.p_mat)
+
+        target = empty((m, n))
+
+    err_code = _cudamat.correlate(mat.p_mat, kernel.p_mat, target.p_mat)
+    if err_code:
+        raise generate_exception(err_code)
+
+    return target
+
+
+def cuda_sync_threads():
+    _cudamat.cuda_sync_threads()
+
+
+def reformat(array, copy=True):
+    """
+    Returns array as a float64 array in FORTRAN order.
+    If copy is set to False, the array will only be copied if it is not already
+    in the correct format.
+    """
+    return np.array(array, dtype=np.float64, order='F', copy=copy)
+
+
+def cuda_set_device(dev_id):
+    """
+    Selects the CUDA device with the given ID.
+    """
+
+    err_code = _cudamat.cuda_set_device(ct.c_int(dev_id))
+    if err_code:
+        raise generate_exception(err_code)
+
+
+def cublas_init(max_ones=(1024*256)):
+    """
+    Initialize Cublas.
+
+    'max_ones' is an optional argument that determines the length of
+    the largest sum that can be computed using Cublas matrix multiply.
+    A larger value causes more memory to be allocated for this purpose.
+    """
+
+    err = _cudamat.cublas_init()
+    if err:
+        raise CUDAMatException('error initializing CUBLAS: (err=%u)' % err)
+    CUDAMatrix.ones = empty((max_ones, 1)).assign(1.0)
+
+init = cublas_init
+
+
+def cublas_shutdown():
+    """
+    Shut down Cublas.
+    """
+
+    CUDAMatrix.ones = 0
+    _cudamat.cublas_shutdown()
+
+shutdown = cublas_shutdown
diff --git a/ot/gpu/cudamat/cudamat/cudamat_kernels.cu b/ot/gpu/cudamat/cudamat/cudamat_kernels.cu
new file mode 100644
index 0000000..707b387
--- /dev/null
+++ b/ot/gpu/cudamat/cudamat/cudamat_kernels.cu
@@ -0,0 +1,804 @@
+#include "cudamat_kernels.cuh"
+#include "float.h"
+
+/* ------------------------- Random number generation ------------------------- */
+
+__global__ void kSeedRandom(unsigned int* rndMults, unsigned long long* rndWords, unsigned int seed) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+    // The initial x is the seed and the initial carry is 1
+    unsigned long long rndWord = ((unsigned long long)seed << 32) + 1;
+    const unsigned int rndMult = rndMults[idx];
+    /*
+     * Run the chain for a few steps so that all the streams have a chance
+     * to differentiate. They start out generating similar random numbers
+     * because all the multipliers are similar.
+     */
+    for(unsigned int i = 0; i < NUM_RND_BURNIN; i++) {
+        rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord);
+    }
+    rndWords[idx] = rndWord;
+}
+
+__global__ void kRandomUniform(unsigned int* rndMults, unsigned long long* rndWords, double* gData, unsigned int numElements) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    unsigned long long rndWord = rndWords[idx];
+    const unsigned int rndMult = rndMults[idx];
+
+    for(unsigned int i = idx; i < numElements; i += NUM_RND_STREAMS) {
+        rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord);
+        gData[i] = (__uint2double_rn(LOW_BITS(rndWord)) + 1.0f) / 4294967296.0f;
+    }
+    rndWords[idx] = rndWord;
+}
+
+__global__ void kRandomGaussian(unsigned int* rndMults, unsigned long long* rndWords, double* gData, unsigned int numElements) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    unsigned long long rndWord = rndWords[idx];
+    const unsigned int rndMult = rndMults[idx];
+
+    double rnd1, rnd2, R, T;
+    for(unsigned int i = idx; i < numElements; i += 2*NUM_RND_STREAMS) {
+        rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord);
+        rnd1 = (__uint2double_rn(LOW_BITS(rndWord)) + 1.0f) / 4294967296.0f;
+        rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord);
+        rnd2 = (__uint2double_rn(LOW_BITS(rndWord)) + 1.0f) / 4294967296.0f;
+        T = 2 * PI * rnd2;
+        R = sqrtf(-2 * __logf(rnd1));
+        gData[i] = R * __cosf(T);
+        if (i + NUM_RND_STREAMS < numElements)
+            gData[i + NUM_RND_STREAMS] = R * __sinf(T);
+    }
+    rndWords[idx] = rndWord;
+}
+
+/* ------------------------- Data copying ------------------------- */
+
+/*
+Copy row slice from source to target. There is a block for every 32x32 chunk being copied.
+*/
+__global__ void kGetRowSlice(double* source, double* target, int start, int end, int width, int height) {
+    const int row = start + blockIdx.x * 32 + threadIdx.x;
+    const int start_col = blockIdx.y * 32;
+
+    const int end_col = (start_col + 32 < width) ? start_col + 32: width;
+
+    const int target_height = end - start;
+
+    if (row < end) {
+        for (int cur_col = start_col; cur_col < end_col; cur_col++)
+            target[cur_col * target_height + row - start] = source[cur_col * height + row];
+    }
+}
+
+__global__ void kSetRowSlice(double* source, double* target, int start, int end, int width, int height) {
+    const int row = start + blockIdx.x * 32 + threadIdx.x;
+    const int start_col = blockIdx.y * 32;
+
+    const int end_col = (start_col + 32 < width) ? start_col + 32: width;
+
+    const int source_height = end - start;
+
+    if (row < end) {
+        for (int cur_col = start_col; cur_col < end_col; cur_col++)
+            target[cur_col * height + row] = source[cur_col * source_height + row - start];
+            //source[cur_col * height + row - start] = target[cur_col * target_height + row];
+    }
+}
+
+__global__ void kTranspose(double *odata, double *idata, int width, int height) {
+    __shared__ double block[COPY_BLOCK_SIZE][COPY_BLOCK_SIZE+1];
+
+    // read the matrix tile into shared memory
+    unsigned int xIndex = blockIdx.x * COPY_BLOCK_SIZE + threadIdx.x;
+    unsigned int yIndex = blockIdx.y * COPY_BLOCK_SIZE + threadIdx.y;
+
+    if((xIndex < width) && (yIndex < height)) {
+        unsigned int index_in = yIndex * width + xIndex;
+
+        block[threadIdx.y][threadIdx.x] = idata[index_in];
+    }
+
+    __syncthreads();
+
+    // write the transposed matrix tile to global memory
+    xIndex = blockIdx.y * COPY_BLOCK_SIZE + threadIdx.x;
+    yIndex = blockIdx.x * COPY_BLOCK_SIZE + threadIdx.y;
+
+    if((xIndex < height) && (yIndex < width)) {
+        unsigned int index_out = yIndex * height + xIndex;
+
+        odata[index_out] = block[threadIdx.x][threadIdx.y];
+    }
+}
+
+/* ------------------------- Mathematical operations ------------------------- */
+
+__global__ void kLessThan(double* mat1, double* mat2, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = mat1[i] < mat2[i];
+    }
+}
+
+__global__ void kLessThanScalar(double* mat, double val, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = mat[i] < val;
+    }
+}
+
+__global__ void kGreaterThan(double* mat1, double* mat2, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = mat1[i] > mat2[i];
+    }
+}
+
+__global__ void kGreaterThanScalar(double* mat, double val, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = mat[i] > val;
+    }
+}
+
+__global__ void kEquals(double* mat1, double* mat2, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = mat1[i] == mat2[i];
+    }
+}
+
+__global__ void kEqualsScalar(double* mat, double val, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = mat[i] == val;
+    }
+}
+
+__global__ void kMinimum(double* mat1, double* mat2, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = fminf(mat1[i], mat2[i]);
+    }
+}
+
+__global__ void kMinimumScalar(double* mat, double val, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = fminf(mat[i], val);
+    }
+}
+
+__global__ void kMaximum(double* mat1, double* mat2, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = fmaxf(mat1[i], mat2[i]);
+    }
+}
+
+__global__ void kMaximumScalar(double* mat, double val, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = fmaxf(mat[i], val);
+    }
+}
+
+__global__ void kMinColumnwise(double* mat, double* target, unsigned int width, unsigned int height) {
+    __shared__ double min_vals[32];
+    double cur_min = FLT_MAX;
+    double val = 0;
+ 
+    for (unsigned int i = threadIdx.x; i < height; i += 32) {
+        val = mat[blockIdx.x * height + i];
+
+        if (val < cur_min)
+            cur_min = val;
+    }
+
+    min_vals[threadIdx.x] = cur_min;
+
+    __syncthreads();
+
+    if (threadIdx.x == 0) {
+        cur_min = FLT_MAX;
+
+        for (unsigned int i = 0; i < 32; i++)
+            if (min_vals[i] < cur_min)
+                cur_min = min_vals[i];
+
+        target[blockIdx.x] = cur_min;
+    }
+}
+
+__global__ void kMinRowwise(double* mat, double* target, unsigned int width, unsigned int height) {
+    __shared__ double min_vals[32];
+    double cur_min = FLT_MAX;
+    double val = 0;
+ 
+    for (unsigned int i = threadIdx.x; i < width; i += 32) {
+        val = mat[i * height + blockIdx.x];
+
+        if (val < cur_min)
+            cur_min = val;
+    }
+
+    min_vals[threadIdx.x] = cur_min;
+
+    __syncthreads();
+
+    if (threadIdx.x == 0) {
+        cur_min = FLT_MAX;
+
+        for (unsigned int i = 0; i < 32; i++)
+            if (min_vals[i] < cur_min)
+                cur_min = min_vals[i];
+
+        target[blockIdx.x] = cur_min;
+    }
+}
+
+__global__ void kMaxColumnwise(double* mat, double* target, unsigned int width, unsigned int height) {
+    __shared__ double max_vals[32];
+    double cur_max = -FLT_MAX;
+    double val = 0;
+ 
+    for (unsigned int i = threadIdx.x; i < height; i += 32) {
+        val = mat[blockIdx.x * height + i];
+
+        if (val > cur_max)
+            cur_max = val;
+    }
+
+    max_vals[threadIdx.x] = cur_max;
+
+    __syncthreads();
+
+    if (threadIdx.x == 0) {
+        cur_max = -FLT_MAX;
+
+        for (unsigned int i = 0; i < 32; i++)
+            if (max_vals[i] > cur_max)
+                cur_max = max_vals[i];
+
+        target[blockIdx.x] = cur_max;
+    }
+}
+
+__global__ void kMaxRowwise(double* mat, double* target, unsigned int width, unsigned int height) {
+    __shared__ double max_vals[32];
+    double cur_max = -FLT_MAX;
+    double val = 0;
+ 
+    for (unsigned int i = threadIdx.x; i < width; i += 32) {
+        val = mat[i * height + blockIdx.x];
+
+        if (val > cur_max)
+            cur_max = val;
+    }
+
+    max_vals[threadIdx.x] = cur_max;
+
+    __syncthreads();
+
+    if (threadIdx.x == 0) {
+        cur_max = -FLT_MAX;
+
+        for (unsigned int i = 0; i < 32; i++)
+            if (max_vals[i] > cur_max)
+                cur_max = max_vals[i];
+
+        target[blockIdx.x] = cur_max;
+    }
+}
+
+__global__ void kArgMinColumnwise(double* mat, double* target, unsigned int width, unsigned int height) {
+    __shared__ double min_vals[32];
+    __shared__ unsigned int min_args[32];
+    double cur_min = FLT_MAX;
+    unsigned int cur_arg = 0;
+    double val = 0;
+ 
+    for (unsigned int i = threadIdx.x; i < height; i += 32) {
+        val = mat[blockIdx.x * height + i];
+
+        if (val < cur_min) {
+            cur_min = val;
+            cur_arg = i;
+        }
+    }
+
+    min_vals[threadIdx.x] = cur_min;
+    min_args[threadIdx.x] = cur_arg;
+
+    __syncthreads();
+
+    if (threadIdx.x == 0) {
+        cur_min = FLT_MAX;
+        cur_arg = 0;
+
+        for (unsigned int i = 0; i < 32; i++)
+            if (min_vals[i] < cur_min) {
+                cur_min = min_vals[i];
+                cur_arg = min_args[i];
+            }
+
+        target[blockIdx.x] = cur_arg;
+    }
+}
+
+__global__ void kArgMinRowwise(double* mat, double* target, unsigned int width, unsigned int height) {
+    __shared__ double min_vals[32];
+    __shared__ unsigned int min_args[32];
+    double cur_min = FLT_MAX;
+    unsigned int cur_arg = 0;
+    double val = 0;
+ 
+    for (unsigned int i = threadIdx.x; i < width; i += 32) {
+        val = mat[i * height + blockIdx.x];
+
+        if (val < cur_min) {
+            cur_min = val;
+            cur_arg = i;
+        }
+    }
+
+    min_vals[threadIdx.x] = cur_min;
+    min_args[threadIdx.x] = cur_arg;
+
+    __syncthreads();
+
+    if (threadIdx.x == 0) {
+        cur_min = FLT_MAX;
+        cur_arg = 0;
+
+        for (unsigned int i = 0; i < 32; i++)
+            if (min_vals[i] < cur_min) {
+                cur_min = min_vals[i];
+                cur_arg = min_args[i];
+            }
+
+        target[blockIdx.x] = cur_arg;
+    }
+}
+
+__global__ void kArgMaxColumnwise(double* mat, double* target, unsigned int width, unsigned int height) {
+    __shared__ double max_vals[32];
+    __shared__ unsigned int max_args[32];
+    double cur_max = -FLT_MAX;
+    unsigned int cur_arg = 0;
+    double val = 0;
+ 
+    for (unsigned int i = threadIdx.x; i < height; i += 32) {
+        val = mat[blockIdx.x * height + i];
+
+        if (val > cur_max) {
+            cur_max = val;
+            cur_arg = i;
+        }
+    }
+
+    max_vals[threadIdx.x] = cur_max;
+    max_args[threadIdx.x] = cur_arg;
+
+    __syncthreads();
+
+    if (threadIdx.x == 0) {
+        cur_max = -FLT_MAX;
+        cur_arg = 0;
+
+        for (unsigned int i = 0; i < 32; i++)
+            if (max_vals[i] > cur_max) {
+                cur_max = max_vals[i];
+                cur_arg = max_args[i];
+            }
+
+        target[blockIdx.x] = cur_arg;
+    }
+}
+
+__global__ void kArgMaxRowwise(double* mat, double* target, unsigned int width, unsigned int height) {
+    __shared__ double max_vals[32];
+    __shared__ unsigned int max_args[32];
+    double cur_max = -FLT_MAX;
+    unsigned int cur_arg = 0;
+    double val = 0;
+ 
+    for (unsigned int i = threadIdx.x; i < width; i += 32) {
+        val = mat[i * height + blockIdx.x];
+
+        if (val > cur_max) {
+            cur_max = val;
+            cur_arg = i;
+        }
+    }
+
+    max_vals[threadIdx.x] = cur_max;
+    max_args[threadIdx.x] = cur_arg;
+
+    __syncthreads();
+
+    if (threadIdx.x == 0) {
+        cur_max = -FLT_MAX;
+        cur_arg = 0;
+
+        for (unsigned int i = 0; i < 32; i++)
+            if (max_vals[i] > cur_max) {
+                cur_max = max_vals[i];
+                cur_arg = max_args[i];
+            }
+
+        target[blockIdx.x] = cur_arg;
+    }
+}
+
+__global__ void kSign(double* mat, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = mat[i] ? copysignf(1., mat[i]) : 0.;
+    }
+}
+
+__global__ void kApplySigmoid(double* mat, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = 1 / (1 + __expf(-mat[i]));
+    }
+}
+
+
+__global__ void kApplyTanh(double* mat, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+    double mat_i, exp2x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        mat_i = mat[i];
+        exp2x = __expf(2 * mat_i);
+        target[i] = 1 - 2 / (exp2x + 1);
+    }
+}
+
+__global__ void kApplySoftThreshold(double* mat, double alpha, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        double f = mat[i];
+        target[i] = f > 0 ? max(0., f - alpha) : min(0., f + alpha);
+    }
+}
+
+__global__ void kApplyAbs(double* mat, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+    
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = mat[i] * ((mat[i] > 0) - (mat[i] < 0));
+    }
+}
+
+__global__ void kApplyLog1PlusExp(double* mat, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+    double mat_i;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        mat_i = mat[i];
+        if (mat_i > 0)
+            target[i] = (__logf(1 + __expf(-mat_i)) + mat_i);
+        else
+            target[i] = __logf(1 + __expf(mat_i));
+    }
+}
+
+__global__ void kLog(double* mat, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = __logf(mat[i]);
+    }
+}
+
+__global__ void kExp(double* mat, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = __expf(mat[i]);
+    }
+}
+
+__global__ void kGamma(double* mat, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = tgammaf(mat[i]);
+    }
+}
+
+__global__ void kLogGamma(double* mat, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = lgammaf(mat[i]);
+    }
+}
+
+__global__ void kSqrt(double* mat, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = sqrt(mat[i]);
+    }
+}
+
+__global__ void kPow(double* mat, double pow, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = powf(mat[i], pow);
+    }
+}
+
+__global__ void kPowMatrix(double* mat, double* pow, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = powf(mat[i], pow[i]);
+    }
+}
+
+__global__ void kReciprocal(double* mat, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads)
+        target[i] = 1.f / mat[i];
+}
+
+__global__ void kAddColVector(double* mat, double* vec, double* tgtMat, unsigned int width,
+                              unsigned int height) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < width * height; i += numThreads) {
+        tgtMat[i] = mat[i] + vec[i % height];
+    }
+}
+
+__global__ void kAddRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < width * height; i += numThreads) {
+        tgtMat[i] = mat[i] + vec[i / height];
+    }
+}
+
+__global__ void kAddColMult(double* mat, double* vec, double* tgtMat, double mult,
+                            unsigned int width, unsigned int height) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < width * height; i += numThreads) {
+        tgtMat[i] = mat[i] + mult * vec[i % height];
+    }
+}
+
+__global__ void kMultByColVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < width * height; i += numThreads) {
+        tgtMat[i] = mat[i] * vec[i % height];
+    }
+}
+
+__global__ void kMultByRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < width * height; i += numThreads) {
+        tgtMat[i] = mat[i] * vec[i / height];
+    }
+}
+
+__global__ void kDivByColVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < width * height; i += numThreads) {
+        tgtMat[i] = mat[i] / vec[i % height];
+    }
+}
+
+__global__ void kDivByRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < width * height; i += numThreads) {
+        tgtMat[i] = mat[i] / vec[i / height];
+    }
+}
+
+__global__ void kAdd(double* a, double* b, double* dest, unsigned int numEls) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < numEls; i += numThreads) {
+        dest[i] = a[i] + b[i];
+    }
+}
+
+__global__ void kSubtract(double* a, double* b, double* dest, unsigned int numEls) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < numEls; i += numThreads) {
+        dest[i] = a[i] - b[i];
+    }
+}
+
+__global__ void kDivide(double* a, double* b, double* dest, unsigned int numEls) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < numEls; i += numThreads) {
+        dest[i] = a[i] / b[i];
+    }
+}
+
+__global__ void kMult(double* a, double* b, double* dest, unsigned int numEls) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < numEls; i += numThreads) {
+        dest[i] = a[i] * b[i];
+    }
+}
+
+__global__ void kMultScalar(double* mat, double alpha, double* dest, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        dest[i] = alpha * mat[i];
+    }
+}
+
+__global__ void kAssignScalar(double* dest, double alpha, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        dest[i] = alpha;
+    }
+}
+
+__global__ void kDivideScalar(double* mat, double alpha, double* dest, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        dest[i] = mat[i] / alpha;
+    }
+}
+
+__global__ void kAddScalar(double* a, double alpha, double* dest, unsigned int numEls) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < numEls; i += numThreads) {
+        dest[i] = a[i] + alpha;
+    }
+}
+
+__global__ void kSelectRows(double* source, double* target, double* indices, int nRowIs, int nCols, int nSourceRows){
+    __shared__ int sourceRowIndices[32];
+    const int startTargetRowI = blockIdx.x * 32;
+    const int tid = threadIdx.x;
+    const int localNRowIs = min(32, nRowIs-startTargetRowI);
+
+    // cooperatively load 32 row indices
+    if (tid < localNRowIs){
+        sourceRowIndices[tid] = int(indices[startTargetRowI + tid]);
+        if (sourceRowIndices[tid]<0)
+            sourceRowIndices[tid] += nSourceRows;
+        if (sourceRowIndices[tid]<0 || sourceRowIndices[tid]>=nSourceRows)
+            sourceRowIndices[tid] = -1;
+    }
+    __syncthreads();
+
+    // copy 32 rows
+    for (int i=0; i<localNRowIs; i++){
+        const int targetRowI = startTargetRowI + i, sourceRowI = sourceRowIndices[i];
+        for (int colI=tid; colI<nCols; colI+=32)
+            target[targetRowI * nCols + colI] = sourceRowI==-1 ? (1.0/0.0 -1.0/0.0) : source[sourceRowI * nCols + colI];
+    }
+}
+
+__global__ void kSetSelectedRows(double* target, double* source, double* indices, int nRowIs, int nCols, int nTargetRows){
+    __shared__ int targetRowIndices[32];
+    const int startSourceRowI = blockIdx.x * 32;
+    const int tid = threadIdx.x;
+    const int localNRowIs = min(32, nRowIs-startSourceRowI);
+
+    // cooperatively load 32 row indices
+    if (tid < localNRowIs){
+        targetRowIndices[tid] = int(indices[startSourceRowI + tid]);
+        if (targetRowIndices[tid]<0)
+            targetRowIndices[tid] += nTargetRows;
+        if (targetRowIndices[tid]<0 || targetRowIndices[tid]>=nTargetRows)
+            targetRowIndices[tid] = -1;
+    }
+    __syncthreads();
+
+    // copy 32 rows
+    for (int i=0; i<localNRowIs; i++){
+        const int sourceRowI = startSourceRowI + i, targetRowI = targetRowIndices[i];
+        for (int colI=tid; colI<nCols; colI+=32)
+            target[targetRowI * nCols + colI] = targetRowI==-1 ? (1.0/0.0 -1.0/0.0) : source[sourceRowI * nCols + colI];
+    }
+}
+
+
+__global__ void kWhere(double* condition_mat, double* if_mat, double* else_mat, double* target, unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+    
+    for (unsigned int i = idx; i < len; i += numThreads) {
+        target[i] = condition_mat[i] ? if_mat[i] : else_mat[i];
+    }
+}
+
+
+__global__ void kCorrelate(double* source, double* kernel, double* dest, int width, int height, int kwidth, int kheight) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for (unsigned int i = idx; i < width * height; i += numThreads) {
+        double sum = 0;
+        for (int w = -kwidth/2; w <= kwidth/2; w++) {
+            for (int h = -kheight/2; h <= (kheight)/2; h++) {
+                const int x = (i / height) + w;
+                const int y = (i % height) + h;
+                const int j = i + (w * height) + h;
+
+                if (x >= 0 && x < width && y >= 0 && y < height)
+                    sum += source[j] * kernel[(kwidth * kheight / 2) + w * kheight + h];
+            }
+        }
+        dest[i] = sum;
+    }
+}
diff --git a/ot/gpu/cudamat/cudamat/cudamat_kernels.cuh b/ot/gpu/cudamat/cudamat/cudamat_kernels.cuh
new file mode 100644
index 0000000..25e2858
--- /dev/null
+++ b/ot/gpu/cudamat/cudamat/cudamat_kernels.cuh
@@ -0,0 +1,92 @@
+#ifndef NVMATRIX_KERNEL_H_
+#define NVMATRIX_KERNEL_H_
+
+#define NUM_RND_BLOCKS                      96
+#define NUM_RND_THREADS_PER_BLOCK           128
+#define NUM_RND_STREAMS                     (NUM_RND_BLOCKS * NUM_RND_THREADS_PER_BLOCK)
+
+/*
+ * Defines for getting the values at the lower and upper 32 bits
+ * of a 64-bit number.
+ */
+#define LOW_BITS(x)                         ((x) & 0xffffffff)
+#define HIGH_BITS(x)                        ((x) >> 32)
+
+/*
+ * Number of iterations to run random number generator upon initialization.
+ */
+#define NUM_RND_BURNIN                      100
+
+/*
+ * CUDA grid dimensions for different types of kernels
+ */
+#define COPY_BLOCK_SIZE                     16
+#
+// element-wise kernels use min(ceil(N / 512), 4096) blocks of 512 threads
+#define MAX_VECTOR_OP_BLOCKS                4096
+#define MAX_VECTOR_OP_THREADS_PER_BLOCK     512
+#define NUM_VECTOR_OP_BLOCKS(N)             (min(((N) + MAX_VECTOR_OP_THREADS_PER_BLOCK - 1)/MAX_VECTOR_OP_THREADS_PER_BLOCK, MAX_VECTOR_OP_BLOCKS))
+#define NUM_VECTOR_OP_THREADS_PER_BLOCK(N)  (min((N), MAX_VECTOR_OP_THREADS_PER_BLOCK))
+
+#define PI 3.1415926535897932f
+
+__global__ void kSeedRandom(unsigned int* randMults, unsigned long long* randWords, unsigned int seed);
+__global__ void kRandomUniform(unsigned int* randMults, unsigned long long* randWords, double* gData, unsigned int numElements);
+__global__ void kRandomGaussian(unsigned int* rndMults, unsigned long long* rndWords, double* gData, unsigned int numElements);
+
+__global__ void kGetRowSlice(double* source, double* target, int start, int end, int width, int height);
+__global__ void kTranspose(double *odata, double *idata, int width, int height);
+__global__ void kSetRowSlice(double* source, double* target, int start, int end, int width, int height);
+
+__global__ void kLessThan(double* mat1, double* mat2, double* target, unsigned int len);
+__global__ void kLessThanScalar(double* mat, double val, double* target, unsigned int len);
+__global__ void kGreaterThan(double* mat1, double* mat2, double* target, unsigned int len);
+__global__ void kGreaterThanScalar(double* mat, double val, double* target, unsigned int len);
+__global__ void kEquals(double* mat1, double* mat2, double* target, unsigned int len);
+__global__ void kEqualsScalar(double* mat, double val, double* target, unsigned int len);
+__global__ void kMinimum(double* mat1, double* mat2, double* target, unsigned int len);
+__global__ void kMinimumScalar(double* mat, double val, double* target, unsigned int len);
+__global__ void kMaximum(double* mat1, double* mat2, double* target, unsigned int len);
+__global__ void kMaximumScalar(double* mat, double val, double* target, unsigned int len);
+__global__ void kMinColumnwise(double* mat, double* target, unsigned int width, unsigned int height);
+__global__ void kMinRowwise(double* mat, double* target, unsigned int width, unsigned int height);
+__global__ void kMaxColumnwise(double* mat, double* target, unsigned int width, unsigned int height);
+__global__ void kMaxRowwise(double* mat, double* target, unsigned int width, unsigned int height);
+__global__ void kArgMinColumnwise(double* mat, double* target, unsigned int width, unsigned int height);
+__global__ void kArgMinRowwise(double* mat, double* target, unsigned int width, unsigned int height);
+__global__ void kArgMaxColumnwise(double* mat, double* target, unsigned int width, unsigned int height);
+__global__ void kArgMaxRowwise(double* mat, double* target, unsigned int width, unsigned int height);
+__global__ void kSign(double* mat, double* target, unsigned int len);
+__global__ void kApplySigmoid(double* mat, double* target, unsigned int len);
+__global__ void kApplyTanh(double* mat, double* target, unsigned int len);
+__global__ void kApplySoftThreshold(double* mat, double alpha, double* target, unsigned int len);
+__global__ void kApplyAbs(double* mat, double* target, unsigned int len);
+__global__ void kApplyLog1PlusExp(double* mat, double* target, unsigned int len);
+__global__ void kLog(double* mat, double* target, unsigned int len);
+__global__ void kExp(double* mat, double* target, unsigned int len);
+__global__ void kGamma(double* mat, double* target, unsigned int len);
+__global__ void kLogGamma(double* mat, double* target, unsigned int len);
+__global__ void kSqrt(double* mat, double* target, unsigned int len);
+__global__ void kPow(double* mat, double pow, double* target, unsigned int len);
+__global__ void kPowMatrix(double* mat, double* pow, double* target, unsigned int len);
+__global__ void kReciprocal(double* mat, double* target, unsigned int len);
+__global__ void kAddColVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height);
+__global__ void kAddRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height);
+__global__ void kAddColMult(double* mat, double* vec, double* tgtMat, double mult, unsigned int width, unsigned int height);
+__global__ void kMultByColVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height);
+__global__ void kMultByRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height);
+__global__ void kDivByColVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height);
+__global__ void kDivByRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height);
+__global__ void kAdd(double* a, double* b, double* dest, unsigned int numEls);
+__global__ void kSubtract(double* a, double* b, double* dest, unsigned int numEls);
+__global__ void kMult(double* a, double* b, double* dest, unsigned int numEls);
+__global__ void kDivide(double* a, double* b, double* dest, unsigned int numEls);
+__global__ void kMultScalar(double* mat, double alpha, double* dest, unsigned int len);
+__global__ void kAssignScalar(double* dest, double alpha, unsigned int len);
+__global__ void kDivideScalar(double* mat, double alpha, double* dest, unsigned int len);
+__global__ void kAddScalar(double* a, double alpha, double* dest, unsigned int numEls);
+__global__ void kSelectRows(double* source, double* target, double* indices, int nRowIs, int nCols, int nSourceRows);
+__global__ void kSetSelectedRows(double* target, double* source, double* indices, int nRowIs, int nCols, int nTargetRows);
+__global__ void kWhere(double* condition_mat, double* if_mat, double* else_mat, double* target, unsigned int len);
+__global__ void kCorrelate(double* source, double* kernel, double* dest, int width, int height, int fwidth, int fheight);
+#endif
diff --git a/ot/gpu/cudamat/cudamat/learn.cu b/ot/gpu/cudamat/cudamat/learn.cu
new file mode 100644
index 0000000..3d9260c
--- /dev/null
+++ b/ot/gpu/cudamat/cudamat/learn.cu
@@ -0,0 +1,34 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <cublas.h>
+#include "learn_kernels.cuh"
+#include "cudamat.cuh"
+
+extern "C" {
+
+inline bool checkCUDAError() {
+    cudaError_t err = cudaGetLastError();
+
+    if (cudaSuccess != err)
+        printf("%s\n", cudaGetErrorString( err));
+    return cudaSuccess != err;
+}
+
+EXPORT int mult_by_sigmoid_deriv(cudamat* target, cudamat* acts) {
+    int len = acts->size[0]*acts->size[1];
+
+    if (acts->is_trans != target->is_trans)
+        return ERROR_TRANSPOSED;
+
+    if (acts->size[0] != target->size[0] || acts->size[1] != target->size[1])
+        return ERROR_INCOMPATIBLE_DIMENSIONS;
+
+    kMultiplyBySigmoidGrad<<<NUM_VECTOR_OP_BLOCKS,NUM_VECTOR_OP_THREADS_PER_BLOCK>>>(acts->data_device, target->data_device, len);
+
+    if (checkCUDAError())
+        return CUDA_ERROR;
+
+    return 0;
+}
+
+}
diff --git a/ot/gpu/cudamat/cudamat/learn.py b/ot/gpu/cudamat/cudamat/learn.py
new file mode 100644
index 0000000..741ca13
--- /dev/null
+++ b/ot/gpu/cudamat/cudamat/learn.py
@@ -0,0 +1,21 @@
+import os
+
+import ctypes as ct
+import numpy as np
+
+from cudamat import load_library, generate_exception
+
+_cudalearn = load_library('libcudalearn')
+
+_cudalearn.mult_by_sigmoid_deriv.restype = ct.c_int
+
+def mult_by_sigmoid_deriv(target, acts):
+    """
+    target = target * acts * (1 - acts)
+
+    Useful for doing backprop in neural networks with logistic units.
+    """
+
+    err_code = _cudalearn.mult_by_sigmoid_deriv(target.p_mat, acts.p_mat)
+    if err_code:
+        raise generate_exception(err_code)
diff --git a/ot/gpu/cudamat/cudamat/learn_kernels.cu b/ot/gpu/cudamat/cudamat/learn_kernels.cu
new file mode 100644
index 0000000..8e897ba
--- /dev/null
+++ b/ot/gpu/cudamat/cudamat/learn_kernels.cu
@@ -0,0 +1,10 @@
+#include "learn_kernels.cuh"
+
+__global__ void kMultiplyBySigmoidGrad(double* act, double* target, const unsigned int len) {
+    const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const unsigned int numThreads = blockDim.x * gridDim.x;
+
+    for(unsigned int i = idx; i < len; i+= numThreads) {
+        target[i] = target[i] * act[i] * (1.0f - act[i]);
+    }
+}
diff --git a/ot/gpu/cudamat/cudamat/learn_kernels.cuh b/ot/gpu/cudamat/cudamat/learn_kernels.cuh
new file mode 100644
index 0000000..26c8abd
--- /dev/null
+++ b/ot/gpu/cudamat/cudamat/learn_kernels.cuh
@@ -0,0 +1,12 @@
+#ifndef EBM_KERNELS_H_
+#define EBM_KERNELS_H_
+
+#define NUM_VECTOR_OP_BLOCKS                4096
+#define NUM_VECTOR_OP_THREADS_PER_BLOCK     512
+
+#define NUM_SPARSE_GRAD_BLOCKS               4096
+#define NUM_SPARSE_GRAD_THREADS_PER_BLOCK    512
+
+__global__ void kMultiplyBySigmoidGrad(double* act, double* target, const unsigned int len);
+
+#endif
diff --git a/ot/gpu/cudamat/cudamat/rnd_multipliers_32bit.txt b/ot/gpu/cudamat/cudamat/rnd_multipliers_32bit.txt
new file mode 100644
index 0000000..098d31d
--- /dev/null
+++ b/ot/gpu/cudamat/cudamat/rnd_multipliers_32bit.txt
@@ -0,0 +1,16028 @@
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diff --git a/ot/gpu/cudamat/examples/bench_cudamat.py b/ot/gpu/cudamat/examples/bench_cudamat.py
new file mode 100644
index 0000000..b3a5c19
--- /dev/null
+++ b/ot/gpu/cudamat/examples/bench_cudamat.py
@@ -0,0 +1,97 @@
+from __future__ import print_function, division
+import sys
+import numpy as np
+import cudamat as cmt
+import time
+import timeit
+from inspect import getmodule, getmembers, isfunction
+try: from itertools import ifilter as filter
+except: pass
+
+# heat-up time in seconds before starting the benchmark
+HEATUP = 2
+
+# shapes used for the small and large test matrix
+XS_SHAPE = (400, 256)
+XL_SHAPE = (4096, 4096)
+
+# timeit number and repeat parameter
+NUM_ITER = 100
+NUM_REPEATS = 5
+
+def setup(shape):
+    """Creates two matrices and corresponding row/column vectors"""
+    mat = cmt.empty(shape).fill_with_randn()
+    mat2 = cmt.empty(shape).fill_with_randn()
+    col = cmt.empty((shape[0], 1)).assign(0)
+    row = cmt.empty((1, shape[1])).assign(0)
+    return mat, mat2, col, row
+
+def bench_dot(X, Y, col, row):
+    cmt.dot(X.T, Y)
+
+def bench_add(X, Y, col, row):
+    X.add(Y)
+bench_add.repeats = 5  # 5 times more repetitions than usual
+
+def bench_mult(X, Y, col, row):
+    X.mult(Y)
+
+def bench_sigm(X, Y, col, row):
+    X.apply_sigmoid()
+
+def bench_colsum(X, Y, col, row):
+    X.sum(axis=0, target=row)
+
+def bench_rowsum(X, Y, col, row):
+    X.sum(axis=1, target=col)
+
+def bench_addcolsum(X, Y, col, row):
+    row.add_sums(X, axis=0, mult=3.2, beta=0.2)
+
+def bench_addrowsum(X, Y, col, row):
+    col.add_sums(X, axis=1, mult=3.2, beta=0.2)
+
+def bench_colmax(X, Y, col, row):
+    X.max(axis=0, target=row)
+
+def bench_rowmax(X, Y, col, row):
+    X.max(axis=1, target=col)
+
+def bench_addcolmult(X, Y, col, row):
+    X.add_col_mult(col, mult=3.2)
+
+def heatup(duration):
+    """Heat-up the GPU for a while so it enters full-performance mode"""
+    t1 = time.time()
+    while time.time() - t1 < duration:
+        cmt.dot(cmt.empty((200, 200)), cmt.empty((200, 200)))
+
+def main():
+    cmt.init()
+    cmt.CUDAMatrix.init_random()
+    if HEATUP:
+        print("heating up for %g seconds..." % HEATUP, end=' ')
+        sys.stdout.flush()
+        heatup(HEATUP)
+        print("done.")
+    print("small matrix shape:", XS_SHAPE)
+    print("large matrix shape:", XL_SHAPE)
+    for funcname, func in filter(lambda f: f[0].startswith('bench_'),
+            getmembers(getmodule(main), isfunction)):
+        print("%-15s" % funcname[len('bench_'):], end=' ')
+        sys.stdout.flush()
+        for size, shape, factor in ('small', XS_SHAPE, 10), ('large', XL_SHAPE, 1):
+            repeat = NUM_REPEATS * getattr(func, 'repeats', 1)
+            time = min(timeit.repeat(\
+                    setup="from __main__ import setup, %s\nmats = setup(%s)" % (funcname, shape),
+                    stmt="%s(*mats)" % funcname, repeat=repeat,
+                    number=NUM_ITER * factor)) / (NUM_ITER * factor)
+            print("%.3es (%s) " % (time, size), end=' ')
+            sys.stdout.flush()
+        print()
+    cmt.shutdown()
+
+if __name__=="__main__":
+    main()
+
diff --git a/ot/gpu/cudamat/examples/nn_cudamat.py b/ot/gpu/cudamat/examples/nn_cudamat.py
new file mode 100644
index 0000000..7c56c7d
--- /dev/null
+++ b/ot/gpu/cudamat/examples/nn_cudamat.py
@@ -0,0 +1,133 @@
+# This file shows how to implement a single hidden layer neural network for
+# performing binary classification on the GPU using cudamat.
+
+from __future__ import division
+import pdb
+import time
+import numpy as np
+import cudamat as cm
+from cudamat import learn as cl
+import util
+
+# initialize CUDA
+cm.cublas_init()
+
+# load data
+util.load('mnist49.dat', globals())
+
+# Put training data onto the GPU.
+dat_train = dat_train/255.
+dat_train = dat_train - (np.mean(dat_train, 1)+10**-8)[:, np.newaxis]
+dev_train = cm.CUDAMatrix(dat_train)
+dev_lbl = cm.CUDAMatrix(lbl_train)
+
+# training parameters
+epsilon = 0.01
+momentum = 0.9
+
+num_epochs = 30
+batch_size = 128
+num_batches = dat_train.shape[1]//batch_size
+
+# model parameters
+dim_in = dat_train.shape[0]
+dim_out = 1
+num_hid = 1024
+
+# initialize weights
+w_w1 = cm.CUDAMatrix(dim_in ** -0.5 * np.random.randn(dim_in, num_hid))
+w_b1 = cm.CUDAMatrix(np.zeros((num_hid, 1)))
+w_w2 = cm.CUDAMatrix(num_hid ** -0.5 * np.random.randn(num_hid, dim_out))
+w_b2 = cm.CUDAMatrix(np.zeros((dim_out, 1)))
+
+# initialize weight update matrices
+wu_w1 = cm.empty(w_w1.shape).assign(0)
+wu_b1 = cm.empty(w_b1.shape).assign(0)
+wu_w2 = cm.empty(w_w2.shape).assign(0)
+wu_b2 = cm.empty(w_b2.shape).assign(0)
+
+# initialize temporary storage
+h = cm.empty((num_hid, batch_size))
+out = cm.empty((dim_out, batch_size))
+delta = cm.empty((num_hid, batch_size))
+
+# Train neural network.
+start_time = time.time()
+for epoch in range(num_epochs):
+    print("Epoch %i" % (epoch + 1))
+    err = []
+
+    for batch in range(num_batches):
+        # get current minibatch
+        inp = dev_train.slice(batch*batch_size,(batch + 1)*batch_size)
+        target = dev_lbl.slice(batch*batch_size,(batch + 1)*batch_size)
+
+        # forward pass
+        cm.dot(w_w1.T, inp, target = h)
+
+        h.add_col_vec(w_b1)
+        h.apply_sigmoid()
+
+        cm.dot(w_w2.T, h, target = out)
+
+        out.add_col_vec(w_b2)
+        out.apply_sigmoid()
+
+        # back prop errors
+        out.subtract(target) # compute error
+
+        # gradients for w_w2 and w_b2
+        wu_w2.add_dot(h, out.T, beta = momentum)
+        wu_b2.add_sums(out, axis = 1, beta = momentum)
+
+        # compute delta
+        cm.dot(w_w2, out, target = delta)
+
+        # delta = delta * h * (1 - h)
+        cl.mult_by_sigmoid_deriv(delta, h)
+
+        # gradients for w_w1 and w_b1
+        wu_w1.add_dot(inp, delta.T, beta = momentum)
+        wu_b1.add_sums(delta, axis = 1, beta = momentum)
+
+        # update weights
+        w_w1.subtract_mult(wu_w1, epsilon/batch_size)
+        w_b1.subtract_mult(wu_b1, epsilon/batch_size)
+        w_w2.subtract_mult(wu_w2, epsilon/batch_size)
+        w_b2.subtract_mult(wu_b2, epsilon/batch_size)
+
+        # calculate error on current minibatch 
+        err.append(np.abs(out.asarray())>0.5)
+
+    print("Training misclassification rate: %f" % np.mean(err))
+    print("Time: %f" % (time.time() - start_time))
+
+# Evaluate neural network on test data.
+
+# Load test data onto the GPU.
+dat_test = dat_test/255.
+dat_test = dat_test - np.mean(dat_test, 1)[:, np.newaxis]
+dev_test = cm.CUDAMatrix(dat_test)
+dev_lbl = cm.CUDAMatrix(lbl_test)
+
+# Initalize temporary storage.
+h = cm.empty((num_hid, dat_test.shape[1]))
+out = cm.empty((dim_out, dat_test.shape[1]))
+
+# forward pass
+cm.dot(w_w1.T, dev_test, target = h)
+
+h.add_col_vec(w_b1)
+h.apply_sigmoid()
+
+cm.dot(w_w2.T, h, target = out)
+
+out.add_col_vec(w_b2)
+out.apply_sigmoid()
+
+# compute error
+out.subtract(dev_lbl)
+
+print("Testing misclassification rate: %f" % np.mean(np.abs(out.asarray())>0.5))
+
+cm.cublas_shutdown()
diff --git a/ot/gpu/cudamat/examples/rbm_cudamat.py b/ot/gpu/cudamat/examples/rbm_cudamat.py
new file mode 100644
index 0000000..3f6a900
--- /dev/null
+++ b/ot/gpu/cudamat/examples/rbm_cudamat.py
@@ -0,0 +1,98 @@
+from __future__ import division
+import time
+import numpy as np
+import cudamat as cm
+import util
+
+# initialize CUDA
+cm.cublas_init()
+cm.CUDAMatrix.init_random(1)
+
+# load data
+util.load('mnist.dat', globals())
+dev_dat = cm.CUDAMatrix(cm.reformat(dat/255.))
+
+# training parameters
+epsilon = 0.1
+momentum = 0.9
+
+num_epochs = 30
+batch_size = 128
+num_batches = dat.shape[1]//batch_size
+
+# model parameters
+num_vis = dat.shape[0]
+num_hid = 4096
+
+# initialize weights
+w_vh = cm.CUDAMatrix(0.1 * np.random.randn(num_vis, num_hid))
+w_v = cm.CUDAMatrix(np.zeros((num_vis, 1)))
+w_h = cm.CUDAMatrix(-4.*np.ones((num_hid, 1)))
+
+# initialize weight updates
+wu_vh = cm.CUDAMatrix(np.zeros((num_vis, num_hid)))
+wu_v = cm.CUDAMatrix(np.zeros((num_vis, 1)))
+wu_h = cm.CUDAMatrix(np.zeros((num_hid, 1)))
+
+# initialize temporary storage
+v = cm.empty((num_vis, batch_size))
+h = cm.empty((num_hid, batch_size))
+r = cm.empty((num_hid, batch_size))
+
+start_time = time.time()
+for epoch in range(num_epochs):
+    print("Epoch %i" % (epoch + 1))
+    err = []
+
+    for batch in range(num_batches):
+        # get current minibatch
+        v_true = dev_dat.slice(batch*batch_size,(batch + 1)*batch_size)
+        v.assign(v_true)
+
+        # apply momentum
+        wu_vh.mult(momentum)
+        wu_v.mult(momentum)
+        wu_h.mult(momentum)
+
+        # positive phase
+        cm.dot(w_vh.T, v, target = h)
+        h.add_col_vec(w_h)
+        h.apply_sigmoid()
+
+        wu_vh.add_dot(v, h.T)
+        wu_v.add_sums(v, axis = 1)
+        wu_h.add_sums(h, axis = 1)
+
+        # sample hiddens
+        r.fill_with_rand()
+        r.less_than(h, target = h)
+
+        # negative phase
+        cm.dot(w_vh, h, target = v)
+        v.add_col_vec(w_v)
+        v.apply_sigmoid()
+
+        cm.dot(w_vh.T, v, target = h)
+        h.add_col_vec(w_h)
+        h.apply_sigmoid()
+
+        wu_vh.subtract_dot(v, h.T)
+        wu_v.add_sums(v, axis = 1, mult = -1.)
+        wu_h.add_sums(h, axis = 1, mult = -1.)
+
+        # update weights
+        w_vh.add_mult(wu_vh, epsilon/batch_size)
+        w_v.add_mult(wu_v, epsilon/batch_size)
+        w_h.add_mult(wu_h, epsilon/batch_size)
+
+        # calculate reconstruction error
+        v.subtract(v_true)
+        err.append(v.euclid_norm()**2/(num_vis*batch_size))
+
+    print("Mean squared error: %f" % np.mean(err))
+    print("Time: %f" % (time.time() - start_time))
+
+w_vh.copy_to_host()
+util.save('weights.dat', 'w_vh', {'w_vh': w_vh.numpy_array})
+
+cm.cublas_shutdown()
diff --git a/ot/gpu/cudamat/examples/rbm_numpy.py b/ot/gpu/cudamat/examples/rbm_numpy.py
new file mode 100644
index 0000000..1331566
--- /dev/null
+++ b/ot/gpu/cudamat/examples/rbm_numpy.py
@@ -0,0 +1,72 @@
+from __future__ import division
+import time
+import numpy as np
+import util
+
+# load data
+util.load('mnist.dat', globals())
+dat = dat/255.
+
+# training parameters
+epsilon = 0.01
+momentum = 0.9
+
+num_epochs = 10
+batch_size = 64
+num_batches = dat.shape[1]//batch_size
+
+# model parameters
+num_vis = dat.shape[0]
+num_hid = 1024
+
+# initialize weights
+w_vh = 0.1 * np.random.randn(num_vis, num_hid)
+w_v = np.zeros((num_vis, 1))
+w_h = np.zeros((num_hid, 1))
+
+# initialize weight updates
+wu_vh = np.zeros((num_vis, num_hid))
+wu_v = np.zeros((num_vis, 1))
+wu_h = np.zeros((num_hid, 1))
+
+start_time = time.time()
+for epoch in range(num_epochs):
+    print("Epoch %i" % (epoch + 1))
+    err = []
+
+    for batch in range(num_batches):
+        v_true = dat[:, batch*batch_size:(batch + 1)*batch_size]
+        v = v_true
+
+        # apply momentum
+        wu_vh *= momentum
+        wu_v *= momentum
+        wu_h *= momentum
+
+        # positive phase
+        h = 1. / (1 + np.exp(-(np.dot(w_vh.T, v) + w_h)))
+
+        wu_vh += np.dot(v, h.T)
+        wu_v += v.sum(1)[:, np.newaxis]
+        wu_h += h.sum(1)[:, np.newaxis]
+
+        # sample hiddens
+        h = 1. * (h > np.random.rand(num_hid, batch_size))
+
+        # negative phase
+        v = 1. / (1 + np.exp(-(np.dot(w_vh, h) + w_v)))
+        h = 1. / (1 + np.exp(-(np.dot(w_vh.T, v) + w_h)))
+
+        wu_vh -= np.dot(v, h.T)
+        wu_v -= v.sum(1)[:, np.newaxis]
+        wu_h -= h.sum(1)[:, np.newaxis]
+
+        # update weights
+        w_vh += epsilon/batch_size * wu_vh
+        w_v += epsilon/batch_size * wu_v
+        w_h += epsilon/batch_size * wu_h
+
+        err.append(np.mean((v - v_true)**2))
+
+    print("Mean squared error: %f" % np.mean(err))
+    print("Time: %f" % (time.time() - start_time))
diff --git a/ot/gpu/cudamat/examples/util.py b/ot/gpu/cudamat/examples/util.py
new file mode 100644
index 0000000..79ceead
--- /dev/null
+++ b/ot/gpu/cudamat/examples/util.py
@@ -0,0 +1,22 @@
+from __future__ import division
+import gzip
+try: import cPickle as pickle
+except: import pickle
+
+def save(fname, var_list, source_dict):
+    var_list = [var.strip() for var in var_list.split() if len(var.strip())>0]
+    fo = gzip.GzipFile(fname, 'wb')
+    pickle.dump(var_list, fo)
+    for var in var_list:
+        pickle.dump(source_dict[var], fo, protocol=2)
+    fo.close()
+
+def load(fname, target_dict, verbose = True):
+    fo = gzip.GzipFile(fname, 'rb')
+    var_list = pickle.load(fo)
+    if verbose:
+        print(var_list)
+    for var in var_list:
+        target_dict[var] = pickle.load(fo)
+    fo.close()
+
diff --git a/ot/gpu/cudamat/setup.py b/ot/gpu/cudamat/setup.py
new file mode 100755
index 0000000..ad386d1
--- /dev/null
+++ b/ot/gpu/cudamat/setup.py
@@ -0,0 +1,121 @@
+#!/usr/bin/env python
+
+import os
+# on Windows, we need the original PATH without Anaconda's compiler in it:
+PATH = os.environ.get('PATH')
+from distutils.spawn import spawn, find_executable
+from setuptools import setup, find_packages, Extension
+from setuptools.command.build_ext import build_ext
+import sys
+
+# CUDA specific config
+# nvcc is assumed to be in user's PATH
+nvcc_compile_args = ['-O', '--ptxas-options=-v', '--compiler-options=-fPIC']
+nvcc_compile_args = os.environ.get('NVCCFLAGS', '').split() + nvcc_compile_args
+cuda_libs = ['cublas']
+
+cudamat_ext = Extension('cudamat.libcudamat',
+                        sources=['cudamat/cudamat.cu',
+                                 'cudamat/cudamat_kernels.cu'],
+                        libraries=cuda_libs,
+                        extra_compile_args=nvcc_compile_args)
+cudalearn_ext = Extension('cudamat.libcudalearn',
+                          sources=['cudamat/learn.cu',
+                                   'cudamat/learn_kernels.cu'],
+                          libraries=cuda_libs,
+                          extra_compile_args=nvcc_compile_args)
+
+
+class CUDA_build_ext(build_ext):
+    """
+    Custom build_ext command that compiles CUDA files.
+    Note that all extension source files will be processed with this compiler.
+    """
+    def build_extensions(self):
+        self.compiler.src_extensions.append('.cu')
+        self.compiler.set_executable('compiler_so', 'nvcc')
+        self.compiler.set_executable('linker_so', 'nvcc --shared')
+        if hasattr(self.compiler, '_c_extensions'):
+            self.compiler._c_extensions.append('.cu')  # needed for Windows
+        self.compiler.spawn = self.spawn
+        build_ext.build_extensions(self)
+
+    def spawn(self, cmd, search_path=1, verbose=0, dry_run=0):
+        """
+        Perform any CUDA specific customizations before actually launching
+        compile/link etc. commands.
+        """
+        if (sys.platform == 'darwin' and len(cmd) >= 2 and cmd[0] == 'nvcc' and
+                cmd[1] == '--shared' and cmd.count('-arch') > 0):
+            # Versions of distutils on OSX earlier than 2.7.9 inject
+            # '-arch x86_64' which we need to strip while using nvcc for
+            # linking
+            while True:
+                try:
+                    index = cmd.index('-arch')
+                    del cmd[index:index+2]
+                except ValueError:
+                    break
+        elif self.compiler.compiler_type == 'msvc':
+            # There are several things we need to do to change the commands
+            # issued by MSVCCompiler into one that works with nvcc. In the end,
+            # it might have been easier to write our own CCompiler class for
+            # nvcc, as we're only interested in creating a shared library to
+            # load with ctypes, not in creating an importable Python extension.
+            # - First, we replace the cl.exe or link.exe call with an nvcc
+            #   call. In case we're running Anaconda, we search cl.exe in the
+            #   original search path we captured further above -- Anaconda
+            #   inserts a MSVC version into PATH that is too old for nvcc.
+            cmd[:1] = ['nvcc', '--compiler-bindir',
+                       os.path.dirname(find_executable("cl.exe", PATH))
+                       or cmd[0]]
+            # - Secondly, we fix a bunch of command line arguments.
+            for idx, c in enumerate(cmd):
+                # create .dll instead of .pyd files
+                if '.pyd' in c: cmd[idx] = c = c.replace('.pyd', '.dll')
+                # replace /c by -c
+                if c == '/c': cmd[idx] = '-c'
+                # replace /DLL by --shared
+                elif c == '/DLL': cmd[idx] = '--shared'
+                # remove --compiler-options=-fPIC
+                elif '-fPIC' in c: del cmd[idx]
+                # replace /Tc... by ...
+                elif c.startswith('/Tc'): cmd[idx] = c[3:]
+                # replace /Fo... by -o ...
+                elif c.startswith('/Fo'): cmd[idx:idx+1] = ['-o', c[3:]]
+                # replace /LIBPATH:... by -L...
+                elif c.startswith('/LIBPATH:'): cmd[idx] = '-L' + c[9:]
+                # replace /OUT:... by -o ...
+                elif c.startswith('/OUT:'): cmd[idx:idx+1] = ['-o', c[5:]]
+                # remove /EXPORT:initlibcudamat or /EXPORT:initlibcudalearn
+                elif c.startswith('/EXPORT:'): del cmd[idx]
+                # replace cublas.lib by -lcublas
+                elif c == 'cublas.lib': cmd[idx] = '-lcublas'
+            # - Finally, we pass on all arguments starting with a '/' to the
+            #   compiler or linker, and have nvcc handle all other arguments
+            if '--shared' in cmd:
+                pass_on = '--linker-options='
+                # we only need MSVCRT for a .dll, remove CMT if it sneaks in:
+                cmd.append('/NODEFAULTLIB:libcmt.lib')
+            else:
+                pass_on = '--compiler-options='
+            cmd = ([c for c in cmd if c[0] != '/'] +
+                   [pass_on + ','.join(c for c in cmd if c[0] == '/')])
+            # For the future: Apart from the wrongly set PATH by Anaconda, it
+            # would suffice to run the following for compilation on Windows:
+            # nvcc -c -O -o <file>.obj <file>.cu
+            # And the following for linking:
+            # nvcc --shared -o <file>.dll <file1>.obj <file2>.obj -lcublas
+            # This could be done by a NVCCCompiler class for all platforms.
+        spawn(cmd, search_path, verbose, dry_run)
+
+setup(name="cudamat",
+      version="0.3",
+      description="Performs linear algebra computation on the GPU via CUDA",
+      ext_modules=[cudamat_ext, cudalearn_ext],
+      packages=find_packages(exclude=['examples', 'test']),
+      include_package_data=True,
+      package_data={'cudamat': ['rnd_multipliers_32bit.txt']},
+      author="Volodymyr Mnih",
+      url="https://github.com/cudamat/cudamat",
+      cmdclass={'build_ext': CUDA_build_ext})
diff --git a/ot/gpu/cudamat/test/test_cudamat.py b/ot/gpu/cudamat/test/test_cudamat.py
new file mode 100644
index 0000000..800243c
--- /dev/null
+++ b/ot/gpu/cudamat/test/test_cudamat.py
@@ -0,0 +1,1217 @@
+import numpy as np
+import nose
+import cudamat as cm
+
+def setup():
+    cm.cublas_init()
+
+def teardown():
+    cm.cublas_shutdown()
+
+def test_reshape():
+    m = 256
+    n = 1
+    cm1 = np.array(np.random.rand(n, m)*10, dtype=np.float64, order='F')
+    cm2 = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+
+    gm1 = cm.CUDAMatrix(cm1)
+    gm2 = cm.CUDAMatrix(cm2)
+
+    gm1.reshape((m, n))
+    gm2.assign(gm1)
+    gm1.reshape((n, m))
+
+    gm1.copy_to_host()
+    gm2.copy_to_host()
+
+    assert np.max(np.abs(gm1.numpy_array - gm2.numpy_array.T)) < 10**-2, "Error in CUDAMatrix.reshape exceeded threshold"
+
+def test_T_field():
+    m = 256
+    n = 128
+    cm1 = np.array(np.random.rand(n, m)*10, dtype=np.float64, order='F')
+    cm2 = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F')
+    gm1 = cm.CUDAMatrix(cm1)
+    gm2 = cm.CUDAMatrix(cm2)
+
+    # test dot
+    gm = cm.dot(gm2.T, gm1.T)
+    c = np.dot(cm2.T, cm1.T)
+    gm.copy_to_host()
+
+    assert np.max(np.abs(gm.numpy_array - c)) < 10**-2, "Error in CUDAMatrix.dot with TransposedCUDAMatrix exceeded threshold"
+
+    # test add_dot
+    cm3 = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F')
+    gm3 = cm.CUDAMatrix(cm3)
+    gm3.add_dot(gm2.T, gm1.T)
+    c = cm3 + np.dot(cm2.T, cm1.T)
+    gm3.copy_to_host()
+
+    assert np.max(np.abs(gm3.numpy_array - c)) < 10**-2, "Error in CUDAMatrix.add_dot TransposedCUDAMatrix exceeded threshold"
+
+    # test add_sums
+    gm2.add_sums(gm1.T, axis = 1)
+    c = cm2 + np.atleast_2d(cm1.sum(0)).T
+    gm2.copy_to_host()
+
+    assert np.max(np.abs(gm2.numpy_array - c)) < 10**-2, "Error in CUDAMatrix.add_sums TransposedCUDAMatrix exceeded threshold"
+
+def test_assign():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+
+    m1.assign(m2)
+    m1.copy_to_host()
+
+    assert np.max(np.abs(m1.numpy_array - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.assign exceeded threshold"
+
+def test_assign_scalar():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+
+    m1 = cm.CUDAMatrix(a)
+
+    m1.assign(np.pi)
+    m1.copy_to_host()
+
+    assert np.max(np.abs(m1.numpy_array - np.pi)) < 10**-4, "Error in CUDAMatrix.assign_scalar exceeded threshold"
+
+def test_get_row_slice():
+    m = 256
+    n = 128
+    start = 11
+    end = 54
+
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(end-start, n)*10, dtype=np.float64, order='F')
+
+    c = np.array(a[start:end,:], order='F')
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m1.get_row_slice(start, end, target = m2)
+    m3 = m1.get_row_slice(start, end)
+    m2.copy_to_host()
+    m3.copy_to_host()
+
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.get_row_slice exceeded threshold"
+    assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.get_row_slice exceeded threshold"
+
+def test_set_row_slice():
+    m = 256
+    n = 128
+    start = 11
+    end = 54
+
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(end-start, n)*10, dtype=np.float64, order='F')
+
+    c = a.copy()
+    c[start:end,:] = b
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m1.set_row_slice(start, end, m2)
+    m1.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.set_row_slice exceeded threshold"
+
+def test_transpose():
+    m = 6
+    n = 128
+
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(n, m), dtype=np.float64, order='F')
+
+    c = a.copy().T
+
+    m = cm.CUDAMatrix(a)
+    mt1 = cm.CUDAMatrix(b)
+    m.transpose(target = mt1)
+    mt2 = m.transpose()
+
+    mt1.copy_to_host()
+    mt2.copy_to_host()
+
+    assert np.max(np.abs(c - mt1.numpy_array)) < 10**-4, "Error in CUDAMatrix.transpose exceeded threshold"
+    assert np.max(np.abs(c - mt2.numpy_array)) < 10**-4, "Error in CUDAMatrix.transpose exceeded threshold"
+
+def test_slice():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+
+    c = np.array(a[:,32:64], order='F')
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = m1.slice(32, 64)
+    m2.copy_to_host()
+
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.slice exceeded threshold"
+
+
+def test_add_col_vec():
+    m = 250
+    n = 120
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F')
+    t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+
+    c = a + b
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m3 = cm.CUDAMatrix(t)
+
+    m1.add_col_vec(m2, target = m3)
+    m1.add_col_vec(m2)
+    m1.copy_to_host()
+    m3.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_col_vec exceeded threshold"
+    assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_col_vec exceeded threshold"
+
+def test_add_col_mult():
+    m = 256
+    n = 128
+    mult = np.pi
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F')
+    t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+
+    c = a + mult * b
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m3 = cm.CUDAMatrix(t)
+
+    m1.add_col_mult(m2, mult, target = m3)
+    m1.add_col_mult(m2, mult)
+    m1.copy_to_host()
+    m3.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_col_mult exceeded threshold"
+    assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_col_mult exceeded threshold"
+
+def test_add_row_vec():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F')
+    t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+
+    c = a + b
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m3 = cm.CUDAMatrix(t)
+
+    m1.add_row_vec(m2, target = m3)
+    m1.add_row_vec(m2)
+    m1.copy_to_host()
+    m3.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_row_vec exceeded threshold"
+    assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_row_vec exceeded threshold"
+
+def test_mult_by_col():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F')
+    t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+
+    c = a * b
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m3 = cm.CUDAMatrix(t)
+
+    m1.mult_by_col(m2, target = m3)
+    m1.mult_by_col(m2)
+    m1.copy_to_host()
+    m3.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult_by_col exceeded threshold"
+    assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult_by_col exceeded threshold"
+
+def test_mult_by_row():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F')
+    t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+
+    c = a * b
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m3 = cm.CUDAMatrix(t)
+
+    m1.mult_by_row(m2, target = m3)
+    m1.mult_by_row(m2)
+    m1.copy_to_host()
+    m3.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult_by_row exceeded threshold"
+    assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult_by_row exceeded threshold"
+
+def test_div_by_col():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') + 0.1
+    t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+
+    c = a / b
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m3 = cm.CUDAMatrix(t)
+
+    m1.div_by_col(m2, target = m3)
+    m1.div_by_col(m2)
+    m1.copy_to_host()
+    m3.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.div_by_col exceeded threshold"
+    assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.div_by_col exceeded threshold"
+
+def test_div_by_row():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') + 0.1
+    t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+
+    c = a / b
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m3 = cm.CUDAMatrix(t)
+
+    m1.div_by_row(m2, target = m3)
+    m1.div_by_row(m2)
+    m1.copy_to_host()
+    m3.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.div_by_row exceeded threshold"
+    assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.div_by_row exceeded threshold"
+
+def test_sum():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    t1 = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F')
+    t2 = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F')
+
+    mult = 0.8
+    c1 = np.atleast_2d(a.sum(0)) * mult
+    c2 = np.atleast_2d(a.sum(1)).T
+
+    m = cm.CUDAMatrix(a)
+    mt1 = cm.CUDAMatrix(t1)
+    mt2 = cm.CUDAMatrix(t2)
+
+    m.sum(axis = 0, target = mt1, mult = mult)
+    mt1r = m.sum(axis = 0, mult = mult)
+
+    m.sum(axis = 1, target = mt2)
+    mt2r = m.sum(axis = 1)
+
+    mt1.copy_to_host()
+    mt1r.copy_to_host()
+    mt2.copy_to_host()
+    mt2r.copy_to_host()
+
+    assert np.max(np.abs(c1 - mt1.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold"
+    assert np.max(np.abs(c1 - mt1r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold"
+    assert np.max(np.abs(c2 - mt2.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold"
+    assert np.max(np.abs(c2 - mt2r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold"
+
+def test_sum_trans():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    t1 = np.array(np.random.rand(1, m)*10, dtype=np.float64, order='F')
+    t2 = np.array(np.random.rand(n, 1)*10, dtype=np.float64, order='F')
+
+    c1 = np.atleast_2d(a.T.sum(0))
+    c2 = np.atleast_2d(a.T.sum(1)).T
+
+    m = cm.CUDAMatrix(a)
+    m.set_trans(True)
+    mt1 = cm.CUDAMatrix(t1)
+    mt2 = cm.CUDAMatrix(t2)
+
+    m.sum(axis = 0, target = mt1)
+    mt1r = m.sum(axis = 0)
+
+    m.sum(axis = 1, target = mt2)
+    mt2r = m.sum(axis = 1)
+
+    mt1.copy_to_host()
+    mt1r.copy_to_host()
+    mt2.copy_to_host()
+    mt2r.copy_to_host()
+
+    assert np.max(np.abs(c1 - mt1.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold"
+    assert np.max(np.abs(c1 - mt1r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold"
+    assert np.max(np.abs(c2 - mt2.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold"
+    assert np.max(np.abs(c2 - mt2r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold"
+
+def test_mean():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    t1 = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F')
+    t2 = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F')
+
+    c1 = np.atleast_2d(a.mean(0))
+    c2 = np.atleast_2d(a.mean(1)).T
+
+    m = cm.CUDAMatrix(a)
+    mt1 = cm.CUDAMatrix(t1)
+    mt2 = cm.CUDAMatrix(t2)
+
+    m.mean(axis = 0, target = mt1)
+    mt1r = m.mean(axis = 0)
+
+    m.mean(axis = 1, target = mt2)
+    mt2r = m.mean(axis = 1)
+
+    mt1.copy_to_host()
+    mt1r.copy_to_host()
+    mt2.copy_to_host()
+    mt2r.copy_to_host()
+
+    assert np.max(np.abs(c1 - mt1.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold"
+    assert np.max(np.abs(c1 - mt1r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold"
+    assert np.max(np.abs(c2 - mt2.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold"
+    assert np.max(np.abs(c2 - mt2r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold"
+
+def test_add_sums():
+    m = 256
+    n = 128
+
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    t1 = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F')
+    t2 = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F')
+
+    mult = np.pi
+    beta = 0.7
+
+    c1 = beta * t1 + mult * np.atleast_2d(a.sum(1)).T
+    c2 = t2 + np.atleast_2d(a.sum(0))
+
+    m = cm.CUDAMatrix(a)
+    mt1 = cm.CUDAMatrix(t1)
+    mt2 = cm.CUDAMatrix(t2)
+
+    mt1.add_sums(m, axis = 1, mult = np.pi, beta = beta)
+    mt2.add_sums(m, axis = 0)
+
+    mt1.copy_to_host()
+    mt2.copy_to_host()
+
+    assert np.max(np.abs(c1 - mt1.numpy_array)) < 10**-3, "Error in CUDAMatrix.add_sums exceeded threshold"
+    assert np.max(np.abs(c2 - mt2.numpy_array)) < 10**-3, "Error in CUDAMatrix.add_sums exceeded threshold"
+
+
+def test_less_than():
+    m = 256
+    n = 128
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    t1 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    t2 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    v = 0.1
+
+    r1 = 1 * (a < b)
+    r2 = 1 * (a < v)
+
+    da = cm.CUDAMatrix(a)
+    db = cm.CUDAMatrix(b)
+    dt1 = cm.CUDAMatrix(t1)
+    dt2 = cm.CUDAMatrix(t2)
+
+    da.less_than(db, target = dt1)
+    da.less_than(v, target = dt2)
+    da.less_than(db)
+
+    da.copy_to_host()
+    dt1.copy_to_host()
+    dt2.copy_to_host()
+
+    assert np.max(np.abs(r1 - da.numpy_array)) < 10**-4, "Error in CUDAMatrix.less_than exceeded threshold"
+    assert np.max(np.abs(r1 - dt1.numpy_array)) < 10**-4, "Error in CUDAMatrix.less_than exceeded threshold"
+    assert np.max(np.abs(r2 - dt2.numpy_array)) < 10**-4, "Error in CUDAMatrix.less_than exceeded threshold"
+
+def test_greater_than():
+    m = 256
+    n = 128
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    t1 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    t2 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    v = 0.1
+
+    r1 = 1 * (a > b)
+    r2 = 1 * (a > v)
+
+    da = cm.CUDAMatrix(a)
+    db = cm.CUDAMatrix(b)
+    dt1 = cm.CUDAMatrix(t1)
+    dt2 = cm.CUDAMatrix(t2)
+
+    da.greater_than(db, target = dt1)
+    da.greater_than(v, target = dt2)
+    da.greater_than(db)
+
+    da.copy_to_host()
+    dt1.copy_to_host()
+    dt2.copy_to_host()
+
+    assert np.max(np.abs(r1 - da.numpy_array)) < 10**-4, "Error in CUDAMatrix.greater_than exceeded threshold"
+    assert np.max(np.abs(r1 - dt1.numpy_array)) < 10**-4, "Error in CUDAMatrix.greater_than exceeded threshold"
+    assert np.max(np.abs(r2 - dt2.numpy_array)) < 10**-4, "Error in CUDAMatrix.greater_than exceeded threshold"
+
+def test_minimum():
+    m = 256
+    n = 128
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    t1 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    t2 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    v = 0.1
+
+    r1 = np.minimum(a, b)
+    r2 = np.minimum(a, v)
+
+    da = cm.CUDAMatrix(a)
+    db = cm.CUDAMatrix(b)
+    dt1 = cm.CUDAMatrix(t1)
+    dt2 = cm.CUDAMatrix(t2)
+
+    da.minimum(db, target = dt1)
+    da.minimum(v, target = dt2)
+    da.minimum(db)
+
+    da.copy_to_host()
+    dt1.copy_to_host()
+    dt2.copy_to_host()
+
+    assert np.max(np.abs(r1 - da.numpy_array)) < 10**-4, "Error in CUDAMatrix.minimum exceeded threshold"
+    assert np.max(np.abs(r1 - dt1.numpy_array)) < 10**-4, "Error in CUDAMatrix.minimum exceeded threshold"
+    assert np.max(np.abs(r2 - dt2.numpy_array)) < 10**-4, "Error in CUDAMatrix.minimum exceeded threshold"
+
+def test_maximum():
+    m = 256
+    n = 128
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    t1 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    t2 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    v = 0.1
+
+    r1 = np.maximum(a, b)
+    r2 = np.maximum(a, v)
+
+    da = cm.CUDAMatrix(a)
+    db = cm.CUDAMatrix(b)
+    dt1 = cm.CUDAMatrix(t1)
+    dt2 = cm.CUDAMatrix(t2)
+
+    da.maximum(db, target = dt1)
+    da.maximum(v, target = dt2)
+    da.maximum(db)
+
+    da.copy_to_host()
+    dt1.copy_to_host()
+    dt2.copy_to_host()
+
+    assert np.max(np.abs(r1 - da.numpy_array)) < 10**-4, "Error in CUDAMatrix.maximum exceeded threshold"
+    assert np.max(np.abs(r1 - dt1.numpy_array)) < 10**-4, "Error in CUDAMatrix.maximum exceeded threshold"
+    assert np.max(np.abs(r2 - dt2.numpy_array)) < 10**-4, "Error in CUDAMatrix.maximum exceeded threshold"
+
+def test_minmax():
+    m = 256
+    n = 128
+    for op in 'min', 'max', 'argmin', 'argmax':
+        for sign in (1, -1):
+            a = np.array(np.random.randn(m, n)*10*sign, dtype=np.float64, order='F')
+            t0 = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F')
+            t1 = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F')
+
+            r0 = np.atleast_2d(getattr(a, op)(0))
+            r1 = np.atleast_2d(getattr(a, op)(1))
+
+            da = cm.CUDAMatrix(a)
+            dr10 = cm.CUDAMatrix(t0)
+            dr11 = cm.CUDAMatrix(t1)
+
+            getattr(da, op)(axis = 0, target = dr10)
+            getattr(da, op)(axis = 1, target = dr11)
+            dr20 = getattr(da, op)(axis = 0)
+            dr21 = getattr(da, op)(axis = 1)
+
+            dr10.copy_to_host()
+            dr11.copy_to_host()
+            dr20.copy_to_host()
+            dr21.copy_to_host()
+
+            assert np.max(np.abs(r0 - dr10.numpy_array)) < 10**-4, "Error in CUDAMatrix.%s exceeded threshold" % op
+            assert np.max(np.abs(r1 - dr11.numpy_array.T)) < 10**-4, "Error in CUDAMatrix.%s exceeded threshold" % op
+            assert np.max(np.abs(r0 - dr20.numpy_array)) < 10**-4, "Error in CUDAMatrix.%s exceeded threshold" % op
+            assert np.max(np.abs(r1 - dr21.numpy_array.T)) < 10**-4, "Error in CUDAMatrix.%s exceeded threshold" % op
+
+def test_sign():
+    m = 256
+    n = 128
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    a[0,0] = 0.
+    a[0,1] = -0.
+    t = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+
+    c = np.sign(a)
+
+    m1 = cm.CUDAMatrix(a)
+    m3 = cm.CUDAMatrix(t)
+
+    m2 = m1.sign()
+    m1.sign(target = m3)
+
+    m2.copy_to_host()
+    m3.copy_to_host()
+
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.sign exceeded threshold"
+    assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.sign exceeded threshold"
+
+def test_sigmoid():
+    m = 256
+    n = 128
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+
+    c = 1. / (1. + np.exp(-a))
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m1.apply_sigmoid(target = m2)
+    m1.apply_sigmoid()
+
+    m1.copy_to_host()
+    m2.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_sigmoid exceeded threshold"
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_sigmoid exceeded threshold"
+
+def test_tanh():
+    m = 256
+    n = 128
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+
+    c = np.tanh(a)
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m1.apply_tanh(target = m2)
+    m1.apply_tanh()
+
+    m1.copy_to_host()
+    m2.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_tanh exceeded threshold"
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_tanh exceeded threshold"
+
+def test_soft_threshold():
+    m = 256
+    n = 128
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+
+    alpha = 0.5
+    c = np.sign(a) * np.maximum(0, np.abs(a) - alpha)
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m1.apply_soft_threshold(alpha, target = m2)
+    m1.apply_soft_threshold(alpha)
+
+    m1.copy_to_host()
+    m2.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_soft_threshold exceeded threshold"
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_soft_threshold exceeded threshold"
+
+def test_log():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10+0.1, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(m, n)*10+0.1, dtype=np.float64, order='F')
+
+    c = np.log(a)
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    cm.log(m1, target = m2)
+    cm.log(m1)
+
+    m1.copy_to_host()
+    m2.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in cudamat.log exceeded threshold"
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in cudamat.log exceeded threshold"
+
+def test_exp():
+    m = 256
+    n = 128
+    a = np.array(np.random.randn(m, n), dtype=np.float64, order='F')
+    b = np.array(np.random.randn(m, n), dtype=np.float64, order='F')
+
+    c = np.exp(a)
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    cm.exp(m1, target = m2)
+    cm.exp(m1)
+
+    m1.copy_to_host()
+    m2.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in cudamat.exp exceeded threshold"
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in cudamat.exp exceeded threshold"
+
+def test_gamma():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*5, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(m, n)*5, dtype=np.float64, order='F')
+
+    from scipy.special import gamma
+    c = gamma(a)
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    cm.gamma(m1, target = m2)
+    cm.gamma(m1)
+
+    m1.copy_to_host()
+    m2.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-2, "Error in cudamat.gamma exceeded threshold"
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-2, "Error in cudamat.gamma exceeded threshold"
+
+def test_lgamma():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+
+    from scipy.special import gammaln
+    c = gammaln(a)
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    cm.lgamma(m1, target = m2)
+    cm.lgamma(m1)
+
+    m1.copy_to_host()
+    m2.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-2, "Error in cudamat.lgamma exceeded threshold " + str(np.max(np.abs(c - m1.numpy_array)))
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-2, "Error in cudamat.lgamma exceeded threshold"
+
+def test_sqrt():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*20, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(m, n), dtype=np.float64, order='F')
+
+    c = np.sqrt(a)
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    cm.sqrt(m1, target = m2)
+    cm.sqrt(m1)
+
+    m1.copy_to_host()
+    m2.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in cudamat.sqrt exceeded threshold"
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in cudamat.sqrt exceeded threshold"
+
+def test_pow():
+    m = 256
+    n = 128
+    a = np.array(np.random.randn(m, n)*20, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(m, n), dtype=np.float64, order='F')
+    p = 2
+
+    c = a**p
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    cm.pow(m1, p, target = m2)
+    cm.pow(m1, p)
+
+    m1.copy_to_host()
+    m2.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-3, "Error in cudamat.pow exceeded threshold"
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-3, "Error in cudamat.pow exceeded threshold"
+
+def test_pow_matrix():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*20, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(m, n), dtype=np.float64, order='F')
+    p = np.array(np.random.randn(m, n), dtype=np.float64, order='F')
+
+
+    c = a**p
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    mp = cm.CUDAMatrix(p)
+    cm.pow(m1, mp, target = m2)
+    cm.pow(m1, mp)
+
+    m1.copy_to_host()
+    m2.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-2, "Error in cudamat.pow exceeded threshold"
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-2, "Error in cudamat.pow exceeded threshold"
+
+def test_reciprocal():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10+10**-3, dtype=np.float64, order='F')
+    b = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+
+    c = 1. / a
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m1.reciprocal(target = m2)
+    m1.reciprocal()
+
+    m1.copy_to_host()
+    m2.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.reciprocal exceeded threshold"
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.reciprocal exceeded threshold"
+
+def test_add_mult():
+    m = 256
+    n = 128
+    alpha = np.pi
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+
+    c = a + np.pi * b
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m1.add_mult(m2, np.pi)
+    m1.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_mult exceeded threshold"
+
+def test_subtract_mult():
+    m = 256
+    n = 128
+    alpha = np.pi
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+
+    c = a - np.pi * b
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m1.subtract_mult(m2, np.pi)
+    m1.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.subtract_mult exceeded threshold"
+
+def test_add():
+    m = 256
+    n = 128
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(1.+np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    t = np.array(np.empty((m, n)), dtype=np.float64, order='F')
+
+    c = a + b
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m3 = cm.CUDAMatrix(t)
+
+    m1.add(m2, target = m3)
+    m1.add(m2)
+
+    m3.copy_to_host()
+    m1.copy_to_host()
+
+    assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.add exceeded threshold"
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add exceeded threshold"
+
+def test_subtract():
+    m = 256
+    n = 128
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(1.+np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    t = np.array(np.empty((m, n)), dtype=np.float64, order='F')
+
+    c = a - b
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m3 = cm.CUDAMatrix(t)
+
+    m1.subtract(m2, target = m3)
+    m1.subtract(m2)
+
+    m3.copy_to_host()
+    m1.copy_to_host()
+
+    assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.subtract exceeded threshold"
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.subtract exceeded threshold"
+
+def test_divide():
+    m = 256
+    n = 128
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(1.+np.random.rand(m, n)*10, dtype=np.float64, order='F')
+    t = np.array(np.empty((m, n)), dtype=np.float64, order='F')
+
+    c = a / b
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m3 = cm.CUDAMatrix(t)
+
+    m1.divide(m2, target = m3)
+    m1.divide(m2)
+
+    m3.copy_to_host()
+    m1.copy_to_host()
+
+    assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.div exceeded threshold"
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.div exceeded threshold"
+
+def test_mult():
+    m = 256
+    n = 128
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    t = np.array(np.empty((m, n)), dtype=np.float64, order='F')
+
+    c = a * b
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m3 = cm.CUDAMatrix(t)
+
+    m1.mult(m2, target = m3)
+    m1.mult(m2)
+
+    m3.copy_to_host()
+    m1.copy_to_host()
+
+    assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.multiply exceeded threshold"
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.multiply exceeded threshold"
+
+def test_scalar_mult():
+    m = 256
+    n = 128
+    alpha = np.pi
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    t = np.array(np.empty((m, n)), dtype=np.float64, order='F')
+
+    c = a * alpha
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(t)
+
+    m1.mult(alpha, target = m2)
+    m1.mult(alpha)
+
+    m1.copy_to_host()
+    m2.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult exceeded threshold"
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult exceeded threshold"
+
+def test_scalar_div():
+    m = 256
+    n = 128
+    alpha = np.pi
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    t = np.array(np.empty((m, n)), dtype=np.float64, order='F')
+
+    c = a / alpha
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(t)
+
+    m1.divide(alpha, target = m2)
+    m1.divide(alpha)
+
+    m1.copy_to_host()
+    m2.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.divide exceeded threshold"
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.divide exceeded threshold"
+
+def test_add_scalar():
+    m = 256
+    n = 128
+    alpha = np.pi
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    t = np.array(np.empty((m, n)), dtype=np.float64, order='F')
+
+    c = a + alpha
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(t)
+
+    m1.add(alpha, target = m2)
+    m1.add(alpha)
+
+    m1.copy_to_host()
+    m2.copy_to_host()
+
+    assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_scalar exceeded threshold"
+    assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_scalar exceeded threshold"
+
+def test_dot():
+    m = 128
+    k = 256
+    n = 64
+    a = np.array(np.random.randn(m, k)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.randn(k, n)*10, dtype=np.float64, order='F')
+    c = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+
+    alpha = 2.
+    beta = 0.3
+    r = beta * c + alpha * np.dot(a, b)
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m3 = cm.CUDAMatrix(c)
+    m3 = cm.dot(m1, m2, target = m3, alpha = alpha, beta = beta)
+    m3.copy_to_host()
+
+    assert np.max(np.abs(r - m3.numpy_array)) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold"
+
+def test_dot_trans():
+    m = 128
+    k = 256
+    n = 64
+    a = np.array(np.random.randn(k, m)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.randn(k, n)*10, dtype=np.float64, order='F')
+
+    c = np.dot(a.T, b)
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m1.set_trans(True);
+    m3 = cm.dot(m1, m2)
+    m3.copy_to_host()
+
+    assert np.max(np.abs(c - m3.numpy_array)) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold"
+
+def test_dot_vect():
+    m = 128
+    k = 256
+    n = 1
+    a = np.array(np.random.randn(m, k)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.randn(k, n)*10, dtype=np.float64, order='F')
+    A = cm.CUDAMatrix(a)
+    B = cm.CUDAMatrix(b)
+
+    c = np.dot(a, b)
+    C = cm.dot(A, B)
+    assert np.max(np.abs(c - C.asarray())) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold"
+
+    c = np.dot(a.T, b[:m])
+    C = cm.dot(A.T, B.slice(0, m))
+    assert np.max(np.abs(c - C.asarray())) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold"
+
+    c = np.dot(b.T, a.T)
+    C = cm.dot(B.T, A.T)
+    assert np.max(np.abs(c - C.asarray())) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold"
+
+    c = np.dot(b[:m].T, a)
+    C = cm.dot(B.slice(0, m).reshape((1, m)), A)
+    assert np.max(np.abs(c - C.asarray())) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold"
+
+def test_add_dot():
+    m = 128
+    k = 256
+    n = 64
+    a = np.array(np.random.randn(m, k)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.randn(k, n)*10, dtype=np.float64, order='F')
+    c = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+
+    mult = 2.1
+    beta = 0.8
+    res = beta * c + mult * np.dot(a, b)
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m3 = cm.CUDAMatrix(c)
+    m3.add_dot(m1, m2, mult = mult, beta = beta)
+
+    m3.copy_to_host()
+
+    assert np.max(np.abs(res - m3.numpy_array)) < 10**-2, "Error in CUDAMatrix.add_dot exceeded threshold"
+
+def test_vdot():
+    m = 64
+    n = 64
+    a = np.array(np.random.randn(m, n), dtype=np.float64, order='F')
+    b = np.array(np.random.randn(m, n), dtype=np.float64, order='F')
+
+    true_res = np.vdot(a, b)
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+
+    res = cm.vdot(m1, m2)
+
+    assert np.abs(res - true_res) < 10**-2, "Error in CUDAMatrix.vdot exceeded threshold"
+
+def test_subtract_dot():
+    m = 128
+    k = 256
+    n = 64
+    a = np.array(np.random.randn(m, k)*10, dtype=np.float64, order='F')
+    b = np.array(np.random.randn(k, n)*10, dtype=np.float64, order='F')
+    c = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+
+    res = c - np.dot(a, b)
+
+    m1 = cm.CUDAMatrix(a)
+    m2 = cm.CUDAMatrix(b)
+    m3 = cm.CUDAMatrix(c)
+    m3.subtract_dot(m1, m2)
+
+    m3.copy_to_host()
+
+    assert np.max(np.abs(res - m3.numpy_array)) < 10**-2, "Error in CUDAMatrix.subtract_dot exceeded threshold"
+
+def test_random():
+    cm.CUDAMatrix.init_random(1)
+    m1 = cm.CUDAMatrix(np.array(np.empty((128,256)), dtype=np.float64, order='F'))
+    m2 = cm.CUDAMatrix(np.array(np.empty((128,256)), dtype=np.float64, order='F'))
+
+    m1.fill_with_rand()
+    m1.copy_to_host()
+    m2.fill_with_randn()
+    m2.copy_to_host()
+
+    assert np.abs(np.mean(m1.numpy_array) - 0.5) < 10**-2, "Error in CUDAMatrix.fill_with_rand threshold"
+    assert np.abs(np.mean(m2.numpy_array)) < 10**-2, "Error in CUDAMatrix.fill_with_randn threshold"
+
+def test_euclid_norm():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+
+    m = cm.CUDAMatrix(a)
+
+    n1 = np.sqrt(np.sum(a**2))
+    n2 = m.euclid_norm()
+
+    assert np.abs(n1-n2) < 10**-2, "Error in CUDAMatrix.euclid_norm exceeded threshold"
+
+def test_manhattan_norm():
+    m = 256
+    n = 128
+    a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F')
+
+    m = cm.CUDAMatrix(a)
+
+    n1 = np.sum(np.abs(a), dtype=np.double)
+    n2 = m.manhattan_norm()
+
+    assert np.abs(n1-n2) < 2e-2, "Error in CUDAMatrix.manhattan_norm exceeded threshold (%f != %f)" % (n1, n2)
+
+def test_allfinite():
+    a = cm.empty((10, 20)).assign(1).divide(0)  # NaN
+    b = cm.empty((10, 20)).assign(1e20).mult(1e20)  # Inf
+    c = cm.empty((10, 20)).assign(1)  # 1.0
+
+    assert (not a.allfinite()) and (not b.allfinite()) and c.allfinite(), "CUDAMatrix.allfinite does not work"
+
+def test_select_columns():
+    m = 256
+    n = 128
+    k = 8
+
+    s = np.array(np.random.randn(m, n), dtype=np.float64, order='F')
+    i_l = [0, 1, 2, 3, 5, 10, 12, 20]
+    i = np.array(i_l).T[np.newaxis, :]
+    t = np.empty((m, k))
+
+    s_d = cm.CUDAMatrix(s)
+    i_d = cm.CUDAMatrix(i)
+    t_d = cm.CUDAMatrix(t)
+
+    s_d.select_columns(i_d, t_d)
+    res = s[:,i_l]
+
+    assert np.max(np.abs(res - t_d.asarray())) < 10**-4, "Error in CUDAMatrix.select_columns exceeded threshold"
+
+
+def test_where():
+    m = 256
+    n = 128
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    z = np.zeros_like(a)
+    res = np.where(a > 0, a, z);
+
+    a_d = cm.CUDAMatrix(a)
+    z_d = cm.CUDAMatrix(z)
+    res_d = cm.empty(a_d.shape)
+    a_d.greater_than(0,  res_d)
+    cm.where(res_d, a_d, z_d)
+    assert np.abs(res-res_d.asarray()).max() < 1e-2, "Error in cudamat.where"
+
+
+def test_correlate():
+    m = 64
+    n = 32
+    km = 17
+    kn = 11
+
+    a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F')
+    k = np.array(np.random.randn(km, kn)*10, dtype=np.float64, order='F')
+
+    res = np.zeros_like(a)
+    for i in range(len(a)):
+        for j in range(len(a[0])):
+            for h in range(-(km/2), km/2 + 1):
+                for w in range(-(kn/2), kn/2 + 1):
+                    if i+h >= 0 and i+h < m and j+w >= 0 and j+w < n:
+                        res[i][j] += a[i + h][j + w] * k[km/2 + h][kn/2 + w]
+
+    a_d = cm.CUDAMatrix(a)
+    k_d = cm.CUDAMatrix(k)
+
+    res_d = cm.correlate(a_d, k_d)
+    assert np.abs(res-res_d.asarray()).max() < 1e-2, "Error in cudamat.correlate"
+
+
+if __name__ == '__main__':
+    nose.runmodule()
diff --git a/ot/gpu/cudamat/test/test_learn.py b/ot/gpu/cudamat/test/test_learn.py
new file mode 100644
index 0000000..954757a
--- /dev/null
+++ b/ot/gpu/cudamat/test/test_learn.py
@@ -0,0 +1,27 @@
+import pdb
+import numpy as np
+import nose
+import cudamat as cm
+
+def setup():
+    cm.cublas_init()
+
+def teardown():
+    cm.cublas_shutdown()
+
+def test_mult_by_sigmoid_deriv():
+    m = 256
+    n = 128
+    c_targets = np.array(np.random.randn(m, n)*10, dtype=np.float32, order='F')
+    c_acts = np.array(np.random.rand(m, n), dtype=np.float32, order='F')
+
+    g_targets = cm.CUDAMatrix(c_targets)
+    g_acts = cm.CUDAMatrix(c_acts)
+
+    c_targets = c_targets * c_acts * (1. - c_acts)
+    cm.learn.mult_by_sigmoid_deriv(g_targets, g_acts)
+
+    assert np.max(np.abs(c_acts - g_acts.asarray())) < 10**-2, "Error in cudamat.learn.mult_by_sigmoid_deriv exceeded threshold"
+
+if __name__ == '__main__':
+    nose.runmodule()
diff --git a/ot/gpu/da.py b/ot/gpu/da.py
new file mode 100644
index 0000000..7d04b5b
--- /dev/null
+++ b/ot/gpu/da.py
@@ -0,0 +1,81 @@
+# -*- coding: utf-8 -*-
+"""
+Domain adaptation with optimal transport and GPU
+"""
+
+import numpy as np
+from ..utils import unif
+from ..da import OTDA
+from .bregman import sinkhornGPU
+
+
+def pairwiseEuclideanGPU(a, b, returnAsGPU=False, squared=False, cudamat=None):
+    # a is shape (n, f) and b shape (m, f). Return matrix c of shape (n, m).
+    # First compute in c_GPU the squared euclidean distance. And return its
+    # square root. At each cell [i,j] of c, we want to have
+    # sum{k in range(f)} ( (a[i,k] - b[j,k])^2 ). We know that
+    # (a-b)^2 = a^2 -2ab +b^2. Thus we want to have in each cell of c:
+    # sum{k in range(f)} ( a[i,k]^2 -2a[i,k]b[j,k] +b[j,k]^2).
+
+    a_GPU = cudamat.CUDAMatrix(a)
+    b_GPU = cudamat.CUDAMatrix(b)
+
+    # Multiply a by b transpose to obtain in each cell [i,j] of c the
+    # value sum{k in range(f)} ( a[i,k]b[j,k] )
+    c_GPU = cudamat.dot(a_GPU, b_GPU.transpose())
+    # multiply by -2 to have sum{k in range(f)} ( -2a[i,k]b[j,k] )
+    c_GPU.mult(-2)
+
+    # Compute the vectors of the sum of squared elements.
+    a_GPU = cudamat.pow(a_GPU, 2).sum(axis=1)
+    b_GPU = cudamat.pow(b_GPU, 2).sum(axis=1)
+
+    # Add the vectors in each columns (respectivly rows) of c.
+    # sum{k in range(f)} ( a[i,k]^2 -2a[i,k]b[j,k] )
+    c_GPU.add_col_vec(a_GPU)
+    # sum{k in range(f)} ( a[i,k]^2 -2a[i,k]b[j,k] +b[j,k]^2)
+    c_GPU.add_row_vec(b_GPU.transpose())
+
+    if not squared:
+        c_GPU = cudamat.sqrt(c_GPU)
+
+    if returnAsGPU:
+        return c_GPU
+    else:
+        return c_GPU.asarray()
+
+
+class OTDA_sinkhorn_GPU(OTDA):
+    def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None):
+        import cudamat
+        cudamat.init()
+        xs = np.asarray(xs, dtype=np.float64)
+        xt = np.asarray(xt, dtype=np.float64)
+
+        self.xs = xs
+        self.xt = xt
+
+        if wt is None:
+            wt = unif(xt.shape[0])
+        if ws is None:
+            ws = unif(xs.shape[0])
+
+        self.ws = ws
+        self.wt = wt
+
+        self.M_GPU = pairwiseEuclideanGPU(xs, xt, returnAsGPU=True,
+                                          squared=True, cudamat=cudamat)
+
+        if norm == "median":
+            self.M_GPU.divide(float(np.median(self.M_GPU.asarray())))
+        elif norm == "max":
+            self.M_GPU.divide(float(np.max(self.M_GPU.asarray())))
+        elif norm == "log":
+            M = np.log(1 + self.M_GPU.asarray())
+            self.M_GPU = cudamat.CUDAMatrix(M)
+        elif norm == "loglog":
+            M = np.log(1 + np.log(1 + self.M_GPU.asarray()))
+            self.M_GPU = cudamat.CUDAMatrix(M)
+
+        self.G = sinkhornGPU(ws, wt, self.M_GPU, reg, cudamat=cudamat)
+        self.computed = True