sinkhorn GPU implementation

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+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
+```