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CLBlast: Supported routines overview
================
This document describes which routines are supported in CLBlast. For other information about CLBlast, see the [main README](../README.md).
Full API documentation is available in a separate [API documentation file](api.md).
Supported types
-------------
The different data-types supported by the library are:
* __S:__ Single-precision 32-bit floating-point (`float`).
* __D:__ Double-precision 64-bit floating-point (`double`).
* __C:__ Complex single-precision 2x32-bit floating-point (`std::complex<float>`).
* __Z:__ Complex double-precision 2x64-bit floating-point (`std::complex<double>`).
* __H:__ Half-precision 16-bit floating-point (`cl_half`). See section 'Half precision' below for more information.
Supported routines
-------------
CLBlast supports almost all the Netlib BLAS routines plus a couple of extra non-BLAS routines. The supported BLAS routines are marked with '✔' in the following tables. Routines marked with '-' do not exist: they are not part of BLAS at all.
| Level-1 | S | D | C | Z | H |
| ---------|---|---|---|---|---|
| xSWAP | ✔ | ✔ | ✔ | ✔ | ✔ |
| xSCAL | ✔ | ✔ | ✔ | ✔ | ✔ |
| xCOPY | ✔ | ✔ | ✔ | ✔ | ✔ |
| xAXPY | ✔ | ✔ | ✔ | ✔ | ✔ |
| xDOT | ✔ | ✔ | - | - | ✔ |
| xDOTU | - | - | ✔ | ✔ | - |
| xDOTC | - | - | ✔ | ✔ | - |
| xNRM2 | ✔ | ✔ | ✔ | ✔ | ✔ |
| xASUM | ✔ | ✔ | ✔ | ✔ | ✔ |
| IxAMAX | ✔ | ✔ | ✔ | ✔ | ✔ |
| Level-2 | S | D | C | Z | H |
| ---------|---|---|---|---|---|
| xGEMV | ✔ | ✔ | ✔ | ✔ | ✔ |
| xGBMV | ✔ | ✔ | ✔ | ✔ | ✔ |
| xHEMV | - | - | ✔ | ✔ | - |
| xHBMV | - | - | ✔ | ✔ | - |
| xHPMV | - | - | ✔ | ✔ | - |
| xSYMV | ✔ | ✔ | - | - | ✔ |
| xSBMV | ✔ | ✔ | - | - | ✔ |
| xSPMV | ✔ | ✔ | - | - | ✔ |
| xTRMV | ✔ | ✔ | ✔ | ✔ | ✔ |
| xTBMV | ✔ | ✔ | ✔ | ✔ | ✔ |
| xTPMV | ✔ | ✔ | ✔ | ✔ | ✔ |
| xGER | ✔ | ✔ | - | - | ✔ |
| xGERU | - | - | ✔ | ✔ | - |
| xGERC | - | - | ✔ | ✔ | - |
| xHER | - | - | ✔ | ✔ | - |
| xHPR | - | - | ✔ | ✔ | - |
| xHER2 | - | - | ✔ | ✔ | - |
| xHPR2 | - | - | ✔ | ✔ | - |
| xSYR | ✔ | ✔ | - | - | ✔ |
| xSPR | ✔ | ✔ | - | - | ✔ |
| xSYR2 | ✔ | ✔ | - | - | ✔ |
| xSPR2 | ✔ | ✔ | - | - | ✔ |
| xTRSV | ✔ | ✔ | ✔ | ✔ | |
| Level-3 | S | D | C | Z | H |
| ---------|---|---|---|---|---|
| xGEMM | ✔ | ✔ | ✔ | ✔ | ✔ |
| xSYMM | ✔ | ✔ | ✔ | ✔ | ✔ |
| xHEMM | - | - | ✔ | ✔ | - |
| xSYRK | ✔ | ✔ | ✔ | ✔ | ✔ |
| xHERK | - | - | ✔ | ✔ | - |
| xSYR2K | ✔ | ✔ | ✔ | ✔ | ✔ |
| xHER2K | - | - | ✔ | ✔ | - |
| xTRMM | ✔ | ✔ | ✔ | ✔ | ✔ |
| xTRSM | ✔ | ✔ | ✔ | ✔ | |
Furthermore, there are also batched versions of BLAS routines available, processing multiple smaller computations in one go for better performance:
| Batched | S | D | C | Z | H |
| --------------------|---|---|---|---|---|
| xAXPYBATCHED | ✔ | ✔ | ✔ | ✔ | ✔ |
| xGEMMBATCHED | ✔ | ✔ | ✔ | ✔ | ✔ |
| xGEMMSTRIDEDBATCHED | ✔ | ✔ | ✔ | ✔ | ✔ |
In addition, some extra non-BLAS routines are also supported by CLBlast, classified as level-X. They are experimental and should be used with care:
| Level-X | S | D | C | Z | H |
| -----------|---|---|---|---|---|
| xSUM | ✔ | ✔ | ✔ | ✔ | ✔ | (Similar to xASUM, but not absolute)
| IxAMIN | ✔ | ✔ | ✔ | ✔ | ✔ | (Similar to IxAMAX, but minimum instead of maximum)
| IxMAX | ✔ | ✔ | ✔ | ✔ | ✔ | (Similar to IxAMAX, but not absolute)
| IxMIN | ✔ | ✔ | ✔ | ✔ | ✔ | (Similar to IxAMAX, but not absolute and minimum instead of maximum)
| xHAD | ✔ | ✔ | ✔ | ✔ | ✔ | (Hadamard product)
| xOMATCOPY | ✔ | ✔ | ✔ | ✔ | ✔ | (Out-of-place copying/transposing/scaling of matrices)
| xIM2COL | ✔ | ✔ | ✔ | ✔ | ✔ | (Image to column transform as used to express convolution as GEMM)
Some less commonly used BLAS routines are not yet supported yet by CLBlast. They are xROTG, xROTMG, xROT, xROTM, xTBSV, and xTPSV.
Half precision (fp16)
-------------
The half-precision fp16 format is a 16-bits floating-point data-type. Some OpenCL devices support the `cl_khr_fp16` extension, reducing storage and bandwidth requirements by a factor 2 compared to single-precision floating-point. In case the hardware also accelerates arithmetic on half-precision data-types, this can also greatly improve compute performance of e.g. level-3 routines such as GEMM. Devices which can benefit from this are among others Intel GPUs, ARM Mali GPUs, and NVIDIA's latest Pascal GPUs. Half-precision is in particular interest for the deep-learning community, in which convolutional neural networks can be processed much faster at a minor accuracy loss.
Since there is no half-precision data-type in C or C++, OpenCL provides the `cl_half` type for the host device. Unfortunately, internally this translates to a 16-bits integer, so computations on the host using this data-type should be avoided. For convenience, CLBlast provides the `clblast_half.h` header (C99 and C++ compatible), defining the `half` type as a short-hand to `cl_half` and the following basic functions:
* `half FloatToHalf(const float value)`: Converts a 32-bits floating-point value to a 16-bits floating-point value.
* `float HalfToFloat(const half value)`: Converts a 16-bits floating-point value to a 32-bits floating-point value.
The [samples/haxpy.c](../samples/haxpy.c) example shows how to use these convenience functions when calling the half-precision BLAS routine HAXPY.
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