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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.

Full API documentation is available in a separate API documentation file.

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
IxAMIN
IxMAX
IxMIN
xHAD
xOMATCOPY
xIM2COL

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 example shows how to use these convenience functions when calling the half-precision BLAS routine HAXPY.