Skip to content

Latest commit

 

History

History
112 lines (89 loc) · 5.94 KB

routines.md

File metadata and controls

112 lines (89 loc) · 5.94 KB

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
xCOL2IM
xCONVGEMM - -

Some less commonly used BLAS routines are not yet supported 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.