Shubham Sharma 16c56e0101 Added 24x8 triangular kernels for DGEMMT SUP
- In order to reuse 24x8 AVX512 DGEMM SUP kernels,
   24x8 triangular AVX512 DGEMMT SUP kernels are added.
 - Since the LCM of MR(24) and NR(8) is 24, therefore the diagonal
   pattern repeats every 24x24 block of C. To cover this 24x24 block,
   3 kernels are needed for one variant of DGEMMT. A total of 6
   kernels are needed to cover both upper and lower variants.
 - In order to maximize code reuse, the 24x8 kernels are broken
   into two parts, 8x8 diagonal GEMM and 16x8 full GEMM. The 8x8
   diagonal GEMM is computed by 8x8 diagonal kernel, and 16x8
   full GEMM part is computed by 24x8 DGEMM SUP kernel.
 - Changes are made in framework to enable the use of these kernels.

AMD-Internal: [CPUPL-5338]
Change-Id: I8e7007031e906f786b0c4fe12377ee439075207a
2024-07-22 12:02:30 -04:00
2019-05-23 12:51:17 -05:00
2021-10-05 14:24:17 -05:00
2019-10-02 10:16:22 +01:00
2021-10-06 10:22:34 -05:00
2021-03-22 17:42:33 -05:00
2024-07-08 06:09:11 -04:00
2018-08-07 14:21:07 -05:00
2023-11-23 08:54:31 -05:00
2024-06-25 05:48:46 -04:00
2023-05-25 14:46:33 +00:00

AOCL-BLAS library

AOCL-BLAS is AMD's optimized version of BLAS targeted for AMD EPYC and Ryzen CPUs. It is developed as a forked version of BLIS (https://github.com/flame/blis), which is developed by members of the Science of High-Performance Computing (SHPC) group in the Institute for Computational Engineering and Sciences at The University of Texas at Austin and other collaborators (including AMD). All known features and functionalities of BLIS are retained and supported in AOCL-BLAS library. AOCL-BLAS is regularly updated with the improvements from the upstream repository.

AOCL BLAS is optimized with SSE2, AVX2, AVX512 instruction sets which would be enabled based on the target Zen architecture using the dynamic dispatch feature. All prominent Level 3, Level 2 and Level 1 APIs are designed and optimized for specific paths targeting different size spectrums e.g., Small, Medium and Large sizes. These algorithms are designed and customized to exploit the architectural improvements of the target platform.

For detailed instructions on how to configure, build, install, and link against AOCL-BLAS on AMD CPUs, please refer to the AOCL User Guide located on AMD developer portal.

The upstream repository (https://github.com/flame/blis) contains further information on BLIS, including background information on BLIS design, usage examples, and a complete BLIS API reference.

AOCL-BLAS is developed and maintained by AMD. You can contact us on the email-id toolchainsupport@amd.com. You can also raise any issue/suggestion on the git-hub repository at https://github.com/amd/blis/issues.

Description
BLAS-like Library Instantiation Software Framework
Readme BSD-3-Clause 72 MiB
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C++ 9.5%
Fortran 1.9%
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