Mangala V 0a4f9d5ac1 Removed -fno-tree-loop-vectorize from kernel flags
- This change in made in MAKE build system.
- Removed -fno-tree-loop-vectorize from global kernel flags,
  instead added it to lpgemm specific kernels only.
- If this flag is not used , then gcc tries to auto
  vectorize the code which results in usages of
  vector registers, if the auto vectorized function
  is using intrinsic then the total numbers of vector
  registers used by intrinsic and auto vectorized
  code becomes more than the registers
  available in machine which causes read and writes
  to stack, which is causing regression in lpgemm.
- If this flag is enabled globally, then the files which
  do not use any intrinsic code do not get auto
  vectorized.
- To get optimal performance for both blis and lpgemm,
  this flag is enabled for lpgemm kernels only.

Previous commit (75df1ef218) contains
similar changes on cmake build system

AMD-Internal: [CPUPL-5544]

Change-Id: I796e89f3fb2116d64c3a78af2069de20ce92d506
2024-08-02 09:40:06 -04:00
2024-08-02 09:31:38 -04:00
2019-05-23 12:51:17 -05:00
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2019-10-02 10:16:22 +01:00
2021-10-06 10:22:34 -05:00
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2018-08-07 14:21:07 -05:00
2023-11-23 08:54:31 -05:00
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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