Skip to content

AOCL-BLAS 5.3.2

Latest

Choose a tag to compare

@rohrayan rohrayan released this 20 Aug 09:44
d8ad9c9

Performance Optimizations

  • cgemm AVX512 kernel: support for all conja/conjb combos
  • Added S/D/C/Z GEMV kernels + multithreading
  • SGEMM Zen5 RD kernel optimization
  • Optimize complex scalv kernels with inline asm + FMA
  • Converting mul+add to FMA for ddot, daxpy, daxpyf zen kernels
  • Optimize edge/non-unit stride paths with intrinsics in axpyv kernels
  • Pack B matrix for zgemm conjugate input in SUP path
  • Support conjugate transpose in AVX512 zgemm SUP kernel

New features

  • Multithreading added to GER API
  • Skip OpenMP parallel region for single-threaded L3 and scalv calls
  • Add optional header-only AOCL allocator backend
  • Contribute BLIS objects to unified AOCL Build-It-Yourself libaocl
  • Enable Exploit Mitigations for Windows (clang-cl) builds
  • configure: follow reproducible-builds spec for SOURCE_DATE_EPOCH

Bug fixes

  • Fix heap overflow when packing to a wider target datatype
  • BigFix: int type cast in AVX512 GEMV transpose
  • Fix heap corruption in mixed-domain/precision GEMM
  • BugFix: skip AOCL_DYNAMIC for conjugate ZGEMM
  • Fix: resolve label redefinition errors in zgemm SUP kernel
  • Fixing memory issue in cgemm pack kernels on zen4
  • Fixing memory issues in sgemm SUP kernels on AVX2/AVX512
  • BF16 AVX2 fallback GEMV m=1 path fix for reordered B inputs

Test infra (gtestsuite)

  • Bitexactness CRC verification + per-test JSON output
  • Refine GEMM tests, cleanup disabled tests, fix ZGEMM tiny tests on Zen3

Known Issues

This release contains a known performance regression that may impact specific workloads and configurations. Depending on the workload characteristics, users may experience lower performance relative to the previous release. We are actively working on a resolution and recommend using the prior stable release if this regression significantly affects your deployment.

Regression issue details:

  • ZGEMM conjugate inputs regression for small sizes
  • Performance regressions for various GEMV cases