MAGMA: Enabling exascale performance with accelerated BLAS and LAPACK for diverse GPU architectures

Ahmad Abdelfattah, Natalie Beams, Robert Carson, Pieter Ghysels, Tzanio Kolev, Thomas Stitt, Arturo Vargas, Stanimire Tomov, Jack Dongarra

Research output: Contribution to journalArticlepeer-review

Abstract

MAGMA (Matrix Algebra for GPU and Multicore Architectures) is a pivotal open-source library in the landscape of GPU-enabled dense and sparse linear algebra computations. With a repertoire of approximately 750 numerical routines across four precisions, MAGMA is deeply ingrained in the DOE software stack, playing a crucial role in high-performance computing. Notable projects such as ExaConstit, HiOP, MARBL, and STRUMPACK, among others, directly harness the capabilities of MAGMA. In addition, the MAGMA development team has been acknowledged multiple times for contributing to the vendors’ numerical software stacks. Looking back over the time of the Exascale Computing Project (ECP), we highlight how MAGMA has adapted to recent changes in modern HPC systems, especially the growing gap between CPU and GPU compute capabilities, as well as the introduction of low precision arithmetic in modern GPUs. We also describe MAGMA’s direct impact on several ECP projects. Maintaining portable performance across NVIDIA and AMD GPUs, and with current efforts toward supporting Intel GPUs, MAGMA ensures its adaptability and relevance in the ever-evolving landscape of GPU architectures.

Original languageEnglish
JournalInternational Journal of High Performance Computing Applications
DOIs
StateAccepted/In press - 2024
Externally publishedYes

Keywords

  • GPU computing
  • The MAGMA library
  • numerical linear algebra
  • performance portability

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