Evolution of the SLATE linear algebra library

SLATE (Software for Linear Algebra Targeting Exascale) is a distributed, dense linear algebra library targeting both CPU-only and GPU-accelerated systems, developed over the course of the Exascale Computing Project (ECP). While it began with several documents setting out its initial design, significant design changes occurred throughout its development. In some cases, these were anticipated: an early version used a simple consistency flag that was later replaced with a full-featured consistency protocol. In other cases, performance limitations and software and hardware changes prompted a redesign. Sequential communication tasks were parallelized; host-to-host MPI calls were replaced with GPU device-to-device MPI calls; more advanced algorithms such as Communication Avoiding LU and the Random Butterfly Transform (RBT) were introduced. Early choices that turned out to be cumbersome, error prone, or inflexible have been replaced with simpler, more intuitive, or more flexible designs. Applications have been a driving force, prompting a lighter weight queue class, nonuniform tile sizes, and more flexible MPI process grids. Of paramount importance has been building a portable library that works across several different GPU architectures – AMD, Intel, and NVIDIA – while keeping a clean and maintainable codebase. Here we explore the evolving design choices and their effects, both in terms of performance and software sustainability.

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Publication Details

Journal
The International Journal of High Performance Computing Applications
Published
2024-09-27
DOI
https://doi.org/10.1177/10943420241286531
Citations
4
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
1.96

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article

Evolution of the SLATE linear algebra library

Rabab Alomairy, Neil Lindquist, Mohammed Al Farhan, Kadir Akbudak et al.
4 citations
The International Journal of High Performance Computing Applications
Parallel Computing and Optimization Techniques
1.96
article

Evolution of the SLATE linear algebra library

Rabab Alomairy, Neil Lindquist, Mohammed Al Farhan, Kadir Akbudak, Dalal Sukkari, Daniel Bielich, Ahmad Abdelfattah, Sébastien Cayrols, Asim YarKhan, Treece Burgess, Mark Gates
article en
4 citations

Abstract

SLATE (Software for Linear Algebra Targeting Exascale) is a distributed, dense linear algebra library targeting both CPU-only and GPU-accelerated systems, developed over the course of the Exascale Computing Project (ECP). While it began with several documents setting out its initial design, significant design changes occurred throughout its development. In some cases, these were anticipated: an early version used a simple consistency flag that was later replaced with a full-featured consistency protocol. In other cases, performance limitations and software and hardware changes prompted a redesign. Sequential communication tasks were parallelized; host-to-host MPI calls were replaced with GPU device-to-device MPI calls; more advanced algorithms such as Communication Avoiding LU and the Random Butterfly Transform (RBT) were introduced. Early choices that turned out to be cumbersome, error prone, or inflexible have been replaced with simpler, more intuitive, or more flexible designs. Applications have been a driving force, prompting a lighter weight queue class, nonuniform tile sizes, and more flexible MPI process grids. Of paramount importance has been building a portable library that works across several different GPU architectures – AMD, Intel, and NVIDIA – while keeping a clean and maintainable codebase. Here we explore the evolving design choices and their effects, both in terms of performance and software sustainability.

The International Journal of High Performance Computing ApplicationsVol. 39(1)
Oak Ridge National Laboratory (US), Ansys (United States) (US), Hewlett Packard Enterprise (United States) (US), Nvidia (United States) (US), Massachusetts Institute of Technology (US), King Abdullah University of Science and Technology (SA), University of Tennessee at Knoxville (US)
National Science Foundation, U.S. Department of Energy
Industry, innovation and infrastructure
Openalex Percentile: Top 12%
Parallel Computing and Optimization Techniques
1.96
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