Linear Solvers for Power Grid Optimization Problems: A Review of GPU-Accelerated Linear Solvers

Kasia Swirydowicz, Eric Darve, Wesley Jones, Jonathan Maack, Shaked Regev, Mchael Saunders, Stephen Thomas, Slaven Peles

Research output: Contribution to journalArticlepeer-review

9 Scopus Citations

Abstract

The linear equations that arise in interior methods for constrained optimization are sparse symmetric indefinite, and they become extremely ill-conditioned as the interior method converges. These linear systems present a challenge for existing solver frameworks based on sparse LU or LDLT decompositions. We benchmark five well known direct linear solver packages on CPU- and GPU-based hardware, using matrices extracted from power grid optimization problems. The achieved solution accuracy varies greatly among the packages. None of the tested packages delivers significant GPU acceleration for our test cases. For completeness of the comparison we include results for MA57, which is one of the most efficient and reliable CPU solvers for this class of problem.

Original languageAmerican English
Article number102870
Number of pages9
JournalParallel Computing
Volume111
DOIs
StatePublished - Jul 2022

Bibliographical note

Publisher Copyright:
© 2021

NREL Publication Number

  • NREL/JA-2C00-81949

Keywords

  • GPU
  • Grid optimization
  • Solvers
  • Sparse linear equations

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