Linear solvers for power grid optimization problems: a review of GPU-accelerated linear solvers

Kasia Swirydowicz; Eric Darve; Wesley Jones; Jonathan Maack; Shaked Regev; Michael A. Saunders; Stephen J. Thomas; Slaven Peles

doi:10.1016/j.parco.2021.102870

arXiv · 2106.13909

Linear solvers for power grid optimization problems: a review of GPU-accelerated linear solvers

Kasia Swirydowicz·Eric Darve·Wesley Jones·Jonathan Maack·Shaked Regev·Michael A. Saunders·Stephen J. Thomas·Slaven Peles

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Abstract

The linear equations that arise in interior methods for constrained optimization are sparse symmetric indefinite and become extremely ill-conditioned as the interior method converges. These linear systems present a challenge for existing solver frameworks based on sparse LU or LDL^T decompositions. We benchmark five well known direct linear solver packages 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.

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BibTeX RIS

Kasia Swirydowicz, Eric Darve, Wesley Jones, Jonathan Maack, Shaked Regev, Michael A. Saunders, Stephen J. Thomas, Slaven Peles. 2021-08-13. Linear solvers for power grid optimization problems: a review of GPU-accelerated linear solvers. https://doi.org/10.1016/j.parco.2021.102870

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