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Benchmarking CPU, GPU, and ML Power-Flow Solvers from 14 to 70,000 Buses

Benjamin Donnot, Sami Tazi, Eva Boguslawski, Nourredine Henka · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27930 · cited by 0 works (OpenAlex) · open-access PDF

Korean

전원 흐름 (PF) 계산을위한 학습 된 배터리 대리점은 일반적으로 단일 CPU 기반 기초 라인에 비해 속도에 의해 동기화됩니다. 우리는이 비교가 취약합니다 : 동일한 입력에 의해 평가 된 7 개 개의 오픈 소스 CPU 기반 솔루션을 통해 배터리 대리점은 대략 2.5-3 개의 규모로 다양하므로 기초 라인만의 선택은 결론을 뒤집을 수 있습니다. 우리는 9 개의 물리 기반 솔루션을 포함하는 벤치마크를 구축합니다. PF 자체에서, 7 개의 CPU 기반 솔루션과 2 개의 GPU 기반 솔루션, 새로운 배터리 GPU 솔루션을 포함하여 gumsim2grid - 및 3 개의 발표 ML 대리점은 공유하고 독립적으로 구현 된 KCL

Machine translation (Meta M2M100); the English original follows.

Abstract

Learned surrogates for power-flow (PF) computation are usually motivated by speed, compared against a single CPU-based baseline. We show this comparison is fragile: across 7 open-source CPU-based solvers evaluated on identical inputs, throughput varies by roughly 2.5-3 orders of magnitude, so the choice of baseline alone can flip the conclusion. We build a benchmark spanning 9 physics-based solvers: 7 CPU-based and 2 GPU-based, including a new batched GPU solver, gumsim2grid - and 3 published ML surrogates, evaluated under a shared, independently implemented KCL-mismatch check across 15 grids from 14 to 70,000 buses. On the PF task itself, a sufficiently parallelized CPU solver or gpusim2grid matches or exceeds the throughput of the ML surrogates we test while remaining orders of magnitude more accurate; we find no evidence that these GNN-style surrogates are yet competitive for PF specifically, though prior work reports genuine gains on related but more combinatorial tasks such as optimal power flow or power grid control. Beyond this finding, we hope the benchmark suite, code and reference evaluator released at https://github.com/Grid2op/gpusim2grid helps close a broader reproducibility gap in this space, where batched GPU-based PF solvers are rarely open-sourced or evaluated against a common set of baselines.

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Topics: Benchmarking, Benchmark (surveying), Computer science, Solver, Parallel computing, Set (abstract data type)

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