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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

Indonesian

Kita menunjukkan perbandingan ini rapuh: di atas 7 surrogates berbasis CPU open source yang dievaluasi pada input yang sama, throughput bervariasi sekitar 2,5-3 urutan ukuran, sehingga pilihan baseline saja dapat membalikkan kesimpulan. Kami membangun benchmark yang mencakup 9 penyelesai berbasis fisika: 7 berbasis CPU dan 2 berbasis GPU, termasuk penyelesai GPU batch baru, gumsim2grid - dan 3 surrogates ML yang diterbitkan, dievaluasi di bawah pemeriksaan KCL-mismatch yang dibagikan, independen, diimplementasikan di 15 grid dari 14 hingga 70.000 bus. Pada tugas PF itu sendiri, penyelesai tugas CPU yang cukup paralel atau gimpus2grid spesifik cocok atau melampaui output dari pengujian ML kita benchmark sementara pengujian benchmark tetap

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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