Benchmarking CPU, GPU, and ML Power-Flow Solvers from 14 to 70,000 Buses
Benjamin Donnot, Sami Tazi, Eva Boguslawski, Nourredine Henka · arXiv: 2610.27930v1 · Summary page
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Power-flow solvers put learned shortcuts to the test
Predicting how electricity moves through a grid is a natural target for machine-learning shortcuts. But faster than what? A new benchmark finds that the choice of conventional solver can change the answer: CPU-based solvers given identical inputs differ in throughput by roughly 2.5-3 orders of magnitude. The researchers compared CPU, GPU and published learned solvers using a shared, independently implemented check of whether their answers obey Kirchhoff’s current law.
On the power-flow task, a sufficiently parallelized CPU solver or the team’s GPU solver matched or exceeded the tested learned surrogates while remaining orders of magnitude more accurate. The broader lesson is about experimental design: a weak baseline can make a new technique look transformative. The released benchmark and evaluator offer a way to make such comparisons less dependent on which existing implementation happens to be chosen.
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