Archived
2026-09-12GPU-CFR: 80x Faster Counterfactual Regret Minimization by Compiling the Game to Static Dataflow and CUDA Graph Replay
Boning Li, Longbo Huang
arXiv:2609.11923v1Counterfactual regret minimization is a workhorse algorithm behind many game-playing and strategic reasoning systems, but oddly enough, it has often run faster on CPUs than on GPUs. The bottleneck is not the math itself, but the overhead of launching thousands of tiny GPU kernels and moving through a game tree with highly irregular control flow. This paper shows that for a fixed game, almost everything about each CFR iteration is known in advance. The authors compile the game once into a static dataflow representation, precompute the indices and batched passes, and then use CUDA Graph replay so the GPU can rerun the whole iteration with almost no dispatch overhead. The result is dramatic: up to 80x speedups over prior GPU CFR systems, and even strong gains over optimized CPU implementations. More broadly, it is a nice example of how compiler-style thinking can unlock hardware that was already there, turning an awkward dynamic workload into something GPUs can actually excel at.
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