ParallelKernelBench: Frontier LLMs can't write fast multi-GPU kernels (yet)

ParallelKernelBench (PKB) provides a benchmark and evaluation framework for multi-GPU kernel generation, focusing on replacing PyTorch + NCCL with CUDA kernels that utilize NVLink for data transfer. While large language models (LLMs) like GPT-5.5 and others have shown some proficiency in generating single-GPU kernels, their performance in multi-GPU scenarios is limited, with only a minority of generated solutions outperforming naive baselines. The evaluation highlighted key challenges such as reasoning about rank coordination, data partitioning, and optimal GPU-to-GPU transfer mechanisms, with most models relying on limited communication mechanisms and struggling with intricate hardware-specific abstractions. Although some models achieved notable speedups, particularly in familiar patterns like collective primitives, the overall findings underscore significant gaps in current LLM capabilities for multi-GPU kernel optimization. PKB's open benchmark aims to foster further research into AI-driven optimization and distributed infrastructure management, encouraging contributions and exploration into inter-node fabrics and other advanced architectures.

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