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We examine the impact of manual elimination of thread divergence in GPU code through removal of all branches using a flattening technique. The goal is to investigate the necessity of manual mitigation of thread divergence on GPU, compared with automated, modern compiler optimization and architectural improvements. We apply our previously presented flattening technique called Algorithm Flattening (AF), which eliminates all branches, producing divergence-free code with increased ILP at the expense of minor to moderate increased instruction overhead. We observe the effect of said optimization on kernel performance across historical architectures and compilers, up to recent offerings. We theorize that modern GPU improvements will eventually eliminate the need for programmer intervention of thread divergence coding issues for GPU, although further study is necessary.

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Is Manual Code Optimization Still Required to Mitigate GPU Thread Divergence? Applying a Flattening Technique to Observe Performance

  • 会议论文

  • 发表日期：

    2025年

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  • Lucas Vespa

摘要

We examine the impact of manual elimination of thread divergence in GPU code through removal of all branches using a flattening technique. The goal is to investigate the necessity of manual mitigation of thread divergence on GPU, compared with automated, modern compiler optimization and architectural improvements. We apply our previously presented flattening technique called Algorithm Flattening (AF), which eliminates all branches, producing divergence-free code with increased ILP at the expense of minor to moderate increased instruction overhead. We observe the effect of said optimization on kernel performance across historical architectures and compilers, up to recent offerings. We theorize that modern GPU improvements will eventually eliminate the need for programmer intervention of thread divergence coding issues for GPU, although further study is necessary.