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This paper introduces SimPoint+, an enhanced sampled simulation methodology that addresses key limitations of the widely-used SimPoint approach. SimPoint+ achieves greater stability, accuracy, and efficiency in program analysis through three major improvements: (1) UMAP-based dimensionality reduction for Basic Block Vectors, (2) a two-stage clustering approach utilizing HDBSCAN, and (3) a lightweight cycle calibration method. Furthermore, an automated hyperparameter tuning strategy accommodates diverse program characteristics for the first two models. Evaluation of SPEC CPU 2006 benchmarks demonstrates that SimPoint+ significantly outperforms SimPoint by yielding more consistent results across runs, reducing cycle error rates by 3–5 orders of magnitude, and decreasing required simulation time by 25%–55% overall. SimPoint+ facilitates more reliable and efficient simulation for computer architecture research, providing a robust foundation for rapid design space exploration and performance analysis of complex processors.

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SimPoint+: More Stable, Accurate and Efficient Program Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Jiangying Xue,
  • Tianyu Xiong,
  • Lingwei Chao,
  • Ruini Xue

摘要

This paper introduces SimPoint+, an enhanced sampled simulation methodology that addresses key limitations of the widely-used SimPoint approach. SimPoint+ achieves greater stability, accuracy, and efficiency in program analysis through three major improvements: (1) UMAP-based dimensionality reduction for Basic Block Vectors, (2) a two-stage clustering approach utilizing HDBSCAN, and (3) a lightweight cycle calibration method. Furthermore, an automated hyperparameter tuning strategy accommodates diverse program characteristics for the first two models. Evaluation of SPEC CPU 2006 benchmarks demonstrates that SimPoint+ significantly outperforms SimPoint by yielding more consistent results across runs, reducing cycle error rates by 3–5 orders of magnitude, and decreasing required simulation time by 25%–55% overall. SimPoint+ facilitates more reliable and efficient simulation for computer architecture research, providing a robust foundation for rapid design space exploration and performance analysis of complex processors.