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English(EN) Heuristic-Based Merging of HPC Traces to Extend Hardware Counter Coverage

HPC跟踪合并框架扩展了用于机器学习的硬件计数器覆盖范围

研究人员开发了一种新的基于启发式的方法来合并高性能计算(HPC)执行跟踪,旨在扩展用于机器学习的硬件计数器覆盖范围,以进行性能预测。该技术通过合并每次运行不同计数器的跟踪来解决同时收集有限硬件计数器集的问题。该方法使用MPI结构、时间和通信模式匹配跨执行的计算爆发,以创建具有更丰富特征空间的统一数据集,用于训练机器学习模型,而无需手动选择计数器。在MareNostrum5机器上的验证表明,合并后的计数器对各种应用程序和内核保持了可接受的准确性。 AI

影响 为机器学习模型提供更全面的硬件计数器数据,有可能提高HPC性能预测的准确性。

排序理由 发布了一篇关于HPC跟踪分析新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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HPC跟踪合并框架扩展了用于机器学习的硬件计数器覆盖范围

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发布了一篇关于HPC跟踪分析新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Marta Garcia-Gasulla ·

    基于启发式的方法合并HPC轨迹以扩展硬件计数器覆盖范围

    This work extends a framework for predicting the performance of High-Performance Computing (HPC) workloads using Machine Learning (ML). A common limitation in performance modeling is the restricted number of hardware counters that can be collected simultaneously. To address this,…