适应变量采样模型用于高缓存性能计算环境中的性能分析
Mincheol Shin1, Mucheol Kim1, Geunchul Park2
1Department of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea.
Heliyon
|June 26, 2023
概括
本研究介绍了用于高性能计算 (HPC) 的自适应模型,该模型自动选择关键变量用于性能预测. 这种方法提高了HPC环境中的效率和准确性,而不需要专家知识.
科学领域:
- 计算机科学 计算机科学
- 计算科学 计算科学
背景情况:
- 高性能计算 (HPC) 对科学进步至关重要,但优化其性能和资源利用是具有挑战性的.
- 预测系统状态有助于调度,但当前的硬件性能监测器需要专家知识,缺乏标准化.
研究的目的:
- 开发一种适应变量采样模型,用于HPC环境中的性能分析.
- 为了自动选择性能预测的最佳变量,减少对专家知识的依赖.
主要方法:
- 为HPC性能分析提出了一个自适应变量采样模型.
- 开发了一种方法,从大量与性能相关的参数中自动分类最佳变量.
- 在各种架构和应用程序中验证了模型.
主要成果:
- 该模型自动识别最佳变量,而不需要在采样过程中对专家进行输入.
- 在各种测试中实现了从24.25%到58.75%的性能改进.
- 保持了预测准确度,同时显著增加了速度.
结论:
- 适应变量采样模型为HPC性能分析提供了高效和准确的解决方案.
- 自动变量选择使性能优化民主化,使其超出专业专业知识的范围.
- 这种方法增强了HPC资源管理,加速了科学发现.
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