Scholarlink

<p>Computational benchmarks are essential for dependable systems, applications, and technologies across multiple domains. However, traditional benchmarks such as High-Performance Linpack (HPL) require parameters to be set to reflect the characteristics of the system under test, and the adoption of empirical fine-tuning strategies is inevitable. As an alternative, adopting simulation models allows the representation of the stochastic nature of benchmark events and provides in-advance estimations instead of stress analysis. This paper extends the HPL benchmark with self-adaptation capabilities (SA-HPL) and proposes a simulation model that enables the automatic configuration of the SA-HPL parameters, allowing the benchmark to adapt effectively, quickly, and autonomously to various scheduling strategies, including static and on-demand schedulers. Experiments conducted over real data show reasonable accuracy between estimated and measured performance. The observed Mean Absolute Percentage Error in throughput was 1.26% for the Server and 3.76% for the Workstation experiments, while for efficiency, they were 1.15% and 3.52%, respectively. These results suggest significant savings in time, effort, and costs.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Simulation-Based Parameter Optimization for Self-adaptive HPL on Parallel Systems

  • 期刊论文

  • 发表日期：

    2025年6月16日

查看全文
  • Cassiano Rista,
  • Marcelo Teixeira,
  • Mauro Fonseca

摘要

Computational benchmarks are essential for dependable systems, applications, and technologies across multiple domains. However, traditional benchmarks such as High-Performance Linpack (HPL) require parameters to be set to reflect the characteristics of the system under test, and the adoption of empirical fine-tuning strategies is inevitable. As an alternative, adopting simulation models allows the representation of the stochastic nature of benchmark events and provides in-advance estimations instead of stress analysis. This paper extends the HPL benchmark with self-adaptation capabilities (SA-HPL) and proposes a simulation model that enables the automatic configuration of the SA-HPL parameters, allowing the benchmark to adapt effectively, quickly, and autonomously to various scheduling strategies, including static and on-demand schedulers. Experiments conducted over real data show reasonable accuracy between estimated and measured performance. The observed Mean Absolute Percentage Error in throughput was 1.26% for the Server and 3.76% for the Workstation experiments, while for efficiency, they were 1.15% and 3.52%, respectively. These results suggest significant savings in time, effort, and costs.