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

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 empi...

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Vydané v:	International journal of parallel programming Ročník 53; číslo 4; s. 24
Hlavní autori:	Rista, Cassiano, Teixeira, Marcelo, Fonseca, Mauro
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.08.2025 Springer Nature B.V
Predmet:	Benchmarks Computer Science Parameters Processor Architectures Simulation Simulation models Software Engineering/Programming and Operating Systems Stress analysis Theory of Computation Simulation Empirical development Benchmarks Autonomous computing Modeling Model-driven development Reconfiguration
ISSN:	0885-7458, 1573-7640
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Abstract	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.
AbstractList	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.
ArticleNumber	24
Author	Fonseca, Mauro Rista, Cassiano Teixeira, Marcelo
Author_xml	– sequence: 1 givenname: Cassiano surname: Rista fullname: Rista, Cassiano email: rista@utfpr.edu.br organization: Federal University of Technology – PR – CPGEI – sequence: 2 givenname: Marcelo surname: Teixeira fullname: Teixeira, Marcelo organization: Federal University of Technology – PR – CPGEI, Federal University of Technology – PR – PPGEEC – sequence: 3 givenname: Mauro surname: Fonseca fullname: Fonseca, Mauro organization: Federal University of Technology – PR – CPGEI
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SubjectTerms	Benchmarks Computer Science Parameters Processor Architectures Simulation Simulation models Software Engineering/Programming and Operating Systems Stress analysis Theory of Computation
Title	Simulation-Based Parameter Optimization for Self-adaptive HPL on Parallel Systems
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