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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Bibliographic Details
Published in:International journal of parallel programming Vol. 53; no. 4; p. 24
Main Authors: Rista, Cassiano, Teixeira, Marcelo, Fonseca, Mauro
Format: Journal Article
Language:English
Published: New York Springer US 01.08.2025
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-025-00803-5