Adaptive variable sampling model for performance analysis in high cache-performance computing environments

Mincheol Shin; Mucheol Kim; Geunchul Park; Ajith Abraham

doi:10.1016/j.heliyon.2023.e16777

Adaptive variable sampling model for performance analysis in high cache-performance computing environments

Heliyon. 2023 Jun 5;9(6):e16777. doi: 10.1016/j.heliyon.2023.e16777. eCollection 2023 Jun.

Authors

Mincheol Shin¹, Mucheol Kim¹, Geunchul Park², Ajith Abraham³

Affiliations

¹ Department of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea.
² Division of Supercomputing, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea.
³ Faculty of Computing and Data Science, FLAME University, Lavale, Pune, Maharashtra, India.

Abstract

High-performance computing provides computing power for a variety of scientific disciplines, supporting advancements by offering insights beyond metacognition. Maximizing computing performance without wasting resources is a major research issue. Predicting the performance of a computer's next state is effective for scheduling. However, hardware performance monitors representing the computer's state require high expert knowledge, and there is no standardized model. In this paper, we propose an adaptive variable sampling model for performance analysis in high-performance computing environments. Our method automatically classifies the optimal variables from numerous variables related to performance prediction and predicts performance using the sampled variables. The optimal variables for performance analysis do not require expert knowledge during the sampling process. We conducted experiments in various architectures and applications to validate this method. This model performed at least 24.25% and up to 58.75% faster without any loss in accuracy.

Keywords: Data science; Decision support; High performance computing; Machine learning; Performance prediction.