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

Year: 2023

Type: article

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.

Source: Heliyon

DOI: 10.1016/j.heliyon.2023.e16777

Authors: Mincheol Shin, Mucheol Kim, Geunchul Park, Ajith Abraham

Institutions: Chung-Ang University, Korea Institute of Science & Technology Information, Flame University

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,100