Efficient Code Region Characterization Through Automatic Performance Counters Reduction Using Machine Learning Techniques
Harutyunyan Gevorgyan, Suren (Universitat Autònoma de Barcelona)
César Galobardes, Eduardo (Universitat Autònoma de Barcelona)
Sikora, Anna (Universitat Autònoma de Barcelona)
Filipovič, Jiří (Masaryk University)
Dutta, Akash (Iowa State University)
Jannesari, Ali (Iowa State University)
Alcaraz, Jordi (Universitat Autònoma de Barcelona)

Imprint:	Cham, Switzerland: Springer, 2024
Description:	15 pàg.
Abstract:	Leveraging hardware performance counters provides valuable insights into system resource utilization, aiding performance analysis and tuning for parallel applications. The available counters vary with architecture and are collected at execution time. Their abundance and the limited number of registers for measurement make gathering laborious and costly. Efficient characterization of parallel regions necessitates a dimension reduction strategy. While recent efforts have focused on manually reducing the number of counters for specific architectures, this paper introduces a novel approach: an automatic dimension reduction technique for efficiently characterizing parallel code regions across diverse architectures. The methodology is based on Machine Learning ensembles because of their precision and ability at capturing different relationships between the input features and the target variables. Evaluation results show that ensembles can successfully reduce the number of hardware performance counters that characterize a code region. We validate our approach on CPUs using a comprehensive dataset of OpenMP regions, showing that any region can be accurately characterized by 8 relevant hardware performance counters. In addition, we also apply the proposed methodology on GPUs using a reduced set of kernels, demonstrating its effectiveness across various hardware configurations and workloads.
Grants:	Agencia Estatal de Investigación PID2020-113614RB-C21 Agència de Gestió d'Ajuts Universitaris i de Recerca 2021/SGR-00574
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Series:	Lecture Notes in Computer Science; 14801
Document:	Capítol de llibre ; recerca ; Versió publicada
Subject:	Performance Counters ; Automatic Dimension Reduction ; Machine Learning Ensembles ; Parallel Region Classification
Published in:	Euro-Par 2024: Parallel Processing - 30th European Conference on Parallel and Distributed Processing, 2024, p. 18-32, ISBN 978-3-031-69576-6

DOI: 10.1007/978-3-031-69577-3_2

15 p, 551.5 KB

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