Enhancing resource management through prediction-based policies
Visualitza/Obre
10.1007/978-3-030-57675-2_31
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/329767
Tipus de documentText en actes de congrés
Data publicació2020
EditorSpringer
Condicions d'accésAccés obert
Tots els drets reservats. Aquesta obra està protegida pels drets de propietat intel·lectual i
industrial corresponents. Sense perjudici de les exempcions legals existents, queda prohibida la seva
reproducció, distribució, comunicació pública o transformació sense l'autorització del titular dels drets
ProjecteCOMPUTACION DE ALTAS PRESTACIONES VII (MINECO-TIN2015-65316-P)
DEEP-EST - DEEP (EC-H2020-754304)
HPC-EUROPA3 - Transnational Access Programme for a Pan-European Network of HPC Research Infrastructures and Laboratories for scientific computing (EC-H2020-730897)
BARCELONA SUPERCOMPUTING CENTER - CENTRO. NACIONAL DE SUPERCOMPUTACION (MINECO-SEV-2015-0493)
DEEP-EST - DEEP (EC-H2020-754304)
HPC-EUROPA3 - Transnational Access Programme for a Pan-European Network of HPC Research Infrastructures and Laboratories for scientific computing (EC-H2020-730897)
BARCELONA SUPERCOMPUTING CENTER - CENTRO. NACIONAL DE SUPERCOMPUTACION (MINECO-SEV-2015-0493)
Abstract
Task-based programming models are emerging as a promising alternative to make the most of multi-/many-core systems. These programming models rely on runtime systems, and their goal is to improve application performance by properly scheduling application tasks to cores. Additionally, these runtime systems offer policies to cope with application phases that lack in parallelism to fill all cores. However, these policies are usually static and favor either performance or energy efficiency. In this paper, we have extended a task-based runtime system with a lightweight monitoring and prediction infrastructure that dynamically predicts the optimal number of cores required for each application phase, thus improving both performance and energy efficiency. Through the execution of several benchmarks in multi-/many-core systems, we show that our prediction-based policies have competitive performance while improving energy efficiency when compared to state of the art policies.
CitacióNavarro, A. [et al.]. Enhancing resource management through prediction-based policies. A: International European Conference on Parallel and Distributed Computing. "Euro-Par 2020: Parallel Processing, 26th International Conference on Parallel and Distributed Computing: Warsaw, Poland, August 24–28, 2020: proceedings". Berlín: Springer, 2020, p. 493-509. ISBN 978-3-030-57675-2. DOI 10.1007/978-3-030-57675-2_31.
ISBN978-3-030-57675-2
Versió de l'editorhttps://link.springer.com/chapter/10.1007/978-3-030-57675-2_31
Col·leccions
- Doctorat en Arquitectura de Computadors - Ponències/Comunicacions de congressos [292]
- Computer Sciences - Ponències/Comunicacions de congressos [574]
- CAP - Grup de Computació d'Altes Prestacions - Ponències/Comunicacions de congressos [784]
- Departament d'Arquitectura de Computadors - Ponències/Comunicacions de congressos [1.954]
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