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TALP: A Lightweight Tool to Unveil Parallel Efficiency of Large-scale Executions

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Lopez, Victor
Ramirez Miranda, Guillem
Garcia Gasulla, MartaMés informació
Document typeConference lecture
Defense date2021
PublisherAssociation for Computing Machinery
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
ProjectPOP2 - Performance Optimisation and Productivity 2 (EC-H2020-824080)
Abstract
This paper presents the design, implementation, and application of TALP, a lightweight, portable, extensible, and scalable tool for online parallel performance measurement. The efficiency metrics reported by TALP allow HPC users to evaluate the parallel efficiency of their executions, both post-mortem and at runtime. The API that TALP provides allows the running application or resource managers to collect performance metrics at runtime. This enables the opportunity to adapt the execution based on the metrics collected dynamically. The set of metrics collected by TALP are well defined, independent of the tool, and consolidated. We extend the collection of metrics with two additional ones that can differentiate between the load imbalance originated from the intranode or internode imbalance. We evaluate the potential of TALP with three parallel applications that present various parallel issues and carefully analyze the overhead introduced to determine its limitations.
CitationLopez, V.; Ramirez Miranda, G.; Garcia Gasulla, M. TALP: A Lightweight Tool to Unveil Parallel Efficiency of Large-scale Executions. A: HPDC: High-Performance Parallel and Distributed Computing. "In Proceedings of the 2021 on Performance EngineeRing, Modelling, Analysis, and VisualizatiOn STrategy (PERMAVOST '21): 25 June, 2021: Virtual Event Sweden". New York, NY, USA: Association for Computing Machinery, 2021, p. 3-10. ISBN 978-1-4503-8387-5. DOI doi.org/10.1145/3452412.3462753. 
URIhttp://hdl.handle.net/2117/349221
DOIdoi.org/10.1145/3452412.3462753
ISBN978-1-4503-8387-5
Publisher versionhttps://dl.acm.org/doi/10.1145/3452412.3462753?sid=SCITRUS
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