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Analysis of threading libraries for high performance computing

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10.1109/TC.2020.2970706
 
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hdl:2117/187368

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Castello, Adrián
Mayo, Rafael
Sangmin, Seo
Pavan, Balaji
Quintana Ortí, Enrique Salvador
Peña, AntonioMés informació
Document typeArticle
Defense date2020
PublisherIEEE
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
ProjectECO-H-MEM - Advanced Ecosystem for Broad Heterogeneous Memory Usage (EC-H2020-749516)
Abstract
With the appearance of multi-many core machines, applications and runtime systems evolved in order to exploit the new on-node concurrency that brought new software paradigms. POSIX threads (Pthreads) was widely-adopted for that purpose and it remains as the most used threading solution in current hardware. Lightweight thread (LWT) libraries emerged offering lighter mechanisms to tackle the massive concurrency that current hardware is offering. In this paper, we analyze in detail the most representative threading libraries including Pthread- and LWT-based solutions. In addition, to examine the suitability of LWTs for different use cases, we develop a set of microbenchmarks consisting of commonly found OpenMP patterns in current parallel codes, and we compare the results using threading libraries and OpenMP implementations. Moreover, we study the semantics offered by threading libraries in order to expose the similarities among different LWT application programming interfaces and their advantages over Pthreads. This study reveals that LWT libraries outperform solutions based on operating system threads in cases where tasks and nested parallelism are required.
CitationCastello, A. [et al.]. Analysis of threading libraries for high performance computing. "IEEE Transactions on Computers", 2020. 
URIhttp://hdl.handle.net/2117/187368
DOI10.1109/TC.2020.2970706
ISSN1557-9956
Publisher versionhttps://ieeexplore.ieee.org/abstract/document/8976296/authors
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  • Computer Sciences - Articles de revista [259]
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