Fork-join and data-driven execution models on multi-core architectures: Case study of the FMM

Handle URI:
http://hdl.handle.net/10754/575764
Title:
Fork-join and data-driven execution models on multi-core architectures: Case study of the FMM
Authors:
Amer, Abdelhalim; Maruyama, Naoya; Pericàs, Miquel; Taura, Kenjiro; Yokota, Rio ( 0000-0001-7573-7873 ) ; Matsuoka, Satoshi
Abstract:
Extracting maximum performance of multi-core architectures is a difficult task primarily due to bandwidth limitations of the memory subsystem and its complex hierarchy. In this work, we study the implications of fork-join and data-driven execution models on this type of architecture at the level of task parallelism. For this purpose, we use a highly optimized fork-join based implementation of the FMM and extend it to a data-driven implementation using a distributed task scheduling approach. This study exposes some limitations of the conventional fork-join implementation in terms of synchronization overheads. We find that these are not negligible and their elimination by the data-driven method, with a careful data locality strategy, was beneficial. Experimental evaluation of both methods on state-of-the-art multi-socket multi-core architectures showed up to 22% speed-ups of the data-driven approach compared to the original method. We demonstrate that a data-driven execution of FMM not only improves performance by avoiding global synchronization overheads but also reduces the memory-bandwidth pressure caused by memory-intensive computations. © 2013 Springer-Verlag.
KAUST Department:
Extreme Computing Research Center
Publisher:
Springer Science + Business Media
Journal:
Lecture Notes in Computer Science
Conference/Event name:
28th International Supercomputing Conference on Supercomputing, ISC 2013
Issue Date:
2013
DOI:
10.1007/978-3-642-38750-0_19
Type:
Conference Paper
ISSN:
03029743
ISBN:
9783642387494
Appears in Collections:
Conference Papers; Extreme Computing Research Center; Extreme Computing Research Center

Full metadata record

DC FieldValue Language
dc.contributor.authorAmer, Abdelhalimen
dc.contributor.authorMaruyama, Naoyaen
dc.contributor.authorPericàs, Miquelen
dc.contributor.authorTaura, Kenjiroen
dc.contributor.authorYokota, Rioen
dc.contributor.authorMatsuoka, Satoshien
dc.date.accessioned2015-08-24T09:25:34Zen
dc.date.available2015-08-24T09:25:34Zen
dc.date.issued2013en
dc.identifier.isbn9783642387494en
dc.identifier.issn03029743en
dc.identifier.doi10.1007/978-3-642-38750-0_19en
dc.identifier.urihttp://hdl.handle.net/10754/575764en
dc.description.abstractExtracting maximum performance of multi-core architectures is a difficult task primarily due to bandwidth limitations of the memory subsystem and its complex hierarchy. In this work, we study the implications of fork-join and data-driven execution models on this type of architecture at the level of task parallelism. For this purpose, we use a highly optimized fork-join based implementation of the FMM and extend it to a data-driven implementation using a distributed task scheduling approach. This study exposes some limitations of the conventional fork-join implementation in terms of synchronization overheads. We find that these are not negligible and their elimination by the data-driven method, with a careful data locality strategy, was beneficial. Experimental evaluation of both methods on state-of-the-art multi-socket multi-core architectures showed up to 22% speed-ups of the data-driven approach compared to the original method. We demonstrate that a data-driven execution of FMM not only improves performance by avoiding global synchronization overheads but also reduces the memory-bandwidth pressure caused by memory-intensive computations. © 2013 Springer-Verlag.en
dc.publisherSpringer Science + Business Mediaen
dc.titleFork-join and data-driven execution models on multi-core architectures: Case study of the FMMen
dc.typeConference Paperen
dc.contributor.departmentExtreme Computing Research Centeren
dc.identifier.journalLecture Notes in Computer Scienceen
dc.conference.date16 June 2013 through 20 June 2013en
dc.conference.name28th International Supercomputing Conference on Supercomputing, ISC 2013en
dc.conference.locationLeipzigen
dc.contributor.institutionTokyo Institute of Technology, Tokyo, Japanen
dc.contributor.institutionRIKEN, Kobe, Japanen
dc.contributor.institutionUniversity of Tokyo, Tokyo, Japanen
kaust.authorYokota, Rioen
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