Merged requests for better performance and productivity in multithreaded OpenSHMEM

Swen Boehm, Swaroop Pophale, Matthew B. Baker, Manjunath Gorentla Venkata

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

A merged request is a handle representing a group of Remote Memory Access (RMA), Atomic or Collective operations. The merged request can be created either by combining multiple outstanding merged request handles or using the same merged request handle for additional operations. We show that introducing such simple yet powerful semantics in OpenSHMEM provides many productivity and performance advantages. In this paper, we first introduce the interfaces and semantics for creating and using merged request handles. Then, we demonstrate with a merge request that we can achieve better performance characteristics in multithreaded OpenSHMEM application. Particularly, we show one can achieve higher message rate, a higher bandwidth for smaller message, and better computation-communication overlap. Further, we use merged request to realize multithreaded collectives, where multiple threads co-operate to complete the collective operation. Our experimental results show that in a multithreaded OpenSHMEM program, the merged request based RMA operations achieve over 100 Million Messages Per Second (MMPS). It achieves over 10 MMPS compared to 4.5 MMPS with default RMA operations in a single threaded environment. Also, we achieve higher bandwidth for smaller message sizes, close to 100% overlap, and reduce the latency by 60%.

Original languageEnglish
Title of host publicationOpenSHMEM and Related Technologies
Subtitle of host publicationBig Compute and Big Data Convergence - 4th Workshop, OpenSHMEM 2017, Revised Selected Papers
EditorsManjunath Gorentla Venkata, Neena Imam, Swaroop Pophale
PublisherSpringer Verlag
Pages35-49
Number of pages15
ISBN (Print)9783319738130
DOIs
StatePublished - 2018
Event4th Workshop on OpenSHMEM and Related Technologies: Big Compute and Big Data Convergence, OpenSHMEM 2017 - Annapolis, United States
Duration: Aug 7 2017Aug 9 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10679 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th Workshop on OpenSHMEM and Related Technologies: Big Compute and Big Data Convergence, OpenSHMEM 2017
Country/TerritoryUnited States
CityAnnapolis
Period08/7/1708/9/17

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

FundersFunder number
U.S. Department of Defense
Oak Ridge National Laboratory

    Keywords

    • Interoperability
    • PGAS
    • Shared memory

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