research-article

Implementing OmpSs support for regions of data in architectures with multiple address spaces

Authors:

Xavier Martorell,

Eduard Ayguadé,

Jesús LabartaAuthors Info & Claims

ICS '13: Proceedings of the 27th international ACM conference on International conference on supercomputing

Pages 359 - 368

https://doi.org/10.1145/2464996.2465017

Published: 10 June 2013 Publication History

Abstract

The need for features for managing complex data accesses in modern programming models has increased due to the emerging hardware architectures. HPC hardware has moved towards clusters of accelerators and/or multicores, architectures with a complex memory hierarchy exposed to the programmer.

We present the implementation of data regions on the OmpSs programming model, a high-productivity annotation-based programming model derived from OpenMP. This enables the programmer to specify regions of strided and/or overlapped data used by the parallel tasks of the application. The data will be automatically managed by the underlying run-time environment, which could transparently apply optimization techniques to improve performance.

This approach based on a high-productivity programming model contrasts with more direct approaches like MPI, where the programmer has to explicitly deal with the data management. It is generally believed that these are capable of achieving the best possible performance, so we also compare the performance of several OmpSs applications against well-known counterparts MPI implementations obtaining comparable or better results.

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Cited By

Polet PFantar RGautier T(2023)Introducing Moldable Tasks in OpenMPOpenMP: Advanced Task-Based, Device and Compiler Programming10.1007/978-3-031-40744-4_4(51-65)Online publication date: 1-Sep-2023
https://doi.org/10.1007/978-3-031-40744-4_4
Yviquel HPereira MFrancesquini EValarini GLeite GRosso PCeccato RCusihualpa CDias VRigo SSouza AAraujo G(2022)The OpenMP Cluster Programming ModelWorkshop Proceedings of the 51st International Conference on Parallel Processing10.1145/3547276.3548444(1-11)Online publication date: 29-Aug-2022
https://dl.acm.org/doi/10.1145/3547276.3548444
Caheny PAlvarez LCasas MMoreto M(2022)TD-NUCA: Runtime Driven Management of NUCA Caches in Task Dataflow Programming ModelsSC22: International Conference for High Performance Computing, Networking, Storage and Analysis10.1109/SC41404.2022.00085(1-15)Online publication date: Nov-2022
https://doi.org/10.1109/SC41404.2022.00085
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Index Terms

Implementing OmpSs support for regions of data in architectures with multiple address spaces
1. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel programming languages
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types
        Parallel programming languages

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Published In

cover image ACM Conferences

ICS '13: Proceedings of the 27th international ACM conference on International conference on supercomputing

June 2013

512 pages

ISBN:9781450321303

DOI:10.1145/2464996

General Chair:
Allen D. Malony
University of Oregon, USA
,
Program Chairs:
Mario Nemirovsky
Barcelona Supercomputing Center, Spain
,
Sam Midkiff
Purdue University, USA

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 10 June 2013

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ICS'13

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SIGARCH

ICS'13: International Conference on Supercomputing

June 10 - 14, 2013

Oregon, Eugene, USA

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ICS '13 Paper Acceptance Rate 43 of 202 submissions, 21%;

Overall Acceptance Rate 629 of 2,180 submissions, 29%

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Cited By

Polet PFantar RGautier T(2023)Introducing Moldable Tasks in OpenMPOpenMP: Advanced Task-Based, Device and Compiler Programming10.1007/978-3-031-40744-4_4(51-65)Online publication date: 1-Sep-2023
https://doi.org/10.1007/978-3-031-40744-4_4
Yviquel HPereira MFrancesquini EValarini GLeite GRosso PCeccato RCusihualpa CDias VRigo SSouza AAraujo G(2022)The OpenMP Cluster Programming ModelWorkshop Proceedings of the 51st International Conference on Parallel Processing10.1145/3547276.3548444(1-11)Online publication date: 29-Aug-2022
https://dl.acm.org/doi/10.1145/3547276.3548444
Caheny PAlvarez LCasas MMoreto M(2022)TD-NUCA: Runtime Driven Management of NUCA Caches in Task Dataflow Programming ModelsSC22: International Conference for High Performance Computing, Networking, Storage and Analysis10.1109/SC41404.2022.00085(1-15)Online publication date: Nov-2022
https://doi.org/10.1109/SC41404.2022.00085
Fraguela BAndrade D(2022)The New UPC++ DepSpawn High Performance Library for Data-Flow Computing with Hybrid ParallelismComputational Science – ICCS 202210.1007/978-3-031-08751-6_55(761-774)Online publication date: 15-Jun-2022
https://doi.org/10.1007/978-3-031-08751-6_55
Patel ADoerfert J(2022)Remote OpenMP OffloadingHigh Performance Computing10.1007/978-3-031-07312-0_16(315-333)Online publication date: 29-May-2022
https://dl.acm.org/doi/10.1007/978-3-031-07312-0_16
Bauer MLee WSlaughter EJia ZDi Renzo MPapadakis MShipman GMcCormick PGarland MAiken ALee JPetrank E(2021)Scaling implicit parallelism via dynamic control replicationProceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming10.1145/3437801.3441587(105-118)Online publication date: 17-Feb-2021
https://dl.acm.org/doi/10.1145/3437801.3441587
Fraguela BAndrade D(2021)High-performance dataflow computing in hybrid memory systems with UPC++ DepSpawnThe Journal of Supercomputing10.1007/s11227-020-03607-1Online publication date: 8-Jan-2021
https://doi.org/10.1007/s11227-020-03607-1
Fraguela BAndrade D(2021)A software cache autotuning strategy for dataflow computing with UPC++ DepSpawnComputational and Mathematical Methods10.1002/cmm4.1148Online publication date: 22-Feb-2021
https://doi.org/10.1002/cmm4.1148
Fraguela BAndrade D(2019)Easy Dataflow Programming in Clusters with UPC++ DepSpawnIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2018.288471630:6(1267-1282)Online publication date: 1-Jun-2019
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Barik RShpeisman TRong HHu CLee VAnderson THenry GLiu HWu YPetersen PLowney G(2019)Mozart : Efficient Composition of Library Functions for Heterogeneous ExecutionLanguages and Compilers for Parallel Computing10.1007/978-3-030-35225-7_13(182-202)Online publication date: 15-Nov-2019
https://doi.org/10.1007/978-3-030-35225-7_13
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