Article

MCSTL: the multi-core standard template library

Authors:

Johannes Singler,

Felix PutzeAuthors Info & Claims

Euro-Par'07: Proceedings of the 13th international Euro-Par conference on Parallel Processing

Pages 682 - 694

Published: 28 August 2007 Publication History

Abstract

Future gain in computing performance will not stem from increased clock rates, but from even more cores in a processor. Since automatic parallelization is still limited to easily parallelizable sections of the code, most applications will soon have to support parallelism explicitly. The Multi-Core Standard Template Library (MCSTL) simplifies parallelization by providing efficient parallel implementations of the algorithms in the C++ Standard Template Library. Thus, simple recompilation will provide partial parallelization of applications that make consistent use of the STL. We present performance measurements on several architectures. For example, our sorter achieves a speedup of 21 on an 8-core 32-thread SUN T1.

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Cited By

Che YLai ZSun SWang YLuo Q(2020)Accelerating truss decomposition on heterogeneous processorsProceedings of the VLDB Endowment10.14778/3401960.340197113:10(1751-1764)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.14778/3401960.3401971
Obeya OKahssay EFan EShun JScheideler CBerenbrink P(2019)Theoretically-Efficient and Practical Parallel In-Place Radix SortingThe 31st ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3323165.3323198(213-224)Online publication date: 17-Jun-2019
https://dl.acm.org/doi/10.1145/3323165.3323198
Butcher NOlivier SBerry JHammond SKogge P(2018)Optimizing for KNL Usage Modes When Data Doesn't Fit in MCDRAMProceedings of the 47th International Conference on Parallel Processing10.1145/3225058.3225116(1-10)Online publication date: 13-Aug-2018
https://dl.acm.org/doi/10.1145/3225058.3225116
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Index Terms

MCSTL: the multi-core standard template library
1. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel algorithms
2. Theory of computation
  1. Design and analysis of algorithms
    1. Data structures design and analysis
      1. Sorting and searching
    2. Parallel algorithms
  2. Models of computation
    1. Concurrency
      1. Parallel computing models

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Guide Proceedings

Euro-Par'07: Proceedings of the 13th international Euro-Par conference on Parallel Processing

August 2007

969 pages

ISBN:3540744657

Editors:
Anne-Marie Kermarrec
IRISA/INRIA, Campus de Beaulieu, Rennes Cedex, France
,
Luc Bougé
IRISA/ENS Cachan, Campus de Beaulieu, Rennes Cedex, France
,
Thierry Priol
IRISA/INRIA, Campus de Beaulieu, Rennes Cedex, France

Sponsors

INRIA/IRISA: INRIA/IRISA
University of Rennes 1: University of Rennes 1
Rennes Métropole
Métivier Foundation
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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 28 August 2007

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Cited By

Che YLai ZSun SWang YLuo Q(2020)Accelerating truss decomposition on heterogeneous processorsProceedings of the VLDB Endowment10.14778/3401960.340197113:10(1751-1764)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.14778/3401960.3401971
Obeya OKahssay EFan EShun JScheideler CBerenbrink P(2019)Theoretically-Efficient and Practical Parallel In-Place Radix SortingThe 31st ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3323165.3323198(213-224)Online publication date: 17-Jun-2019
https://dl.acm.org/doi/10.1145/3323165.3323198
Butcher NOlivier SBerry JHammond SKogge P(2018)Optimizing for KNL Usage Modes When Data Doesn't Fit in MCDRAMProceedings of the 47th International Conference on Parallel Processing10.1145/3225058.3225116(1-10)Online publication date: 13-Aug-2018
https://dl.acm.org/doi/10.1145/3225058.3225116
Sun YFerizovic DBelloch G(2018)PAMACM SIGPLAN Notices10.1145/3200691.317850953:1(290-304)Online publication date: 10-Feb-2018
https://dl.acm.org/doi/10.1145/3200691.3178509
Sun YFerizovic DBelloch GKrall AGross T(2018)PAMProceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming10.1145/3178487.3178509(290-304)Online publication date: 10-Feb-2018
https://dl.acm.org/doi/10.1145/3178487.3178509
Sanders PLamm SHübschle-Schneider LSchrade EDachsbacher C(2018)Efficient Parallel Random Sampling—Vectorized, Cache-Efficient, and OnlineACM Transactions on Mathematical Software10.1145/315773444:3(1-14)Online publication date: 3-Jan-2018
https://dl.acm.org/doi/10.1145/3157734
Bender MBerry JHammond SScott Hemmert KMcCauley SMoore BMoseley BPhillips CResnick DRodrigues A(2017)Two-level main memory co-designJournal of Parallel and Distributed Computing10.1016/j.jpdc.2016.12.009102:C(213-228)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1016/j.jpdc.2016.12.009
Bingmann TEberle ASanders P(2017)Engineering Parallel String SortingAlgorithmica10.1007/s00453-015-0071-177:1(235-286)Online publication date: 1-Jan-2017
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Papatriantafyllou ASacharidis D(2016)High Performance Parallel Summed-Area Table Kernels for Multi-core and Many-core SystemsProceedings of the 22nd International Conference on Euro-Par 2016: Parallel Processing - Volume 983310.1007/978-3-319-43659-3_23(306-318)Online publication date: 24-Aug-2016
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Gu YShun JSun YBlelloch GBlelloch GAgrawal K(2015)A Top-Down Parallel SemisortProceedings of the 27th ACM symposium on Parallelism in Algorithms and Architectures10.1145/2755573.2755597(24-34)Online publication date: 13-Jun-2015
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