The STAPL parallel container framework

Gabriel Tanase; Antal Buss; Adam Fidel; null Harshvardhan; Ioannis Papadopoulos; Olga Pearce; Timmie Smith; Nathan Thomas; Xiabing Xu; Nedal Mohammed Nabeel Mourad; Jeremy Vu; Mauro Bianco; Nancy M. Amato; Lawrence Rauchwerger

doi:10.1145/2038037.1941586

The STAPL parallel container framework

Gabriel Tanase, Antal Buss, Adam Fidel, Harshvardhan, Ioannis Papadopoulos, Olga Pearce, Timmie Smith, Nathan Thomas, Xiabing Xu, Nedal Mohammed Nabeel Mourad, Jeremy Vu, Mauro Bianco, Nancy M. Amato, Lawrence Rauchwerger

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

Abstract

The Standard Template Adaptive Parallel Library (STAPL) is a parallel programming infrastructure that extends C++ with support for parallelism. It includes a collection of distributed data structures called pContainers that are thread-safe, concurrent objects, i.e., shared objects that provide parallel methods that can be invoked concurrently. In this work, we present the STAPL Parallel Container Framework (PCF), that is designed to facilitate the development of generic parallel containers. We introduce a set of concepts and a methodology for assembling a pContainer from existing sequential or parallel containers, without requiring the programmer to deal with concurrency or data distribution issues. The PCF provides a large number of basic parallel data structures (e.g., pArray, pList, pVector, pMatrix, pGraph, pMap, pSet). The PCF provides a class hierarchy and a composition mechanism that allows users to extend and customize the current container base for improved application expressivity and performance. We evaluate STAPL pContainer performance on a CRAY XT4 massively parallel system and show that pContainer methods, generic pAlgorithms, and different applications provide good scalability on more than 16,000 processors. Copyright © 2011 ACM.

Original language	English (US)
Pages (from-to)	235-246
Number of pages	12
Journal	ACM SIGPLAN Notices
Volume	46
Issue number	8
DOIs	https://doi.org/10.1145/2038037.1941586
State	Published - Sep 7 2011
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2021-03-31
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This research supported in part by NSF awards CRI-0551685, CCF0833199, CCF-0830753, IIS-096053, IIS-0917266, NSF/DNDO award
2008-DN-077-ARI018-02, by the DOE NNSA under the Predictive Science Academic Alliances Program by grant DE-FC52-08NA28616, by THECB NHARP award 000512-0097-2009, by Chevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Mourad is a masters student at KAUST who did an internship at the Parasol Lab. Tanase is currently a Research Staff Member at IBM T.J. Watson Research Center. Bianco is currently a scientist at the Swiss National Supercomputing Centre.

ASJC Scopus subject areas

General Computer Science

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10.1145/2038037.1941586

Cite this

@article{4afb0bcfe82546c59f3ba8eb508320fa,

title = "The STAPL parallel container framework",

abstract = "The Standard Template Adaptive Parallel Library (STAPL) is a parallel programming infrastructure that extends C++ with support for parallelism. It includes a collection of distributed data structures called pContainers that are thread-safe, concurrent objects, i.e., shared objects that provide parallel methods that can be invoked concurrently. In this work, we present the STAPL Parallel Container Framework (PCF), that is designed to facilitate the development of generic parallel containers. We introduce a set of concepts and a methodology for assembling a pContainer from existing sequential or parallel containers, without requiring the programmer to deal with concurrency or data distribution issues. The PCF provides a large number of basic parallel data structures (e.g., pArray, pList, pVector, pMatrix, pGraph, pMap, pSet). The PCF provides a class hierarchy and a composition mechanism that allows users to extend and customize the current container base for improved application expressivity and performance. We evaluate STAPL pContainer performance on a CRAY XT4 massively parallel system and show that pContainer methods, generic pAlgorithms, and different applications provide good scalability on more than 16,000 processors. Copyright {\textcopyright} 2011 ACM.",

author = "Gabriel Tanase and Antal Buss and Adam Fidel and Harshvardhan and Ioannis Papadopoulos and Olga Pearce and Timmie Smith and Nathan Thomas and Xiabing Xu and Mourad, {Nedal Mohammed Nabeel} and Jeremy Vu and Mauro Bianco and Amato, {Nancy M.} and Lawrence Rauchwerger",

note = "KAUST Repository Item: Exported on 2021-03-31 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: This research supported in part by NSF awards CRI-0551685, CCF0833199, CCF-0830753, IIS-096053, IIS-0917266, NSF/DNDO award 2008-DN-077-ARI018-02, by the DOE NNSA under the Predictive Science Academic Alliances Program by grant DE-FC52-08NA28616, by THECB NHARP award 000512-0097-2009, by Chevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Mourad is a masters student at KAUST who did an internship at the Parasol Lab. Tanase is currently a Research Staff Member at IBM T.J. Watson Research Center. Bianco is currently a scientist at the Swiss National Supercomputing Centre.",

year = "2011",

month = sep,

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doi = "10.1145/2038037.1941586",

language = "English (US)",

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AU - Tanase, Gabriel

AU - Buss, Antal

AU - Fidel, Adam

AU - Harshvardhan, null

AU - Papadopoulos, Ioannis

AU - Pearce, Olga

AU - Smith, Timmie

AU - Thomas, Nathan

AU - Xu, Xiabing

AU - Mourad, Nedal Mohammed Nabeel

AU - Vu, Jeremy

AU - Bianco, Mauro

AU - Amato, Nancy M.

AU - Rauchwerger, Lawrence

N1 - KAUST Repository Item: Exported on 2021-03-31 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: This research supported in part by NSF awards CRI-0551685, CCF0833199, CCF-0830753, IIS-096053, IIS-0917266, NSF/DNDO award 2008-DN-077-ARI018-02, by the DOE NNSA under the Predictive Science Academic Alliances Program by grant DE-FC52-08NA28616, by THECB NHARP award 000512-0097-2009, by Chevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Mourad is a masters student at KAUST who did an internship at the Parasol Lab. Tanase is currently a Research Staff Member at IBM T.J. Watson Research Center. Bianco is currently a scientist at the Swiss National Supercomputing Centre.

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N2 - The Standard Template Adaptive Parallel Library (STAPL) is a parallel programming infrastructure that extends C++ with support for parallelism. It includes a collection of distributed data structures called pContainers that are thread-safe, concurrent objects, i.e., shared objects that provide parallel methods that can be invoked concurrently. In this work, we present the STAPL Parallel Container Framework (PCF), that is designed to facilitate the development of generic parallel containers. We introduce a set of concepts and a methodology for assembling a pContainer from existing sequential or parallel containers, without requiring the programmer to deal with concurrency or data distribution issues. The PCF provides a large number of basic parallel data structures (e.g., pArray, pList, pVector, pMatrix, pGraph, pMap, pSet). The PCF provides a class hierarchy and a composition mechanism that allows users to extend and customize the current container base for improved application expressivity and performance. We evaluate STAPL pContainer performance on a CRAY XT4 massively parallel system and show that pContainer methods, generic pAlgorithms, and different applications provide good scalability on more than 16,000 processors. Copyright © 2011 ACM.

AB - The Standard Template Adaptive Parallel Library (STAPL) is a parallel programming infrastructure that extends C++ with support for parallelism. It includes a collection of distributed data structures called pContainers that are thread-safe, concurrent objects, i.e., shared objects that provide parallel methods that can be invoked concurrently. In this work, we present the STAPL Parallel Container Framework (PCF), that is designed to facilitate the development of generic parallel containers. We introduce a set of concepts and a methodology for assembling a pContainer from existing sequential or parallel containers, without requiring the programmer to deal with concurrency or data distribution issues. The PCF provides a large number of basic parallel data structures (e.g., pArray, pList, pVector, pMatrix, pGraph, pMap, pSet). The PCF provides a class hierarchy and a composition mechanism that allows users to extend and customize the current container base for improved application expressivity and performance. We evaluate STAPL pContainer performance on a CRAY XT4 massively parallel system and show that pContainer methods, generic pAlgorithms, and different applications provide good scalability on more than 16,000 processors. Copyright © 2011 ACM.

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The STAPL parallel container framework

Abstract

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