Design and Implementation of an Extended Collectives Library for Unified Parallel C

Unified Parallel C （UPC） is a parallel extension of ANSI C based on the Partitioned Global Address Space （PGAS） programming model, which provides a shared memory view that simplifies code development while it can take advantage of the scalability of distributed memory architectures. Therefore, UPC a...

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Published in:	Journal of computer science and technology Vol. 28; no. 1; pp. 72 - 89
Main Authors:	Teijeiro, Carlos, Taboada, Guillermo L., Touriño, Juan, Doallo, Ramón, Mouriño, José C., Mallón, Damián A., Wibecan, Brian
Format:	Journal Article
Language:	English
Published:	Boston Springer US 2013 Springer Nature B.V Computer Architecture Group, University of A Coru(n)a, A Coru(n)a 15071, Spain%Galicia Supercomputing Center, Santiago de Compostela 15705, Spain%Jülich Supercomputing Centre, Institute for Advanced Simulation, Forschungszentrum Jülich, Jülich D-52425, Germany%Industry Standard Servers Group, Hewlett-Packard Company, Montgomery, Alabama 36117, U.S.A
Subjects:	Algorithms Analysis Artificial Intelligence C (programming language) Computer programming Computer Science Data Structures and Information Theory Distributed memory Fourier transforms High performance computing Information Systems Applications (incl.Internet) Libraries Mathematical analysis Mathematical models Parallel processing Programming Proposals Regular Paper Software Software Engineering Studies Theory of Computation Variables 共享内存内存架构并行编程应用程序性能计算编程模型设计集体 collective operation partitioned global address space Unified Parallel C high performance computing programmability
ISSN:	1000-9000, 1860-4749
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Abstract	Unified Parallel C （UPC） is a parallel extension of ANSI C based on the Partitioned Global Address Space （PGAS） programming model, which provides a shared memory view that simplifies code development while it can take advantage of the scalability of distributed memory architectures. Therefore, UPC allows programmers to write parallel applications on hybrid shared/distributed memory architectures, such as multi-core clusters, in a more productive way, accessing remote memory by means of different high-level language constructs, such as assignments to shared variables or collective primitives. However, the standard UPC collectives library includes a reduced set of eight basic primitives with quite limited functionality. This work presents the design and implementation of extended UPC collective functions that overcome the limitations of the standard collectives library, allowing, for example, the use of a specific source and destination thread or defining the amount of data transferred by each particular thread. This library fulfills the demands made by the UPC developers community and implements portable algorithms, independent of the specific UPC compiler/runtime being used. The use of a representative set of these extended collectives has been evaluated using two applications and four kernels as case studies. The results obtained confirm the suitability of the new library to provide easier programming without trading off performance, thus achieving high productivity in parallel programming to harness the performance of hybrid shared/distributed memory architectures in high performance computing.
AbstractList	Unified Parallel C (UPC) is a parallel extension of ANSI C based on the Partitioned Global Address Space (PGAS) programming model, which provides a shared memory view that simplifies code development while it can take advantage of the scalability of distributed memory architectures. Therefore, UPC allows programmers to write parallel applications on hybrid shared/distributed memory architectures, such as multi-core clusters, in a more productive way, accessing remote memory by means of different high-level language constructs, such as assignments to shared variables or collective primitives. However, the standard UPC collectives library includes a reduced set of eight basic primitives with quite limited functionality. This work presents the design and implementation of extended UPC collective functions that overcome the limitations of the standard collectives library, allowing, for example, the use of a specific source and destination thread or defining the amount of data transferred by each particular thread. This library fulfills the demands made by the UPC developers community and implements portable algorithms, independent of the specific UPC compiler/runtime being used. The use of a representative set of these extended collectives has been evaluated using two applications and four kernels as case studies. The results obtained confirm the suitability of the new library to provide easier programming without trading off performance, thus achieving high productivity in parallel programming to harness the performance of hybrid shared/distributed memory architectures in high performance computing.[PUBLICATION ABSTRACT] Unified Parallel C (UPC) is a parallel extension of ANSI C based on the Partitioned Global Address Space (PGAS) programming model, which provides a shared memory view that simplifies code development while it can take advantage of the scalability of distributed memory architectures. Therefore, UPC allows programmers to write parallel applications on hybrid shared/distributed memory architectures, such as multi-core clusters, in a more productive way, accessing remote memory by means of different high-level language constructs, such as assignments to shared variables or collective primitives. However, the standard UPC collectives library includes a reduced set of eight basic primitives with quite limited functionality. This work presents the design and implementation of extended UPC collective functions that overcome the limitations of the standard collectives library, allowing, for example, the use of a specific source and destination thread or defining the amount of data transferred by each particular thread. This library fulfills the demands made by the UPC developers community and implements portable algorithms, independent of the specific UPC compiler/runtime being used. The use of a representative set of these extended collectives has been evaluated using two applications and four kernels as case studies. The results obtained confirm the suitability of the new library to provide easier programming without trading off performance, thus achieving high productivity in parallel programming to harness the performance of hybrid shared/distributed memory architectures in high performance computing. Unified Parallel C （UPC） is a parallel extension of ANSI C based on the Partitioned Global Address Space （PGAS） programming model, which provides a shared memory view that simplifies code development while it can take advantage of the scalability of distributed memory architectures. Therefore, UPC allows programmers to write parallel applications on hybrid shared/distributed memory architectures, such as multi-core clusters, in a more productive way, accessing remote memory by means of different high-level language constructs, such as assignments to shared variables or collective primitives. However, the standard UPC collectives library includes a reduced set of eight basic primitives with quite limited functionality. This work presents the design and implementation of extended UPC collective functions that overcome the limitations of the standard collectives library, allowing, for example, the use of a specific source and destination thread or defining the amount of data transferred by each particular thread. This library fulfills the demands made by the UPC developers community and implements portable algorithms, independent of the specific UPC compiler/runtime being used. The use of a representative set of these extended collectives has been evaluated using two applications and four kernels as case studies. The results obtained confirm the suitability of the new library to provide easier programming without trading off performance, thus achieving high productivity in parallel programming to harness the performance of hybrid shared/distributed memory architectures in high performance computing.
Author	Carlos Teijeiro Guillermo L. Taboada Juan Tourino Ramon Doallo Jose C. Mourino Damian A. Malldn Brian Wibecan
AuthorAffiliation	Computer Architecture Group, University of A Coruna, A Coruna 15071, Spain Galicia Supercomputing Center, Santiago de Compostela 15705, Spain Julich Supercomputing Centre, Institute for Advanced Simulation, Forschungszentrum Jiilich, Jiilich D-52425, Germany Industry Standard Servers Group, Hewlett-Packard Company, Montgomery, Alabama 36117, U.S.A.
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Cites_doi	10.1109/HPCC.2009.88 10.1109/ICPADS.2011.162 10.1016/j.parco.2011.05.006 10.1145/1787275.1787323 10.1109/71.642949 10.1007/978-3-642-03770-2_24 10.1016/j.future.2006.02.002 10.1145/1345206.1345224 10.1109/ICPP.2001.952082 10.1145/1629175.1629198 10.1109/IPDPS.2004.1303318
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Copyright	Springer Science+Business Media New York & Science Press, China 2013 Springer Science+Business Media New York & Science Press, China 2013. Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
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Notes	Unified Parallel C （UPC） is a parallel extension of ANSI C based on the Partitioned Global Address Space （PGAS） programming model, which provides a shared memory view that simplifies code development while it can take advantage of the scalability of distributed memory architectures. Therefore, UPC allows programmers to write parallel applications on hybrid shared/distributed memory architectures, such as multi-core clusters, in a more productive way, accessing remote memory by means of different high-level language constructs, such as assignments to shared variables or collective primitives. However, the standard UPC collectives library includes a reduced set of eight basic primitives with quite limited functionality. This work presents the design and implementation of extended UPC collective functions that overcome the limitations of the standard collectives library, allowing, for example, the use of a specific source and destination thread or defining the amount of data transferred by each particular thread. This library fulfills the demands made by the UPC developers community and implements portable algorithms, independent of the specific UPC compiler/runtime being used. The use of a representative set of these extended collectives has been evaluated using two applications and four kernels as case studies. The results obtained confirm the suitability of the new library to provide easier programming without trading off performance, thus achieving high productivity in parallel programming to harness the performance of hybrid shared/distributed memory architectures in high performance computing. 11-2296/TP Unified Parallel C, collective operation, programmability, partitioned global address space, high performance computing ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
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Publisher	Springer US Springer Nature B.V Computer Architecture Group, University of A Coru(n)a, A Coru(n)a 15071, Spain%Galicia Supercomputing Center, Santiago de Compostela 15705, Spain%Jülich Supercomputing Centre, Institute for Advanced Simulation, Forschungszentrum Jülich, Jülich D-52425, Germany%Industry Standard Servers Group, Hewlett-Packard Company, Montgomery, Alabama 36117, U.S.A
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References_xml	– reference: NishtalaRZhengYHargrovePYelickKTuning collective communication for Partitioned Global Address Space programming modelsParallel Computing201137957659110.1016/j.parco.2011.05.006 – reference: Dinan J, Balaji P, Lusk E L et al. Hybrid parallel programming with MPI and unified parallel C. In Proc. the 7th Int. Conf. Computing Frontiers, May 2010, pp.177-186. – reference: Teijeiro C, Taboada G L, Touriño J, Doallo R. Design and implementation of MapReduce using the PGAS programming model with UPC. In Proc. the 17th International Conference on Parallel and Distributed Systems, Dec. 2011, pp.196-203. – reference: Zhang Z, Seidel S. Benchmark measurements of current UPC platforms. In Proc. the 19th Int. Parallel and Distributed Processing Symposium, Apr. 2005. – reference: DeanJGhemawatSMapReduce: A flexible data processing toolCommunications of the ACM2010531727710.1145/1629175.1629198 – reference: Mallón D A, Taboada G L, Teijeiro C, Touriño J, Fraguela B B, Gómez A, Doallo R, Mouriño J C. Performance evaluation of MPI, UPC and OpenMP on multicore architectures. In Proc. the 16th European PVM/MPI Users' Group Meeting, Sept. 2009, pp.174-184. – reference: SalamaRASamehAPotential performance improvement of collective operations in UPCAdvances in Parallel Computing200815413422 – reference: El-Ghazawi T, Chauvin S. UPC benchmarking issues. In Proc. the 30th Int. Conference on Parallel Processing, Sept. 2001, pp.365-372. – reference: BruckJHoCTKipnisSUpfalEWeathersbyDEfficient algorithms for all-to-all communications in multiport message-passing systemsIEEE Transactions on Parallel and Distributed Systems19978111143115610.1109/71.642949 – reference: El-GhazawiTCantonnetFYaoYAnnareddySMohamedASBenchmarking parallel compilers: A UPC case studyFuture Generation Computer Systems200622776477510.1016/j.future.2006.02.002 – reference: Cantonnet F, Yao Y, Zahran M M et al. Productivity analysis of the UPC language. In Proc. the 18th Int. Parallel and Distributed Processing Symposium, Apr. 2004, pp.254. – reference: Taboada G L, Teijeiro C, Touriño J et al. Performance evaluation of unified parallel C collective communications. In Proc. the 11th IEEE Int. Conf. High Performance Computing and Communications, Jun. 2009, pp.69-78. – reference: Nishtala R, Almási G, Caşcaval C. Performance without pain = productivity: Data layout and collective communication in UPC. In Proc. the 13thACM SIGPLAN Symp. Principles and Practice of Parallel Programming, Feb. 2008, pp.99-110. – ident: 1313_CR2 doi: 10.1109/HPCC.2009.88 – ident: 1313_CR13 doi: 10.1109/ICPADS.2011.162 – volume: 37 start-page: 576 issue: 9 year: 2011 ident: 1313_CR6 publication-title: Parallel Computing doi: 10.1016/j.parco.2011.05.006 – ident: 1313_CR11 – ident: 1313_CR8 doi: 10.1145/1787275.1787323 – volume: 8 start-page: 1143 issue: 11 year: 1997 ident: 1313_CR7 publication-title: IEEE Transactions on Parallel and Distributed Systems doi: 10.1109/71.642949 – volume: 15 start-page: 413 year: 2008 ident: 1313_CR3 publication-title: Advances in Parallel Computing – ident: 1313_CR10 doi: 10.1007/978-3-642-03770-2_24 – volume: 22 start-page: 764 issue: 7 year: 2006 ident: 1313_CR9 publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2006.02.002 – ident: 1313_CR5 doi: 10.1145/1345206.1345224 – ident: 1313_CR1 doi: 10.1109/ICPP.2001.952082 – volume: 53 start-page: 72 issue: 1 year: 2010 ident: 1313_CR12 publication-title: Communications of the ACM doi: 10.1145/1629175.1629198 – ident: 1313_CR4 doi: 10.1109/IPDPS.2004.1303318
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Snippet	Unified Parallel C （UPC） is a parallel extension of ANSI C based on the Partitioned Global Address Space （PGAS） programming model, which provides a shared... Unified Parallel C (UPC) is a parallel extension of ANSI C based on the Partitioned Global Address Space (PGAS) programming model, which provides a shared...
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SubjectTerms	Algorithms Analysis Artificial Intelligence C (programming language) Computer programming Computer Science Data Structures and Information Theory Distributed memory Fourier transforms High performance computing Information Systems Applications (incl.Internet) Libraries Mathematical analysis Mathematical models Parallel processing Programming Proposals Regular Paper Software Software Engineering Studies Theory of Computation Variables 共享内存内存架构并行编程应用程序性能计算编程模型设计集体
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Title	Design and Implementation of an Extended Collectives Library for Unified Parallel C
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