Parallel Haskell implementations of the N-body problem
SUMMARYThis paper provides an assessment of high‐level parallel programming models for multi‐core programming by implementing two versions of the n‐body problem. We compare three different parallel programming models on the basis of parallel Haskell, differing in the ways how potential parallelism i...
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| Veröffentlicht in: | Concurrency and computation Jg. 26; H. 4; S. 987 - 1019 |
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25.03.2014
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| Abstract | SUMMARYThis paper provides an assessment of high‐level parallel programming models for multi‐core programming by implementing two versions of the n‐body problem. We compare three different parallel programming models on the basis of parallel Haskell, differing in the ways how potential parallelism is identified and managed. We assess the performance of each implementation, discuss the sequential and parallel tuning steps leading to the final versions and draw general conclusions on the suitability of high‐level parallel programming models for multi‐core programming. We achieve speed‐ups of up to 7.2 for the all‐pairs algorithm and up to 6.5 for the Barnes–Hut algorithm on an 8‐core machine. Copyright © 2013 John Wiley & Sons, Ltd. |
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| AbstractList | This paper provides an assessment of high-level parallel programming models for multi-core programming by implementing two versions of the n-body problem. We compare three different parallel programming models on the basis of parallel Haskell, differing in the ways how potential parallelism is identified and managed. We assess the performance of each implementation, discuss the sequential and parallel tuning steps leading to the final versions and draw general conclusions on the suitability of high-level parallel programming models for multi-core programming. We achieve speed-ups of up to 7.2 for the all-pairs algorithm and up to 6.5 for the Barnes-Hut algorithm on an 8-core machine. Copyright copyright 2013 John Wiley & Sons, Ltd. SUMMARYThis paper provides an assessment of high‐level parallel programming models for multi‐core programming by implementing two versions of the n‐body problem. We compare three different parallel programming models on the basis of parallel Haskell, differing in the ways how potential parallelism is identified and managed. We assess the performance of each implementation, discuss the sequential and parallel tuning steps leading to the final versions and draw general conclusions on the suitability of high‐level parallel programming models for multi‐core programming. We achieve speed‐ups of up to 7.2 for the all‐pairs algorithm and up to 6.5 for the Barnes–Hut algorithm on an 8‐core machine. Copyright © 2013 John Wiley & Sons, Ltd. This paper provides an assessment of high‐level parallel programming models for multi‐core programming by implementing two versions of the n ‐body problem. We compare three different parallel programming models on the basis of parallel Haskell, differing in the ways how potential parallelism is identified and managed. We assess the performance of each implementation, discuss the sequential and parallel tuning steps leading to the final versions and draw general conclusions on the suitability of high‐level parallel programming models for multi‐core programming. We achieve speed‐ups of up to 7.2 for the all‐pairs algorithm and up to 6.5 for the Barnes–Hut algorithm on an 8‐core machine. Copyright © 2013 John Wiley & Sons, Ltd. |
| Author | Totoo, Prabhat Loidl, Hans-Wolfgang |
| Author_xml | – sequence: 1 givenname: Prabhat surname: Totoo fullname: Totoo, Prabhat email: Correspondence to: Prabhat Totoo, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, UK., pt114@hw.ac.uk organization: School of Mathematical and Computer Sciences, Heriot-Watt University, EH14 4AS, Edinburgh, UK – sequence: 2 givenname: Hans-Wolfgang surname: Loidl fullname: Loidl, Hans-Wolfgang organization: School of Mathematical and Computer Sciences, Heriot-Watt University, EH14 4AS, Edinburgh, UK |
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| Cites_doi | 10.1145/1327452.1327492 10.1145/2364474.2364483 10.1017/S0956796810000201 10.1109/PDP.2010.26 10.1017/S0956796802004458 10.1145/74818.74825 10.1006/jpdc.1996.0106 10.1023/A:1025641323400 10.1145/1094811.1094852 10.1145/1863523.1863535 10.1145/1596550.1596588 10.1017/S0956796897002967 10.1002/(SICI)1096-9128(199910)11:12<701::AID-CPE443>3.0.CO;2-P 10.1145/1248648.1248656 10.1007/3‐540‐45361‐X 10.1145/165854.165874 10.1145/289918.289920 10.1145/2034675.2034690 10.1002/spe.v40:20 10.1002/0471478369 10.1145/1708046.1708058 10.1145/2034675.2034685 10.1007/798‐3‐540‐89330‐1_10 10.1145/237721.237794 10.1017/S0956796805005526 10.1145/1596550.1596563 10.1038/324446a0 10.1109/71.86103 10.1145/277650.277725 10.1006/jpdc.1993.1070 10.1007/11549468_83 10.1007/3‐540‐44681‐8_76 10.1145/1272996.1273005 10.1145/231379.231392 10.1145/1708046.1708048 10.1177/1094342007078442 10.1002/cpe.4330070305 |
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| References_xml | – reference: Fluet M, Rainey M, Reppy J, Shaw A. Implicitly-threaded parallelism in manticore. Journal of Functional Programming 2010; 20(5-6):537-576. DOI: 10.1017/S0956796810000201. – reference: Loogen R, Ortega-Mallén Y, Peña Marí R. Parallel functional programming in Eden. Journal of Functional Programming 2005; 15:431-475. DOI: 10.1017/S0956796805005526. – reference: Barnes J, Hut P. A hierarchical O(N log N) force-calculation algorithm. Nature 1986December; 324:446-449. DOI: 10.1038/324446a0. – reference: Scholz SB. Single assignment C - efficient support for high-level array operations in a functional setting. Journal of Functional Programming 2003; 13(6):1005-1059. DOI: 10.1017/S0956796802004458. http://www.sac-home.org/publications/sac-overview-jfp-03.pdf. – reference: Lowenthal DK, Andrews VWFGR. Using fine-grain threads and run-time decision making in parallel computing. Journal of Parallel and Distributed Computing 1996; 37(1). DOI: 10.1006/jpdc.1996.0106. Special Issue on Multithreading for Multiprocessors. – reference: Loidl HW, Rubio F, Scaife N, Hammond K, Horiguchi S, Klusik U, Loogen R, Michaelson GJ, Peña R, Priebe S, Rebón ÁJ, Trinder PW. Comparing parallel functional languages: programming and performance. Higher Order Symbolic Computer 2003; 16:203-251. DOI: 10.1023/A:1025641323400. http://www.macs.hw.ac.uk/~dsg/gph/papers/ps/HOSC03.ps.gz. – reference: Mattson TG, Sanders BA, Massingill BL. Patterns for Parallel Programming. Addison-Wesley, 2004. ISBN 978-0321228116. – reference: Chamberlain BL, Callahan D, Zima HP. Parallel programmability and the Chapel language. International Journal of High Performance Computing Applications 2007; 21(3):291-312. DOI: 10.1177/1094342007078442. http://www.highproductivity.org/IJHPCA-HPPLM.html. – reference: Bacci B, Danelutto M, Orlando S, Pelagatti S, Vanneschi M. P 3L: a Structured high-level programming language and its structured support. Concurrency and Computation: Practice and Experience 1995; 7(3):225-255. DOI: 10.1002/cpe.4330070305. – reference: Cole M. Algorithmic skeletons: structural management of parallel computation. Pitman/MIT Press, 1989. – reference: Dean J, Ghemawat S. MapReduce: simplified data processing on large clusters. Communications ACM 2008; 51(1):107-113. DOI: 10.1145/1327452.1327492. – reference: Loidl HW, Trinder P, Hammond K, Junaidu SB, Morgan R, Peyton Jones SL. Engineering parallel symbolic programs in GpH. Concurrency and Computation: Practice and Experience 1999; 11:701-752. DOI: 10.1.1.49.2354. http://www.macs.hw.ac.uk/~dsg/gph/papers/abstracts/cpe.html. – reference: Culler D, Goldstein S, Schauser K, von Eicken T. TAM - a compiler controlled threaded abstract machine. Journal of Parallel and Distributed Computing 1993; 18:347-370. DOI: 10.1006/jpdc.1993.1070. http://www.cs.cmu.edu/~seth/papers/CullerGSvE93.pdf. – reference: Nikhil R, Arvind. Implicit Parallel Programming in pH. Morgan Kaufmann, 2001. ISBN 978-1558606449. – reference: Trinder PW, Hammond K, Loidl. HW. Encyclopedia of Parallel Computing. Springer Verlag, 2011. ISBN 978-0-387-09844-9. – reference: Reinders J. Intel threading building blocks: outfitting C++ for multi-core processor parallelism. O'Reilly, 2007. – reference: Campbell C, Johnson R, Miller A, Toub S. Parallel programming with Microsoft .NET - Design patterns for decomposition and coordination on multicore architectures. Microsoft Press, 2010. http://msdn.microsoft.com/en-us/library/ff963553.aspx. – reference: Numrich RW, Reid J. Co-array Fortran for parallel programming. SIGPLAN Fortran Forum 1998 August; 17:1-31. DOI: 10.1145/289918.289920. – reference: Gonzalez-Velez H, Leyton M. A survey of algorithmic skeleton frameworks: high-level structured parallel programming enablers. Software: Practice and Experience November 2010; 40(12):1135-1160. DOI: 10.1002/spe.v40:20. – reference: Trinder PW, Hammond K, Loidl HW, Peyton Jones SL. Algorithm + strategy = parallelism. Journal of Functional Programming 1998; 8:23-60. DOI: 10.1017/S0956796897002967. http://www.macs.hw.ac.uk/~dsg/gph/papers/abstracts/strategies.html. – reference: Mohr E, Kranz D, Halstead Jr R. Lazy task creation: a technique for increasing the granularity of parallel programs. IEEE Transactions on Parallel and Distributed Systems 1991; 2(3):264-280. DOI: 10.1109/71.86103. – reference: El-Ghazawi T, Carlson W, Sterling T, Yelick K. UPC: distributed shared memory programming. John Wiley and Sons, 2005. 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| Snippet | SUMMARYThis paper provides an assessment of high‐level parallel programming models for multi‐core programming by implementing two versions of the n‐body... This paper provides an assessment of high‐level parallel programming models for multi‐core programming by implementing two versions of the n ‐body problem. We... This paper provides an assessment of high-level parallel programming models for multi-core programming by implementing two versions of the n-body problem. We... |
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| SubjectTerms | Algorithms Assessments Computation Concurrency functional programming high-level parallel programming models Mathematical models n-body problem Parallel programming Programming Tuning |
| Title | Parallel Haskell implementations of the N-body problem |
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