A general and efficient divide-and-conquer algorithm framework for multi-core clusters
Divide-and-conquer is one of the most important patterns of parallelism, being applicable to a large variety of problems. In addition, the most powerful parallel systems available nowadays are computer clusters composed of distributed-memory nodes that contain an increasing number of cores that shar...
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| Vydané v: | Cluster computing Ročník 20; číslo 3; s. 2605 - 2626 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Springer US
01.09.2017
Springer Nature B.V |
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| ISSN: | 1386-7857, 1573-7543 |
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| Abstract | Divide-and-conquer is one of the most important patterns of parallelism, being applicable to a large variety of problems. In addition, the most powerful parallel systems available nowadays are computer clusters composed of distributed-memory nodes that contain an increasing number of cores that share a common memory. The optimal exploitation of these systems often requires resorting to a hybrid model that mimics the underlying hardware by combining a distributed and a shared memory parallel programming model. This results in longer development times and increased maintenance costs. In this paper we present a very general skeleton library that allows to parallelize any divide-and-conquer problem in hybrid distributed-shared memory systems with little effort while providing much flexibility and good performance. Our proposal combines a message-passing paradigm at the process level and a threaded model inside each process, hiding the related complexity from the user. The evaluation shows that this skeleton provides performance comparable, and often better than that of manually optimized codes while requiring considerably less effort when parallelizing applications on multi-core clusters. |
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| AbstractList | Divide-and-conquer is one of the most important patterns of parallelism, being applicable to a large variety of problems. In addition, the most powerful parallel systems available nowadays are computer clusters composed of distributed-memory nodes that contain an increasing number of cores that share a common memory. The optimal exploitation of these systems often requires resorting to a hybrid model that mimics the underlying hardware by combining a distributed and a shared memory parallel programming model. This results in longer development times and increased maintenance costs. In this paper we present a very general skeleton library that allows to parallelize any divide-and-conquer problem in hybrid distributed-shared memory systems with little effort while providing much flexibility and good performance. Our proposal combines a message-passing paradigm at the process level and a threaded model inside each process, hiding the related complexity from the user. The evaluation shows that this skeleton provides performance comparable, and often better than that of manually optimized codes while requiring considerably less effort when parallelizing applications on multi-core clusters. |
| Author | Fraguela, Basilio B. González, Carlos H. |
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| Cites_doi | 10.1145/1709093.1709096 10.1145/201059.201065 10.1007/s10766-010-0140-7 10.1038/324446a0 10.1145/1809961.1809975 10.1109/HPCC.2010.24 10.1006/jpdc.1996.0107 10.1002/cpe.2831 10.1007/s10766-013-0268-3 10.1016/S0167-739X(02)00172-3 10.1002/cpe.3511 10.1145/3002125.3002128 10.1007/978-3-642-03770-2_24 10.1016/j.parco.2013.04.012 10.1007/3-540-45706-2_86 10.1109/SFFCS.1999.814600 10.1109/SBAC-PAD.2013.28 10.1145/1055531.1055532 10.1109/TC.1983.1676280 10.1145/1278177.1278183 10.1109/TSE.1976.233837 10.1109/ICPP.2009.18 10.1145/5666.5673 10.1145/1327452.1327492 10.1137/120876605 10.1109/PDP.2015.78 10.1016/j.parco.2006.06.001 10.1016/j.jocs.2015.05.002 10.1109/ISPASS.2009.4919639 10.1109/HPCC.2010.23 10.1109/IPDPS.2015.38 10.1109/PDP.2010.26 10.1109/PDP.2015.81 10.1016/j.parco.2003.12.002 |
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| Keywords | Divide-and-conquer High performance computing Hybrid parallelism Algorithmic skeletons Template metaprogramming Multi-core clusters |
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| References_xml | – reference: Lima, J.V.F., Broquedis, F., Gautier, T., Raffin, B.: Preliminary experiments with xKaapi on Intel Xeon Phi coprocessor. In Proceedings of 25th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD 2013), pp. 105–112. Los Alamitos, CA (2013) – reference: Denis, A.: pioman: A pthread-based multithreaded communication engine. In Proceedings of 23rd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2015), pp. 155–162. Los Alamitos, CA (2015) – reference: TejedorEFarrerasMGroveDBadiaRMAlmasiGLabartaJA high-productivity task-based programming model for clustersConcurr. Comput. Pract. Exp.201224182421244810.1002/cpe.2831 – reference: Gregor, D., Troyer, M.: Boost.MPI. http://boost.cowic.de/rc/pdf/mpi.pdf (2007) – reference: Tousimojarad, A., Vanderbauwhede, W.: Number of tasks, not threads, is key. 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