Adaptive structured parallelism for distributed heterogeneous architectures: a methodological approach with pipelines and farms

Algorithmic skeletons commonly used patterns of parallel computation, communication, and interaction. Based on the algorithmic skeleton concept, structured parallelism provides a high‐level parallel programming technique that allows the conceptual description of parallel programs while fostering pla...

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Published in:	Concurrency and computation Vol. 22; no. 15; pp. 2073 - 2094
Main Authors:	González-Vélez, Horacio, Cole, Murray
Format:	Journal Article
Language:	English
Published:	Chichester, UK John Wiley & Sons, Ltd 01.10.2010
Subjects:	Adaptive structures algorithmic skeletons Algorithms cluster computing Computation Computer networks concurrent programming structures Farms Methodology parallel patterns Parallel programming Programming structured parallelism
ISSN:	1532-0626, 1532-0634, 1532-0634
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Abstract	Algorithmic skeletons commonly used patterns of parallel computation, communication, and interaction. Based on the algorithmic skeleton concept, structured parallelism provides a high‐level parallel programming technique that allows the conceptual description of parallel programs while fostering platform independence and algorithm ion. This work presents a methodology to improve skeletal parallel programming in heterogeneous distributed systems by introducing adaptivity through resource awareness. As we hypothesise that a skeletal program should be able to adapt to the dynamic resource conditions over time using its structural forecasting information, we have developed adaptive structured parallelism (ASPARA). ASPARA is a generic methodology to incorporate structural information at compilation into a parallel program, which will help it to adapt at execution. ASPARA comprises four phases: programming, compilation, calibration, and execution. We illustrate the feasibility of this approach and its associated performance improvements using independent case studies based on two algorithmic skeletons—the task farm and the pipeline—evaluated in a non‐dedicated heterogeneous multi‐cluster system. Copyright © 2010 John Wiley & Sons, Ltd.
AbstractList	Algorithmic skeletons commonly used patterns of parallel computation, communication, and interaction. Based on the algorithmic skeleton concept, structured parallelism provides a high‐level parallel programming technique that allows the conceptual description of parallel programs while fostering platform independence and algorithm ion. This work presents a methodology to improve skeletal parallel programming in heterogeneous distributed systems by introducing adaptivity through resource awareness. As we hypothesise that a skeletal program should be able to adapt to the dynamic resource conditions over time using its structural forecasting information, we have developed adaptive structured parallelism (ASPARA). ASPARA is a generic methodology to incorporate structural information at compilation into a parallel program, which will help it to adapt at execution. ASPARA comprises four phases: programming, compilation, calibration, and execution. We illustrate the feasibility of this approach and its associated performance improvements using independent case studies based on two algorithmic skeletons—the task farm and the pipeline—evaluated in a non‐dedicated heterogeneous multi‐cluster system. Copyright © 2010 John Wiley & Sons, Ltd. Algorithmic skeletons abstract commonly used patterns of parallel computation, communication, and interaction. Based on the algorithmic skeleton concept, structured parallelism provides a high-level parallel programming technique that allows the conceptual description of parallel programs while fostering platform independence and algorithm abstraction. This work presents a methodology to improve skeletal parallel programming in heterogeneous distributed systems by introducing adaptivity through resource awareness. As we hypothesise that a skeletal program should be able to adapt to the dynamic resource conditions over time using its structural forecasting information, we have developed adaptive structured parallelism (ASPARA). ASPARA is a generic methodology to incorporate structural information at compilation into a parallel program, which will help it to adapt at execution. ASPARA comprises four phases: programming, compilation, calibration, and execution. We illustrate the feasibility of this approach and its associated performance improvements using independent case studies based on two algorithmic skeletons-the task farm and the pipeline-evaluated in a non-dedicated heterogeneous multi-cluster system. Algorithmic skeletons abstract commonly used patterns of parallel computation, communication, and interaction. Based on the algorithmic skeleton concept, structured parallelism provides a high‐level parallel programming technique that allows the conceptual description of parallel programs while fostering platform independence and algorithm abstraction. This work presents a methodology to improve skeletal parallel programming in heterogeneous distributed systems by introducing adaptivity through resource awareness. As we hypothesise that a skeletal program should be able to adapt to the dynamic resource conditions over time using its structural forecasting information, we have developed adaptive structured parallelism (ASP ARA ). ASP ARA is a generic methodology to incorporate structural information at compilation into a parallel program, which will help it to adapt at execution. ASP ARA comprises four phases: programming, compilation, calibration, and execution. We illustrate the feasibility of this approach and its associated performance improvements using independent case studies based on two algorithmic skeletons—the task farm and the pipeline—evaluated in a non‐dedicated heterogeneous multi‐cluster system. Copyright © 2010 John Wiley & Sons, Ltd.
Author	Cole, Murray González-Vélez, Horacio
Author_xml	– sequence: 1 givenname: Horacio surname: González-Vélez fullname: González-Vélez, Horacio email: h.gonzalez-velez@rgu.ac.uk organization: School of Computing and IDEAS Research Institute, Robert Gordon University, Aberdeen, U.K – sequence: 2 givenname: Murray surname: Cole fullname: Cole, Murray organization: School of Informatics, University of Edinburgh, Edinburgh, U.K
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References_xml	– reference: Benoit A, Robert Y. Mapping pipeline skeletons onto heterogeneous platforms. Journal of Parallel and Distributed Computing 2008; 68(6):790-808. DOI: 10.1016/j.jpdc.2007.11.004. – reference: Alba E, Almeida F, Blesa M, Cotta C, Díaz M, Dorta I, Gabarró J, León C, Luque G, Petit J, Rodriguez C, Rojas A, Xhafa F. Efficient parallel LAN/WAN algorithms for optimization. The MALLBA project. Parallel Computing 2006; 32(5-6):415-440. DOI: 10.1016/j.parco.2006.06.007. – reference: Bharadwaj V, Ghose D, Mani V, Robertazzi TG. Scheduling Divisible Loads in Parallel and Distributed Systems. IEEE: Los Alamitos, 1996. – reference: Danelutto M. Irregularity handling via structured parallel programming. International Journal of Computational Science and Engineering 2005; 1(2-4):73-85. DOI: 10.1504/IJCSE.2005.009693. – reference: Majumdar S, Eager DL, Bunt RB. Scheduling in multiprogrammed parallel systems. SIGMETRICS Performance Evaluation Review 1988; 16(1):104-113. 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A taxonomy of scientific workflow systems for grid computing. SIGMOD Record 2005; 34(3):44-49. DOI: 10.1145/1084805.1084814. – reference: Dongarra J, Gannon D, Fox G, Kennedy K. The impact of multicore on computational science software. CTWatch Quarterly 2007; 3(1):3-10. – reference: Thain D, Tannenbaum T, Livny M. Distributed computing in practice: The Condor experience. Concurrency and Computation: Practice and Experience 2005; 17(2-4):323-356. DOI: 10.1002/cpe.v17:2/4. – reference: Cunha JC, Rana OF, Medeiros PD. Future trends in distributed applications and problem-solving environments. Future Generation Computer Systems 2005; 21(6):843-855. DOI: 10.1016/j.future.2003.12.015. – reference: Skillicorn DB, Talia D. Models sand languages for parallel computation. ACM Computing Surveys 1998; 30(2):123-169. DOI: 10.1145/280277.280278. – reference: Brinch Hansen P. Model programs for computational science: A programming methodology for multicomputers. 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DOI: 10.1093/comjnl/19.1.43. – reference: Drozdowski M, Lawenda M. Multi-installment divisible load processing in heterogeneous distributed systems. Concurrency and Computation: Practice and Experience 2007; 19(17):2237-2253. DOI: 10.1002/cpe.1180. – reference: González-Vélez V, González-Vélez H. Parallel stochastic simulation of macroscopic calcium currents. Journal of Bioinformatics and Computational Biology 2007; 5(3):755-772. DOI: 10.1142/S0219720007002679. – reference: González-Vélez H, Cole M. Adaptive statistical scheduling of divisible workloads in heterogeneous systems. Journal of Scheduling 2009; DOI: 10.1007/s10951-009-0138-4. – reference: Van Der Aalst WMP, Ter Hofstede AHM, Kiepuszewski B, Barros AP. Workflow patterns. Distributed and Parallel Databases 2003; 14(1):5-51. DOI: 10.1023/A:1022883727209. – reference: Hagerup T. Allocating independent tasks to parallel processors: An experimental study. Journal of Parallel and Distributed Computing 1997; 47(2):185-197. 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Snippet	Algorithmic skeletons commonly used patterns of parallel computation, communication, and interaction. Based on the algorithmic skeleton concept, structured... Algorithmic skeletons abstract commonly used patterns of parallel computation, communication, and interaction. Based on the algorithmic skeleton concept,...
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SubjectTerms	Adaptive structures algorithmic skeletons Algorithms cluster computing Computation Computer networks concurrent programming structures Farms Methodology parallel patterns Parallel programming Programming structured parallelism
Title	Adaptive structured parallelism for distributed heterogeneous architectures: a methodological approach with pipelines and farms
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