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

González-Vélez, Horacio and Cole, Murray (2010) Adaptive structured parallelism for distributed heterogeneous architectures: a methodological approach with pipelines and farms. Concurrency and Computation: Practice and Experience, 22 (15). pp. 2073-2094. ISSN 1532-0626

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Abstract

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.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Caoimhe Ní Mhaicín
Date Deposited:	27 Feb 2014 17:36
Last Modified:	11 Jun 2014 16:13
URI:	https://norma.ncirl.ie/id/eprint/939

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