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

H. Gonzalez-Velez, M. Cole

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)2073-2094
Number of pages22
JournalConcurrency and Computation: Practice and Experience
Volume22
Issue number15
Early online date22 Jan 2010
DOIs
Publication statusPublished - Oct 2010

Keywords

  • structured parallelism
  • algorithmic skeletons
  • parallel patterns
  • parallel programming
  • concurrent programming structures
  • cluster computing

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