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...

Full description

Saved in:
Bibliographic Details
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ark:/67375/WNG-582PK338-C
ArticleID:CPE1549
istex:9735F0C678FECAD71B518D5CA8FBA5AEABBFBB01
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1532-0626
1532-0634
1532-0634
DOI:10.1002/cpe.1549