Design strategies for irregularly adapting parallel applications

Conference · Wed Nov 01 04:00:00 EST 2000

OSTI ID:787124

Oliker, Leonid ^[1]; Biswas, Rupak; Shan, Hongzhang; Sing, Jaswinder Pal

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1. LBNL Library

Achieving scalable performance for dynamic irregular applications is eminently challenging. Traditional message-passing approaches have been making steady progress towards this goal; however, they suffer from complex implementation requirements. The use of a global address space greatly simplifies the programming task, but can degrade the performance of dynamically adapting computations. In this work, we examine two major classes of adaptive applications, under five competing programming methodologies and four leading parallel architectures. Results indicate that it is possible to achieve message-passing performance using shared-memory programming techniques by carefully following the same high level strategies. Adaptive applications have computational work loads and communication patterns which change unpredictably at runtime, requiring dynamic load balancing to achieve scalable performance on parallel machines. Efficient parallel implementations of such adaptive applications are therefore a challenging task. This work examines the implementation of two typical adaptive applications, Dynamic Remeshing and N-Body, across various programming paradigms and architectural platforms. We compare several critical factors of the parallel code development, including performance, programmability, scalability, algorithmic development, and portability.

• Research Organization: Lawrence Berkeley National Lab., CA (US)
• Sponsoring Organization: USDOE Director, Office of Science. Office of Advanced Scientific Computing Research. Mathematical, Information, and Computational Sciences Division (US)
• DOE Contract Number: AC03-76SF00098
• OSTI ID: 787124
• Report Number(s): LBNL--47804
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
COMMUNICATIONS
DESIGN
DYNAMIC LOADS
IMPLEMENTATION
PARALLEL PROCESSING
PERFORMANCE
PROGRAMMING