Skip navigation

Portability, predictability and performance for parallel computing: BSP in practice

Portability, predictability and performance for parallel computing: BSP in practice

Reed, Joy, Parrott, Kevin and Lanfear, Tim (1996) Portability, predictability and performance for parallel computing: BSP in practice. Concurrency: Practice and Experience, 8 (10). pp. 799-812. ISSN 1040-3108 (Print), 1096-9128 (Online) (doi:10.1002/(SICI)1096-9128(199612)8:10<799::AID-CPE274>3.0.CO;2-7)

Full text not available from this repository.

Abstract

We report on practical experience using the Oxford BSP Library to parallelize a large electromagnetic code, the British Aerospace finite-difference time-domain code EMMA T:FD3D. The Oxford BS Library is one of the first realizations of the Bulk Synchronous Parallel computational model to be targeted at numerically intensive scientific (typically Fortran) computing. The BAe EMMA code is one of the first large-scale applications to be parallelized using this library, and it is an important demonstration of the cost effectiveness of the BSP approach. We illustrate how BSP cost-modelling techniques can be used to predict and optimize performance for single-source programs across different parallel platforms. We provide predicted and observed performance figures for an industrial-strength, single-source parallel code for a variety of real parallel architectures: shared memory multiprocessors, workstation clusters and massively parallel platforms.

Item Type: Article
Uncontrolled Keywords: portability, predictability, performance, parallel computing
Subjects: Q Science > QA Mathematics
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Mechanics & Reliability Group
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Science & Engineering Group
School of Computing & Mathematical Sciences > Computer & Computational Science Research Group
School of Computing & Mathematical Sciences > Department of Computer Science
School of Computing & Mathematical Sciences > Department of Mathematical Sciences
Related URLs:
Last Modified: 14 Oct 2016 09:00
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/308

Actions (login required)

View Item View Item