A quantitative comparison of parallel computation models

• Published in: ACM transactions on computer systems 1998-08, Vol.16 (3), p.271-318
• Main Authors: Juurlink, Ben H. H., Wijshoff, Harry A. G.
• Format: Article
• Language: English
• Subjects: Applied sciences; Architectures; Computer engineering; Computer programming; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Computer systems organization; Computing methodologies; Exact sciences and technology; General programming languages; Innovations; Language types; Methods; Model development and analysis; Modeling and simulation; Modeling methodologies; Parallel architectures; Parallel computing methodologies; Parallel programming languages; Software; Software and its engineering; Software notations and tools
• ISSN: 0734-2071; 1557-7333
• DOI: 10.1145/290409.290412

In recent years, a large number of parallel computation models have been proposed to replace the PRAM as the parallel computation model presented to the algorithm designer. Although mostly the theoretical justifications for these models are sound, and many algorithmic results where obtained through these models, little experimentation has been conducted to validate the effectiveness of these models for developing cost-effective algorithms and applications on existing hardware platforms. In this article a first attempt is made to perform a detailed experimental account on the preciseness of these models. The achieve this, three models (BSP, E-BSP, and BPRAM) were selected and validated on five parallel platforms (Cray T3E, Thinking Machines CM-5, Intel Paragon, MasPar MP-1, and Parsytec GCel). The work described in this article consists of three parts. First, the predictive capabilities of the models are investigated. Unlike previous experimental work, which mostly demonstrated a close match between the measuredd and predicted execution times, this article shows that there are several situations in which the models do not precisely predict the actual runtime behavior of an algorithm implementation. Second, a comparison between the models is provided in order to determine the model that induces that most efficient algorithms. Lastly, the performance achieved by the model-derived algorithms is compared with the performace attained by machine-specific algorithms in order to examine the effectiveness of deriving fast algorithms through the formalisms of the models.