Distributed parallel computing using navigational programming

Authors:
Lei Pan;Ming Kin Lai;Koji Noguchi;Javid J. Huseynov;Lubomir F. Bic;Michael B. Dillencourt
Affiliations:
School of Information & Computer Science, University of California, Irvine, California;School of Information & Computer Science, University of California, Irvine, California;School of Information & Computer Science, University of California, Irvine, California;School of Information & Computer Science, University of California, Irvine, California;School of Information & Computer Science, University of California, Irvine, California;School of Information & Computer Science, University of California, Irvine, California
Venue:
International Journal of Parallel Programming
Year:
2004

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Abstract

Message Passing (MP) and Distributed Shared Memory (DSM) are the two most common approaches to distributed parallel computing. MP is difficult to use, whereas DSM is not scalable. Performance scalability and ease of programming can be achieved at the same time by using navigational programming (NavP). This approach combines the advantages of MP and DSM, and it balances convenience and flexibility. Similar to MP, NavP suggests to its programmers the principle of pivot-computes and hence is efficient and scalable. Like DSM, NavP supports incremental parallelization and shared variable programming and is therefore easy to use. The implementation and performance analysis of real-world algorithms, namely parallel Jacobi iteration and parallel Cholesky factorization, presented in this paper supports the claim that the NavP approach is better suited for general-purpose parallel distributed programming than either MP or DSM.