Paper Info
Title
Distributed parallel computing using navigational programming
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.
Year
DOI
Venue
2004
10.1023/B:IJPP.0000015563.36375.17
International Journal of Parallel Programming
Keywords
Field
DocType
distributed shared memory,navigational programming,parallel computing,message passing,performance analysis,general-purpose parallel,distributed parallel computing,variable programming,performance scalability,navigational program- ming,parallel cholesky factorization,jacobi iteration,in- cremental parallelization,common approach,navp approach,cholesky factorization
Shared variables,Jacobi method,Computer science,Parallel computing,Theoretical computer science,Distributed shared memory,Message passing,Scalability,Cholesky decomposition
Journal
Volume
Issue
ISSN
32
1
1573-7640
Citations 
PageRank 
References 
7
0.51
13
Authors
6
Name
Order
Citations
PageRank
Lei Pan1299.49
Ming Kin Lai2203.71
Koji Noguchi370.51
Javid J. Huseynov4122.13
Lubomir F. Bic514719.23
Michael B. Dillencourt649857.58