Three non conventional paradigms of parallel computation
We consider three paradigms of computation where the benefits of a parallel solution are greater than usual. Paradigm 1 works on a timevarying input data set, whose size increases with time. In paradigm 2 the data set is fixed, but the processors may fail at any time with a given constant probability. In paradigm 3, the execution of a single operation may require more than one processor, for security or reliability reasons. We discuss the organization of PRAM algorithms for these paradigms, and prove new bounds on parallel speed-up.
KeywordsParallel Algorithm Single Operation Constant Probability Parallel Solution Reliability Reason
Unable to display preview. Download preview PDF.
- A. Aggarwal and A.K. Chandra. Communication Complexity of PRAMs. Proc. 15th Int. Colloquium on Automata, Languages and Programming (1988) 1–18.Google Scholar
- S.G. Akl, M. Cosnard and A.G. Ferreira. Data-movement-intensive problems: two folk theorems in parallel computation revisited. Theoretical Computer Science 95 (1992) 323–337.Google Scholar
- M.R. Garey and D.S. Johnson. Computers and Intractability. Freeman, San Francisco, 1978.Google Scholar
- P.C. Kanellakis and A.A. Shvartsman. Efficient parallel algorithms can be made robust. In Proc. 8th Annual ACM Symp. on Principles of Distributed Computing (1989) 211–222.Google Scholar
- R.M. Karp and V. Ramachandran A survey of parallel algorithms for shared memory machines. In Handbook of Theoretical Computer Science North Holland, New York NY (1990) 869–941.Google Scholar
- Z.M. Kedem, K.V. Palem and P.G. Spirakis. Efficient robust parallel computations. In Proc. 22nd Annual ACM Symp. on Theory of Computing (1990) 590–599.Google Scholar
- F. Luccio and L. Pagli. The p-shovelers problem. (Computing with time-varying data). SIGACT News 23, 2 (1992) 72–75Google Scholar
- C. Martel, R. Subramonian, A. Park. Asynchronous PRAMs are (almost) as good as synchronous prams. In Proc. 31st Symp. on Foundations of Computer Science (1990) 590–599.Google Scholar
- W. Paul. On line simulation of k+1 tapes by k tapes requires nonlinear time. Information and Control 53 (1982) 1–8.Google Scholar
- K.S. Trivedi. Probability and statistics with reliability, queueing, and computer science applications. Prentice-Hall, Englewood Cliffs NJ (1982).Google Scholar
- U. Vishkin. Can parallel algorithms enhance serial implementation?. SIGACT News 22, 4 (1991) 63.Google Scholar