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Coarse Grained Parallel Selection

    Several efficient, but non-optimal, solutions to the Selection Problem on coarse grained parallel computers have appeared in the literature. We consider the example of the Saukas-Song algorithm; we analyze it without expressing the running time in terms of communication rounds. This shows that while in the best case the Saukas-Song algorithm runs in asymptotically optimal time, in general it does not. We propose another algorithm for coarse grained selection that has optimal expected running time.

    References

    • 1. I. Al-Furiah, S. Aluru, S. Goil and S. Ranka, Practical algorithms for selection on coarse-grained parallel computers, IEEE Transactions on Parallel and Distributed Systems 8(8) (1997) 813–824. Crossref, ISIGoogle Scholar
    • 2. D. A. Bader, An improved, randomized algorithm for parallel selection with an experimental study, Journal of Parallel and Distributed Computing 64(9) (2004) 1051–1059. Crossref, ISIGoogle Scholar
    • 3. M. Blum, R. W. Floyd, V. Pratt, R. L. Rivest and R. E. Tarjan, Bounds for selection, Journal of Computer and System Sciences 7 (1973) 448–461. CrossrefGoogle Scholar
    • 4. L. Boxer and R. Miller, Coarse grained gather and scatter operations with applications, Journal of Parallel and Distributed Computing 64(11) (2004) 1297–1310. Crossref, ISIGoogle Scholar
    • 5. L. Boxer, R. Miller and A. Rau-Chaplin, Scalable parallel algorithms for geometric pattern recognition, Journal of Parallel and Distributed Computing 58 (1999) 466–486. Crossref, ISIGoogle Scholar
    • 6. F. Dehne, A. Fabri and A. Rau-Chaplin, Scalable parallel geometric algorithms for multicomputers, Int. Journal of Computational Geometry and Applications 6 (1996) 379–400. Link, ISIGoogle Scholar
    • 7. A. Fujiwara, M. Inoue and T. Masuzawa, Parallel selection algorithms for CGM and BSP models with application to sorting, Information Processing Society of Japan Journal 41(5) (2000) 1500–1508. Google Scholar
    • 8. A. V. Gerbessiotis and C. J. Siniolakis, Architecture independent parallel selection with applications to parallel priority queues, Theoretical Computer Science 301 (2003) 119–142. Crossref, ISIGoogle Scholar
    • 9. T. Ishimizu, A. Fujiwara, M. Inoue, T. Masuzawa and H. Fujiwara, Parallel algorithms for selection on the BSP and BSP models, Systems and Computers in Japan 33(12) (2002) 97–107. CrossrefGoogle Scholar
    • 10. R. Miller and L. Boxer, Algorithms Sequential and Parallel, A Unified Approach, 3rd edn. (Cengage Learning, Boston, 2013). Google Scholar
    • 11. R. Miller and Q. F. Stout, Parallel Algorithms for Regular Architectures: Meshes and Pyramids (The MIT Press, Cambridge, MA, 1996). Google Scholar
    • 12. J. A. Rice, Mathematical Statistics and Data Analysis, 2nd edn. (International Thomson Publishing, Belmont, CA, 1995). Google Scholar
    • 13. E. L. G. Saukas and S. W. Song, A note on parallel selection on coarse-grained multicomputers, Algorithmica 24 (1999) 371–380. Crossref, ISIGoogle Scholar
    • 14. A. Tiskin, Parallel selection by regular sampling, Euro-Par 2010, Part II, P. D’AmbraM. GuarracinoD. Talia (eds.), LNCS, 6272 (Springer-Verlag, Berlin/Heidelberg, 2010), pp. 393–399. CrossrefGoogle Scholar