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Basics

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Scheduling for Parallel Processing

Part of the book series: Computer Communications and Networks ((CCN))

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Abstract

In this chapter, we present notions shared across the book. The purpose of this chapter is not only to define terms and notions, which may be known to the reader, but also to disambiguate some of them. Graph theory models are often used in scheduling. Therefore, we will introduce basic definitions of the graph theory. Most of the scheduling problems have combinatorial nature. Hence, elements of the computational complexity theory providing guidelines in analyzing combinatorial optimization problems are outlined. Then, selected methods solving hard combinatorial problems are discussed. Finally, basic metrics of parallel performance are presented.

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References

  1. E. Aarts and J.K. Lenstra. Local Search in Combinatorial Optimization. Wiley, New York, NY, 1997.

    MATH  Google Scholar 

  2. S.G. Akl. The Design and Analysis of Parallel Algorithms. Prentice-Hall, Englewood Cliffs, NJ, 1989.

    MATH  Google Scholar 

  3. G.M. Amdahl. Validity of the single processor approach to achieving large scale computing capabilities. In AFIPS Conference Proceedings (Atlantic City Apr. 18–20, 1967), volume 30, pages 483–485. AFIPS, 1967.

    Google Scholar 

  4. R. Bellman. Dynamic Programming. Princeton University Press, Princeton, NJ, 1957.

    MATH  Google Scholar 

  5. M. Calzarossa and G. Serazzi. Workload characterization: A survey. Proceedings of the IEEE, 81(8):1136–1150, 1993.

    Article  Google Scholar 

  6. V. Cerny. Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. J. Optimization Theory and Applications, 45:41–51, 1985.

    Article  MATH  MathSciNet  Google Scholar 

  7. E.G. Coffman Jr., editor. Computer and Job-Shop Scheduling Theory. Wiley, New York, 1976.

    MATH  Google Scholar 

  8. T.H. Cormen, C.E. Leiserson, and R.L. Rivest. Introduction to Algorithms. MIT Press, Cambridge MA and MCGraw-Hill, New York, 1990.

    MATH  Google Scholar 

  9. T.G. Crainic, B. Le Cun, and C. Roucairol. Parallel branch-and-bound algorithms. In E.-G. Talbi, editor, Parallel Combinatorial Optimization, pages 1–28. Wiley, Hoboken NJ, 2006.

    Chapter  Google Scholar 

  10. D.E. Culler and Arvind. Resource requirements of dataflow programs. In Proceedings of the 15th Annual International Symposium on Computer Architecture (ISCA’88), pages 141–150, 1988. IEEE Computer Society, Los Alamitos, CA, USA.

    Google Scholar 

  11. L. Dowdy and M. Leuze. On modeling partitioned multiprocessor systems. Journal of High Performance Computing, 1(6):31–53, 1993.

    Google Scholar 

  12. A.B. Downey. A model for speedup of parallel programs. Technical Report Report No. UCB/CSD-97-933, Computer Science Division, University of California, Berkeley, CA 94720, 1997.

    Google Scholar 

  13. M. Drozdowski and P. Wolniewicz. Out-of-core divisible load processing. IEEE Transactions on Parallel and Distributed Systems, 14(10):1048–1056, 2003.

    Article  Google Scholar 

  14. J. Edmonds. Scheduling in the dark. Theoretical Computer Science, 235(1):109–141, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  15. L. Finta, Z. Liu, I. Millis, and E. Bampis. Scheduling UET-UCT series–parallel graphs on two processors. Theoretical Computer Science, 162(2):323–340, 1996.

    Article  MATH  MathSciNet  Google Scholar 

  16. M.R. Garey and D.S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, San Francisco, 1979.

    MATH  Google Scholar 

  17. E.F. Gehringer, D.P. Siewiorek, and Z. Segall. Parallel Processing: The Cm ∗ Experience. Digital Press, Bedford, 1987.

    Google Scholar 

  18. D. Ghosal, G. Serazzi, and S. Tripathi. The processor working set and its use in scheduling multiprocessor systems. IEEE Transactions on Software Engineering, 17(5):443–453, 1991.

    Article  Google Scholar 

  19. F. Glover. Tabu-search part i. ORSA Journal on Computing, 1(3):190–206, 1989.

    MATH  Google Scholar 

  20. F. Glover and M. Laguna. Tabu Search. Kluwer Academic, Boston, 1997.

    MATH  Google Scholar 

  21. F. Glover and M. Laguna. Tabu search. In D.-Z. Du and P.M. Pardalos, editors, Handbook of Combinatorial Optimization, volume 3, pages 621–757. Kluwer Academic, Dordrecht, The Netherlands, 1998.

    Google Scholar 

  22. D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA, 1989.

    MATH  Google Scholar 

  23. A.Y. Grama and V. Kumar. Scalability analysis of partitioning strategies for finite element graphs: A summary of results. In Proceedings of Supercomputing ’92, pages 83–92. IEEE Computer Society, Los Alamitos, CA, USA, 1992.

    Chapter  Google Scholar 

  24. A. Gupta and V. Kumar. Performance properties of large scale parallel systems. Journal of Parallel and Distributed Computing, 19(3):234–244, 1993.

    Article  Google Scholar 

  25. J.L. Gustafson. Reevaluating Amdahl’s law. Communications of the ACM, 31(5):532–533, 1988.

    Article  Google Scholar 

  26. A. Hertz and D. de Werra. The tabu search metaheuristic: How we used it. Annals of Mathematics and Artificial Intelligence, 1:111–121, 1990.

    Article  MATH  Google Scholar 

  27. K. Hwang. Advanced Computer Architecture: Parallelism, Scalability, Programmability. McGraw-Hill, New York, 1993.

    Google Scholar 

  28. J. Jájá. An Introduction to Parallel Algorithms. Addison-Wesley, Reading, MA, 1992.

    MATH  Google Scholar 

  29. L.G. Khachiyan. A polynomial algorithm in linear programming (in Russian). Doklady Akademii Nauk SSSR, 244:1093–1096, 1979.

    MATH  MathSciNet  Google Scholar 

  30. S. Kirkpatrick, C.D. Gelatt Jr., and M.P. Vecchi. Optimization by simulated annealing. Science, 220(4598):671–680, 1983.

    Article  MathSciNet  Google Scholar 

  31. B. Korte and J. Vygen. Combinatorial Optimization: Theory and Algorithms. Springer, Berlin, Heidelberg, 2002.

    MATH  Google Scholar 

  32. M. Kumar. Measuring parallelism in computation-intensive scientific/engineering applications. IEEE Transactions on Computers, 9(37):1088–1098, 1988.

    Article  Google Scholar 

  33. P.J.M. Laarhoven and E.H.L. Aarts, editors. Simulated Annealing: Theory and Applications. D. Reidel, Dordrecht, The Netherlands, 1987.

    MATH  Google Scholar 

  34. J.K. Lenstra. Sequencing by Enumerative Methods. Number 69 in Mathematical Centre Tracts. Matematisch Centrum, Amsterdam, 1977.

    Google Scholar 

  35. M. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller. Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21:1087–1092, 1953.

    Article  Google Scholar 

  36. Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin, Heidelberg, 1996.

    MATH  Google Scholar 

  37. J.E. Mitchell, P.M. Pardalos, and M.G.C. Resende. Interior point methods for combinatorial optimization. In D.-Z. Du and P.M. Pardalos, editors, Handbook of Combinatorial Optimization, volume 1, pages 189–297. Kluwer Academic, Dordrecht, The Netherlands, 1998.

    Google Scholar 

  38. C.H. Papadimitriou. Computational Complexity. Addison Wesley, Reading, MA, 1994.

    MATH  Google Scholar 

  39. C.H. Papadimitriou and K. Steiglitz. Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, Englewood Cliffs, NJ, 1982.

    MATH  Google Scholar 

  40. M. Rothkopf. Scheduling independent tasks on parallel processors. Management Science, 12(5):437–447, 1966.

    Article  MathSciNet  Google Scholar 

  41. V. Sarkar. Partitioning and Scheduling Parallel Programs for Multiprocessors. MIT Press, Cambridge MA, 1989.

    MATH  Google Scholar 

  42. K.C. Sevcik. Application scheduling and processor allocation in multiprogrammed parallel processing systems. Performance Evaluation, 19:107–140, 1994.

    Article  MATH  Google Scholar 

  43. Wikipedia. Computational complexity theory. http://en.wikipedia.org/wiki/Computational_complexity_theory, 2008 [online accessed 29 September 2008].

  44. Wikipedia. Metaheuristic. http://en.wikipedia.org/wiki/Metaheuristic, 2008 [online accessed 29 September 2008].

  45. J.L. Wolf, J. Turek, M.-S. Chen, and P.S. Yu. A hierarchical approach to parallel multiquery scheduling. IEEE Transactions on Parallel and Distributed Systems, 6(6):578–590, 1995.

    Article  Google Scholar 

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Authors and Affiliations

  1. Poznan University of Technology Inst. Computing Science, ul. Piotrowo 2, 60-695, Poznan, Poland

    Maciej Drozdowski

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  1. Maciej Drozdowski
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Correspondence to Maciej Drozdowski .

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© 2009 Springer-Verlag London Limited

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Drozdowski, M. (2009). Basics. In: Scheduling for Parallel Processing. Computer Communications and Networks. Springer, London. https://doi.org/10.1007/978-1-84882-310-5_2

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  • DOI: https://doi.org/10.1007/978-1-84882-310-5_2

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-309-9

  • Online ISBN: 978-1-84882-310-5

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