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On Task Scheduling Accuracy: Evaluation Methodology and Results

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Abstract

Many heuristics based on the directed acyclic graph (DAG) have been proposed for the static scheduling problem. Most of these algorithms apply a simple model of the target system that assumes fully connected processors, a dedicated communication sub-system and no contention for the communication resources. Only a few algorithms consider the network topology and the contention for the communication resources. This article evaluates the accuracy of task scheduling algorithms and thus the appropriateness of the applied models. An evaluation methodology is proposed and applied to a representative set of scheduling algorithms. The obtained results show a significant inaccuracy of the produced schedules. Analyzing these results is important for the development of more appropriate models and more accurate scheduling algorithms.

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References

  1. T. L. Adam, K. M. Chandy, and J. R. Dickson.A comparison of list schedules for parallel processing systems. Communications of the ACM, 17:685–689, 1974.

    Google Scholar 

  2. I. Ahmad and Y.-K. Kwok. On exploiting task duplication in parallel program scheduling. IEEE Transactions on Parallel and Distributed Systems, 9(8):872–892, September 1998.

    Google Scholar 

  3. J. Baxter and J. H. Patel. The LAST algorithm: A heuristic-based static task allocation algorithm. In Proceedings of the International Conference on Parallel Processing, 2:217–222, 1989.

    Google Scholar 

  4. C. Berge. Graphs and Hypergraphs, 2nd edn., North-Holland, 1976.

  5. S. Booth, J. Fisher, and M. Bowers.Introduction to the Cray T3E at EPCC. Edinburgh Parallel Computing center, Scotland, UK, June 1999. http://www.epcc.ed.ac.uk/t3d/documents/t3e-intro.html

    Google Scholar 

  6. E. G. Coffman. Computer and Job-Scheduling Theory, Wiley, New York, USA, 1976.

    Google Scholar 

  7. E. G. Coffman and R. L. Graham. Optimal scheduling for two-processor systems. Acta Informatica, 1:200–213, 1972.

    Google Scholar 

  8. M. Cosnard and D. Trystram. Parallel Algorithms and Architectures, Int. Thomson Computer Press, London, UK, 1995.

    Google Scholar 

  9. D. E. Culler and J. P. Singh. Parallel Computer Architecture, Morgan Kaufmann Publishers, San Francisco, USA, 1999.

    Google Scholar 

  10. H. El-Rewini and T. G. Lewis. Scheduling parallel program tasks onto arbitray target machines. Journal of Parallel and Distributed Computing, 9(2):138–153, June 1990.

    Google Scholar 

  11. M. R. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NPCompleteness, Freeman, New York, USA, 1979.

    Google Scholar 

  12. A. Gerasoulis and T. Yang. A comparison of clustering heuristics for scheduling DAGs on muliprocessors. Journal of Parallel and Distributed Computing, 16(4):276–291, December 1992.

    Google Scholar 

  13. W. Gropp, E. Lusk, N. Doss, and A. Skjellum. A high-performance, portable implementation of the MPI message passing interface standard. Parallel Computing, 22(6):789–828, September 1996.

    Google Scholar 

  14. T. Hu. Parallel sequencing and assembly line problems. Operations Research, 9:841–848, 1961.

    Google Scholar 

  15. J. J. Hwang, Y. C. Chow, F. D. Anger, and C. Y. Lee.Scheduling precedence graphs in systems with interprocessor communication times. SIAM Journal of Computing, 18(2):244–257, April 1989.

    Google Scholar 

  16. A. A. Khan, C. L. McCreary, and M. S. Jones. A comparison of multiprocessor scheduling heursitics. In Proceedings of the International Conference on Parallel Processing, 2:243–250, August 1994.

    Google Scholar 

  17. S. J. Kim and J. C. Browne. A general approach to mapping of parallel computation upon multiprocessor architectures. In International Conference on Parallel Processing, 3:1–8, 1988.

    Google Scholar 

  18. B. Kruatrachue and T. Lewis. Grain size determination for parallel processing. IEEE Software, 23–32, January 1988.

  19. Y.-K. Kwok and I. Ahmad. Benchmarking the task graph scheduling algorithms. In Proceedings of the International Parallel Processing Symposium/Symposium on Parallel and Distributed Processing (IPPS/ SPDP-98), pp. 531–537, Orlando, Florida, USA, April 1998.

  20. Y.-K. Kwok and I. Ahmad. Link contention-constrained scheduling and mapping of tasks and messages to a network of heterogeneous processors. Cluster Computing: The Journal of Networks, Software Tools, and Applications, 3(2):113–124, 2000.

    Google Scholar 

  21. C. Lee, J. Hwang, Y. Chow, and F. Anger. Multiprocessor scheduling with interprocessor communication delays. Operations Research Letters, 7(3):141–147, 1988.

    Google Scholar 

  22. Z. Liu. A note on Graham's bound. Information Processing Letters, 36:1–5, October 1990.

    Google Scholar 

  23. B. S. Macey and A. Y. Zomaya. A performance evaluation of CP list scheduling heuristics for communication intensive task graphs. In Parallel Processing Symposium, 1998. Proceedings of IPPS/ SPDP 1998, pp. 538–541, 1998.

  24. C. L. McCreary, A. A. Khan, J. J. Thompson, and M. E. McArdle. A comparison of heuristics for scheduling DAGs on multiprocessors. In Eighth International Parallel Processing Symposium, 1994, pp. 446–451, April 1994.

  25. N. Mehdiratta and K. Ghose. A Bottom-UP approach to task scheduling on distributed memory multiprocessors. In Proceedings of the International Conference on Parallel Processing, 2:151–154, August 1994.

    Google Scholar 

  26. Message Passing Interface Forum. MPI:A Message-Passing Interface Standard, June 1995. http:// www.mpi-forum.org/docs/docs.html

  27. M. A. Palis, J.-C. Liou, and D. S. L. Wei. Task clustering and scheduling for distributed memory parallel architectures. IEEE Transactions on Parallel and Distributed Systems, 7(1):46–55, January 1996.

    Google Scholar 

  28. C. H. Papadimitriou and M. Yannakakis. Towards an architecture-independent analysis of parallel algorithms. SIAM Journal of Computing, 19(2):322–328, April 1990.

    Google Scholar 

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

    Google Scholar 

  30. G. C. Sih and E. A. Lee. A compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architectures. IEEE Transactions on Parallel and Distributed Systems, 4(2):175–186, February 1993.

    Google Scholar 

  31. O. Sinnen. Experimental evaluation of task scheduling accuracy. Tese de Mestrado (Master's thesis), Instituto Superior Técnico, Technical University Lisbon, Portugal, December 2001.

    Google Scholar 

  32. O. Sinnen and L. Sousa. Comparison of contention aware list scheduling heuristics for cluster computing. In Workshop on Scheduling and Resource Management for Cluster Computing (ICPP 2001), pp. 382–387, IEEE Computer Society Press, Valencia, Spain, September 2001.

    Google Scholar 

  33. O. Sinnen and L. Sousa. Exploiting unused time slots in list-scheduling considering communication contention. In Euro-Par 2001 Parallel Processing, volume 2150 of Lecture Notes in Computer Science, pp. 166–170, Springer, 2001.

  34. O. Sinnen and L. Sousa. A platform independent parallelising tool based on graph theoretic models. In Vector and Parallel Processing—VECPAR 2000, selected papers, volume 1981 of Lecture Notes in Computer Science, pp. 154–167, Springer, 2001.

  35. O. Sinnen and L. Sousa. Scheduling task graphs on arbitrary processor architectures considering contention. In High Performance Computing and Networking, volume 2110 of Lecture Notes in Computer Science, pp. 373–382, Springer, 2001.

  36. T. Sterling, D. Savarese, D. J. Becker, J. E. Dorband, U. A. Ranawake, and C. V. Packer. BEOWULF: A parallel workstation for scientific computation. In International Conference on Parallel Processing, Vol.1: Architecture, pp. 11–14, CRC Press, Boca Raton, USA, August 1995.

    Google Scholar 

  37. S. Telford. BOBCAT User Guide. Edinburgh Parallel Computing center, Scotland, UK, May 2000. http://www.epcc.ed.ac.uk/sun/documents/introdoc.html

    Google Scholar 

  38. L. Wang, H. J. Siegel, V. P. Roychowdhury, and A. A. Maciejewski. Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach. Journal of Parallel and Distributed Computing, 47:8–22, November 1997.

    Google Scholar 

  39. G. Wirtz. Developing parallel programs in a graph-based environment. In D. Trystram, editor, Proceedings of Parallel Computing 93, Grenoble, France, pp. 345–352, Elsevier Science Publ. Amsterdam, September 1993, North Holland.

    Google Scholar 

  40. M. Y. Wu and D. D. Gajski. Hypertool: A programming aid for message-passing systems. IEEE Transactions on Parallel and Distributed Systems, 1(3):330–343, July 1990.

    Google Scholar 

  41. T. Yang and A. Gerasoulis. PYRROS: static scheduling and code generation for message passing multiprocessors. In Proceedings of 6th ACM International Conference on Supercomputing, pp. 428– 437,Washington D.C, July 1992.

  42. T. Yang and A. Gerasoulis. DSC: scheduling parallel tasks on an unbounded number of processors. IEEE Transactions on Parallel and Distributed Systems, 5(9):951–967, September 1994.

    Google Scholar 

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Sinnen, O., Sousa, L. On Task Scheduling Accuracy: Evaluation Methodology and Results. The Journal of Supercomputing 27, 177–194 (2004). https://doi.org/10.1023/B:SUPE.0000009321.92150.64

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  • DOI: https://doi.org/10.1023/B:SUPE.0000009321.92150.64

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