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A fault-tolerant parallel heuristic for assignment problems

  • Workshop on Biologically Inspired Solutions to Parallel Processing Problems Albert V. Zomaya, The University of Western Australia Fikret Ercal, University of Missouri-Rolla Stephan Olariu, Old Dominion University
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Parallel and Distributed Processing (IPPS 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1388))

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Abstract

This paper presents a new approach for parallel heuristic algorithms based on adaptive parallelism. Adaptive parallelism was used to dvnamically adjust the parallelism degree of the application with respect to the system load. This approach demonstrates that high-performance computing using heterogeneous workstations combined with massively parallel machines is feasible to solve large assignment problems. The fault-tolerant algorithm allows a minimal loss of computation in case of failures. The proposed algorithm exploits the properties of this class of applications in order to reduce the complexity of the algorithm. The parallel heuristic algorithm combines different search strategies: simulated annealing and tabu search. Encouraging results have been obtained in solving the quadratic assignment problem. We have improved the best known solutions for some large real-world problems.

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References

  1. K. M. Chandy and L. Lamport. Distributed snapshots: Determining global states of distributed systems. ACM Transactions on Computer Systems, 3:63–75, Feb 1985.

    Article  Google Scholar 

  2. D. Gelernter and D. L. Kaminsky. Supercomputing out of recycled garbage: Preliminary experience with Piranha. In 6th ACM International Conference on Supercomputing, Jul 1991.

    Google Scholar 

  3. F. Glover and M. Laguna. Tabu search. In C. R. Reeves, editor, Modern Heuristic Techniques for Combinatorial Problems, pages 70–150. Black-well Scientific Publications, 1992.

    Google Scholar 

  4. F. Glover. Tabu search — part I. ORSA Journal of Computing, 1(3):190–206, 1989.

    Google Scholar 

  5. Z. Hafidi, E. G. Talbi, and J-M. Geib. MARS: Adaptive scheduling of parallel applications in a multi-user heterogeneous environment. In European School of Computer Science ESPPE'96: Parallel Programming Environments for High Performance Computing, pages 119–122, Alpe d'Huez, France, Apr 1996.

    Google Scholar 

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

    Google Scholar 

  7. D. Kebbal, E. G. Talbi, and J-M. Geib. A new approach for checkpointing parallel applications. In Int. Conf. on Parallel and Distributed Processing Techniques and Applications PDPTA'97, pages 1643–1651, Las-Vegas, USA, June 1997.

    Google Scholar 

  8. T. Muntean and E. G. Talbi. A parallel genetic algorithm for process-processor mapping.In M. Durand and F. El-Dabagh, editors, 2nd Symposium on High Performance Computing, pages 71–82, Montpellier, France, Oct 1991. Elsevier Science Pub., North-Holland.

    Google Scholar 

  9. D. A. Nichols. Using idle workstations in a shared computing environment. ACM Operating System Review, 21(5):5–12, Nov 1987.

    Article  Google Scholar 

  10. J. Pruyne and M. Livny. Parallel processing on dynamic resources with CARMI. In Proc. of the Workshop on Job Scheduling for Parallel Processing IPPS'95, LNCS No.949, pages 259–278. Springer Verlag, Apr 1995.

    Google Scholar 

  11. J.S. Plank. Efficient Check-pointing on a1IMD Architectures. PhD thesis, Faculty of Princeton University, 1993.

    Google Scholar 

  12. P. M. Pardalos, F. Rendl, and H. Wolkowicz. The quadratic assignment problem: A survey and recent developments. DIMACS Series in Discrete Mathematics and Theoretical Computer Science. 16:1–42, 1994.

    Google Scholar 

  13. C. R. Reeves. Modern heuristic techniques for combinatorial problems. Black Scientific Publications, 1993.

    Google Scholar 

  14. E. Taillard. Comparison of iterative searches for the quadratic assignment problem. Location Science, 3:87–103, 1995.

    Article  Google Scholar 

  15. M. M. Theimer and K. A. Lantz. Finding Idle Machines in a Workstation-Based Distributed System. IEEE Transactions on. Software Engineering, 15(11):1444–1458, November 1989.

    Article  Google Scholar 

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Authors
  1. E-G. Talbi
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  2. J-M. Geib
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  3. Z. Hafidi
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  4. D. Kebbal
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Editor information

José Rolim

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© 1998 Springer-Verlag Berlin Heidelberg

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Talbi, EG., Geib, JM., Hafidi, Z., Kebbal, D. (1998). A fault-tolerant parallel heuristic for assignment problems. In: Rolim, J. (eds) Parallel and Distributed Processing. IPPS 1998. Lecture Notes in Computer Science, vol 1388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64359-1_701

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  • DOI: https://doi.org/10.1007/3-540-64359-1_701

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64359-3

  • Online ISBN: 978-3-540-69756-5

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