Abstract
We address the problem of efficient strategies for mapping arbitrary parallel programs onto distributed memory message-passing parallel computers. An efficient task assignment strategy based on two phases (task clustering and task reassignment) is proposed. This strategy is suitable for applications which could be partitioned into parallel executable tasks and its design is a trade-off between solution quality and computational complexity. It is evaluated by comparison with some representative heuristics from the literature that cover the range of different complexity categories: two simple greedy algorithms (Largest Processing Time First and Largest Global Cost First) with low complexity, two iterative heuristics (Simulated Annealing and Tabu Search) with the highest complexity and a mixed heuristic (Even Distribution and Task Reassignment) with an intermediate complexity between the other two. It is shown to be very effective for computations whose communication topology matches some well-known regular graph families such as trees, rings and meshes, as well as for arbitrary computations with irregular communications patterns. Its solution quality is better than that produced by the other mixed heuristic and as good as (and sometimes better than) that produced by Simulated Annealing.
This work has been supported by the CYCYT under contract TIC 95-0868, and partially sponsored by the EU's Copernicus Programme under contract numbers CIPA C1-93-0251 and CIPA CP-93-5383.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
M. G. Norman & P. Thanish, “Models of machines and computation for mapping in multicomputer”. ACM Computer Surveys, 25 (3) (1993), pp. 263–302.
O. Krämer and H. Mühlenbein, “Mapping strategies in message-based multiprocessor systems”, Parallel Computing, Vol. 9 (1988/89), pp. 213–225.
H.A-Choi and B. Narahari, “Efficient algorithms for mapping and partitioning a class of parallel computations”, J. Parallel Distributed Computing. 19(4) (1993), pp. 349–363.
C. Lee and L. Bic, “Comparing quenching and slow simulated annealing on the mapping problem”, Proc. 3rd. Annual Parallel Proc. Symp. (1989), pp. 671–685.
M.R. Garey and D.S. Johnson, “Computer and Intractability — A Guide to the Theory of NP-Completeness”, (Freeman, New York, 1979).
P. Bouvry, J. Chassin de Kergommeaux & D. Trystram, “Efficient solutions for mapping parallel programs”. Proc. of EuroPar'95. Springer-Verlag, August (1995), pp. 379–390.
V. Chaudhary and J. Aggarwal, “A generalized scheme for mapping parallel algorithms”, IEEE Trans. on Parall. and Distrib. Systems, Vol. 4, no. 3 (1993), pp. 328–346.
V. M. Lo, “Algorithms for static assignment and symmetric contraction in distributed computing systems”, Proc IEEE Int. Conf. on Parallel Proc., (1988), pp. 239–244.
P. Sadayappan, F.Ercal and J. Ramanujam, “Cluster partitioning approaches to mapping parallel problems on to a hypercube”, Parallel Computing, 13 (1) (1990), pp. 1–6.
P. Sadayappan and F. Ercal, “Nearest-neighbor mapping of finite element graphs onto processor meshes”, IEEE Trans. Comput, Vol 36, no. 12 (1990), pp.1408–1424.
S. Selvakumar & C. Silva Ram Murthy, “An efficient heuristic algorithm for mapping parallel programs onto multicomputers”. Microprocessing and Microprogramming 36 (1992/1993) pp 83–92.
Shen Shen Wu & D. Sweeting, “Heuristic algorithms for task assignment and scheduling in a processor network”. Parallel Computing 20 (1994), pp. 1–14.
T. Agerwala et alter, “SP2 system architecture”, IBM Systems Journal, Vol. 34, No. 2 (1995), pp. 152–184.
M. D. May, P. W. Thompson & P. H. Welch, “Networks, Routers & Transputers”, IOS Press (1993).
A. Gerasoulis and T. Yang, “A comparison of heuristics for scheduling directed acyclic graphs on multiprocessors”, J. of Par. and Distrib. Comput., Vol. 16 (1992), pp. 276–291.
M. A. Senar, A. Cortés, A. Ripoll and E. Luque, “A clustering-reassigning strategy for mapping parallel programs”, Proc. of 8th IASTED Int. Conf. on Par. and Distrib. Computing and Systems, Acta Press, Anaheim, (1996), pp. 166–170.
M. A. Senar, A. Ripoll, A. Cortés and E. Luque, “An efficient clustering-based approach for mapping parallel programs”, Proc. of 5th Euromicro Workshop on Parallel and Distributed Processing, London, January, (1997), pp. 407–412.
R.Graham, “Bounds on multiprocessing timing anomalies”, SIAM, J. Appl. Math., Vol. 17, No. 2, (1969), pp. 416–429.
E. Luque et alter, “PSEE: a tool for parallel systems learning”, Computers and Artificial Intelligence, Vol. 14, No. 1 (1996), pp. 319–339.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Senar, M.A., Ripoll, A., Cortés, A., Luque, E. (1997). Performance comparison of strategies for static mapping of parallel programs. In: Hertzberger, B., Sloot, P. (eds) High-Performance Computing and Networking. HPCN-Europe 1997. Lecture Notes in Computer Science, vol 1225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0031630
Download citation
DOI: https://doi.org/10.1007/BFb0031630
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-62898-9
Online ISBN: 978-3-540-69041-2
eBook Packages: Springer Book Archive