Abstract
Partitioning of loops is a very important optimization issue and requires the efficient and exact data dependence analysis. Although several methods exist in order to parallelize loops with non-uniform dependences, most of them perform poorly due to irregular and complex dependence constraints. This paper proposes Improved Region Partitioning Method for minimizing the size of the sequential region and maximizing parallelism. Our approach is based on the Convex Hull theory that has adequate information to handle non-uniform dependences. By parallelizing anti dependence region using variable renaming, we will divide the iteration space into two parallel regions and one or less sequential region. Comparison with other schemes shows more parallelism than the existing techniques.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kuck, D., Sameh, A., Cytron, R., Polychronopoulos, A.V.C., Lee, G., McDaniel, T., Leasure, B., Beckman, C., Davies, J., Kruskal, C.: The effects of program restructuring, algorithm change and architecture choice on program performance. In: Proceedings of the 1984 International Conference on Parallel Processing (1984)
Chen, Y.S., Wang, S.D., Wang, C.M.: Tiling nested loops into maximal rectangular blocks. Journal of Parallel and Distributed Computing 35(2), 123–132 (1996)
Wolfe, M.: Loop skewing: The wavefront method revisited. International Journal of Parallel Programming, 279–293 (1986)
Banerjee, U.: Loop Transformations for Restructuring compilers. Kluwer Academic Publishers, Dordrecht (1993)
Wolfe, E., Lam, M.S.: A loop transformation theory and an algorithm to maximize parallelism. IEEE transactions on Parallel and Distributed Systems 2, 452–471 (1991)
Banerjee, U.: Dependence Analysis for Supercomputing. Kluwer Academic, Norwell, Mass. (1988)
Shen, Z., Li, Z., Yew, P.: An empirical study on array subscripts and data dependences. In: Proc. Int. Conf. Parallel Processing, vol. II, pp. 145–152 (1989)
Tzen, T., Ni, L.: Dependence uniformization: A loop parallelization technique. IEEE Trans. Parallel and Distributed Systems 4(5), 547–558 (1993)
Punyamurtula, S., Chaudhary, V.: Minimum dependence distance tiling of nested loops with non-uniform dependences. In: Proc. Symp. Parallel and Distributed Processing, pp. 74–81 (1994)
Ju, J., Chaudhary, V.: Unique sets oriented Partitioning of nested loops with nonuniform dependences. In: Proc. Int. Conf. Parallel Processing, vol. III, pp. 45–52 (1996)
Zaafrani, A., Ito, M.R.: Parallel region execution of loops with irregular dependencies. In: Proc. Int. Conf. Parallel Processing, vol. II, pp. 11–19 (1994)
Wolfe, M., Tseng, C.W.: The power test for data dependence. IEEE Trans. Parallel and Distributed Systems 3(5), 591–601 (1992)
Cho, C.K., Lee, M.H.: A Loop Parallization Method for Nested Loops with Nonuniform Dependences. In: Proceedings of the International Conference on Parallel and Distributed Systems, pp. 314–321 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jeong, S.J. (2004). Maximizing Parallelism for Nested Loops with Non-uniform Dependences. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24768-5_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-24768-5_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22060-2
Online ISBN: 978-3-540-24768-5
eBook Packages: Springer Book Archive