Abstract
Merge sort is useful in sorting a great number of data progressively, especially when they can be partitioned and easily collected to a few processors. Merge sort can be parallelized, however, conventional algorithms using distributed memory computers have poor performance due to the successive reduction of the number of participating processors by a half, up to one in the last merging stage.
This paper presents load-balanced parallel merge sort where all processors do the merging throughout the computation. Data are evenly distributed to all processors, and every processor is forced to work in all merging phases. An analysis shows the upper bound of the speedup of the merge time as (P- 1)/log P where P is the number of processors. We have reached a speedup of 8.2 (upper bound is 10.5) on 32-processor Cray T3E in sorting of 4M 32-bit integers.
This research was supported by KRF grant (no. KRF-99-041-E00287).
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© 2002 Springer-Verlag Berlin Heidelberg
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Jeon, M., Kim, D. (2002). Parallelizing Merge Sort onto Distributed Memory Parallel Computers. In: Zima, H.P., Joe, K., Sato, M., Seo, Y., Shimasaki, M. (eds) High Performance Computing. ISHPC 2002. Lecture Notes in Computer Science, vol 2327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47847-7_5
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DOI: https://doi.org/10.1007/3-540-47847-7_5
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