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The Computer Journal
Article . 2003 . Peer-reviewed
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Coarse-Grained Parallel Transitive Closure Algorithm: Path Decomposition Technique

Coarse-grained parallel transitive closure algorithm: Path decomposition technique
Authors: Gibbons, A; Pagourtzis, A; Potapov, I; Rytter, W;

Coarse-Grained Parallel Transitive Closure Algorithm: Path Decomposition Technique

Abstract

Summary: We investigate the relation between fine-grained and coarse-grained distributed computations of a class of problems related to the generic Transitive Closure problem (TC for short). We choose an intricate systolic algorithm for the TC problem, by Guibas, Kung and Thompson (GKT algorithm for short), as a starting point due to its particularly close relationship to matrix multiplication. The GKT algorithm reduces the TC problem to three successive parallel matrix multiplications. We extract the main ideas of this algorithm, namely different path decompositions related to min paths and max paths computations and devise a two-pass parallel algorithm, such that the second pass is purely a triangular matrix multiplication involving exactly 3 of the total number of elementary operations (multiplying two single elements of the matrix). This is helpful in coarse grained parallel computations since matrix multiplication is well parallelizable. A novel approach is used and as a first result a more efficient and simpler two-pass fine-grained algorithm is designed. The second result is a non-trivial transformation of this fine-grained algorithm into a coarse-grained (and more practical) version. The full proof of correctness of the transformation, which is presented in the appendices, is quite complex and is the hardest result of the paper. Our algorithms are specially structured to directly show the correspondence between the main fine-grained and the main coarse-grained operations.

Keywords

Parallel algorithms in computer science, fine-grained algorithm, 004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Average
Average
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