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List ranking and list scan on the Cray C-90
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Source ACM Symposium on Parallel Algorithms and Architectures archive
Proceedings of the sixth annual ACM symposium on Parallel algorithms and architectures table of contents
Cape May, New Jersey, United States
Pages: 104 - 113  
Year of Publication: 1994
ISBN:0-89791-671-9
Author
Margaret Reid-Miller  Carnegie Mellon Univ., Pittsburgh, PA
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGARCH: ACM Special Interest Group on Computer Architecture
European Comp Soc : European Computer Society
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 20,   Citation Count: 5
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ABSTRACT

List ranking and list scan are two primitive operations used in many parallel algorithms that use list, trees, and graph data structures. But vectorizing and parallelizing list ranking is a challenge because it is highly communication intensive and dynamic. In addition, the serial algorithm is very simple and has very small constants. In order to compete, a parallel algorithm must also be simple and have small constants. A parallel algorithm due to Wyllie is such an algorithm, but it is not work efficient—its performance degrades for longer and longer linked lists. In contrast, work efficient PRAM algorithms developed to date have very large constants. It does not achieve O(log n) running time, but we contend that work efficiency and small constants is more important, given that vector and multiprocessor machines are used for problems that are much larger than the number of processors and, therefore, the O(log n) running time, but we contend that work efficiency and small constants is more important, given that vector and multiprocessor machines are used for problems that are much larger than the number of processors and, therefore, the O(log n) time is never achieved in practice. In particular, to the best of our knowledge, our implementation of list ranking and list scan on the CRAY C-90 is the fastest implementation to date. In addition, it is the first implementation of which we are aware that outperforms fast workstations. The success of our algorithm is due to its relatively large grain size and simplicity of the inner loops, and the success of the implementation is due to pipelining reads and writes through vectorization to hide latency, minimizing load balancing by deriving equations for predicting and optimizing performance, and avoiding conditional tests except when load balancing.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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S. Baase. Introduction to parallel connectivity, list ranking, and Euler tour techniques. In J. Reif, editor, Synthesis of Parallel Algorithms, pages 61-114. Morgan Kaufmann, 1993.
 
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M. Reid-Miller, G. L. Miller, and F. Modugno. List ranking and parallel tree contraction. In J. Reif, editor, Synthesis of Parallel Algorithms, pages 115-194. Morgan Kaufmann, 1993.
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REVIEW

"William Fennell Smyth : Reviewer"

Given a list L of n elements x1,x2,&ldots;,xn , the list scan problem requir  more...

Collaborative Colleagues:
Margaret Reid-Miller: colleagues

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