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On Deciding Granularity for Optimal Speedup for Solving Data Parallel Problems with Clustered Distributed Computing
Taipei, Taiwan December 18-December 20
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ISPAN.1997.6450851997 International Symposium on Paral ...
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Jung-Sing Jwo, Yu Chin Cheng, Chin-Yun Hsieh, "On Deciding Granularity for Optimal Speedup for Solving Data Parallel Problems with Clustered Distributed Computing," Parallel Architectures, Algorithms, and Networks, International Symposium on, pp. 144, 1997 International Symposium on Parallel Architectures, Algorithms and Networks (ISPAN '97), 1997.
 
 
BibTex
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@article{ 10.1109/ISPAN.1997.645085,
author = {Jung-Sing Jwo and Yu Chin Cheng and Chin-Yun Hsieh},
title = {On Deciding Granularity for Optimal Speedup for Solving Data Parallel Problems with Clustered Distributed Computing},
journal ={Parallel Architectures, Algorithms, and Networks, International Symposium on},
volume = {0},
year = {1997},
issn = {1087-4089},
pages = {144},
doi = {http://doi.ieeecomputersociety.org/10.1109/ISPAN.1997.645085},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Parallel Architectures, Algorithms, and Networks, International Symposium on
TI - On Deciding Granularity for Optimal Speedup for Solving Data Parallel Problems with Clustered Distributed Computing
SN - 1087-4089
SP
EP
A1 - Jung-Sing Jwo,
A1 - Yu Chin Cheng,
A1 - Chin-Yun Hsieh,
PY - 1997
KW - granularity
KW - clustered computing
KW - distributed computing
KW - network computing
KW - data parallelism
VL - 0
JA - Parallel Architectures, Algorithms, and Networks, International Symposium on
ER -
 
Jung-Sing Jwo, Tunghai University
Yu Chin Cheng, National Taipei University of Technology
Chin-Yun Hsieh, National Taipei University of Technology
In this paper we show how to obtain optimal speedup in a master-slave model for solving data-parallel problems. Given the number of homogeneous workstations, their computation time for solving a basic sub-task of the problem, network transmission bandwidth and data volume per basic sub-task, the per-distribution number of basic sub-tasks sent to a slave for attaining the optimal speedup can be decided. The effectiveness of the proposed theory has been tested using a parallel computing experiment involving the Hough transformation.
Index Terms:
granularity, clustered computing, distributed computing, network computing, data parallelism
Citation:
Jung-Sing Jwo, Yu Chin Cheng, Chin-Yun Hsieh, "On Deciding Granularity for Optimal Speedup for Solving Data Parallel Problems with Clustered Distributed Computing," ispan, pp.144, 1997 International Symposium on Parallel Architectures, Algorithms and Networks (ISPAN '97), 1997
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