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Accelerating Pairwise Computations on Cell Processors
January 2011 (vol. 22 no. 1)
pp. 69-77
ASCII Text
x 
 
Abhinav Sarje, Jaroslaw Zola, Srinivas Aluru, "Accelerating Pairwise Computations on Cell Processors," IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 1, pp. 69-77, January, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TPDS.2010.65,
author = {Abhinav Sarje and Jaroslaw Zola and Srinivas Aluru},
title = {Accelerating Pairwise Computations on Cell Processors},
journal ={IEEE Transactions on Parallel and Distributed Systems},
volume = {22},
number = {1},
issn = {1045-9219},
year = {2011},
pages = {69-77},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPDS.2010.65},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Parallel and Distributed Systems
TI - Accelerating Pairwise Computations on Cell Processors
IS - 1
SN - 1045-9219
SP69
EP77
EPD - 69-77
A1 - Abhinav Sarje,
A1 - Jaroslaw Zola,
A1 - Srinivas Aluru,
PY - 2011
KW - Parallel algorithms
KW - computations on matrices
KW - cell broadband engine
KW - pairwise computations
KW - heterogeneous (hybrid) systems
KW - multicore/single-chip multiprocessors.
VL - 22
JA - IEEE Transactions on Parallel and Distributed Systems
ER -
 
Abhinav Sarje, Iowa State University, Ames
Jaroslaw Zola, Iowa State University, Ames
Srinivas Aluru, Iowa State University, Ames and Indian Institute of Technology Bombay, Mumbai
Direct computation of all pairwise distances or interactions is a fundamental problem that arises in many application areas including particle or atomistic simulations, fluid dynamics, computational electromagnetics, materials science, genomics and systems biology, and clustering and data mining. In this paper, we present methods for performing such pairwise computations efficiently in parallel on Cell processors. This problem is particularly challenging on the Cell processor due to the small sized Local Stores of the Synergistic Processing Elements, the main computational cores of the processor. We present techniques for different variants of this problem including those with large number of entities or when the dimensionality of the information per entity is large. We demonstrate our methods in the context of multiple applications drawn from fluid dynamics, materials science and systems biology, and present detailed experimental results. Our software library is an open source and can be readily used by application scientists to accelerate pairwise computations using Cell accelerators.

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Index Terms:
Parallel algorithms, computations on matrices, cell broadband engine, pairwise computations, heterogeneous (hybrid) systems, multicore/single-chip multiprocessors.
Citation:
Abhinav Sarje, Jaroslaw Zola, Srinivas Aluru, "Accelerating Pairwise Computations on Cell Processors," IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 1, pp. 69-77, Jan. 2011, doi:10.1109/TPDS.2010.65
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