DocumentCode :
1690292
Title :
Accelerating matrix decomposition with replications
Author :
Tai, Yi-Gang ; Dan Lo, Chia-Tien ; Psarris, Kleanthis
Author_Institution :
Dept. of Comput. Sci., Univ. of Texas at San Antonio, San Antonio, TX
fYear :
2008
Firstpage :
1
Lastpage :
8
Abstract :
Matrix decomposition applications that involve large matrix operations can take advantage of the flexibility and adaptability of reconfigurable computing systems to improve performance. The benefits come from replication, which includes vertical replication and horizontal replication. If viewed on a space-time chart, vertical replication allows multiple computations executed in parallel, and horizontal replication renders multiple functions on the same piece of hardware. In this paper, the reconfigurable architecture that supports replications for matrix decomposition applications on reconfigurable computing systems is described, and issues including the comparison of algorithms on the system and data movement between the internal computation cores and the external memory subsystem are addressed. A prototype of such a system is implemented to prove the concept. It is expected to improve the performance and scalability of matrix decomposition involving large matrices.
Keywords :
mathematics computing; matrix decomposition; reconfigurable architectures; matrix decomposition; reconfigurable architecture; reconfigurable computing systems; space-time chart; vertical replication; Acceleration; Computer science; Concurrent computing; Equations; Field programmable gate arrays; Iterative algorithms; Least squares approximation; Least squares methods; Matrix decomposition; Scalability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Parallel and Distributed Processing, 2008. IPDPS 2008. IEEE International Symposium on
Conference_Location :
Miami, FL
ISSN :
1530-2075
Print_ISBN :
978-1-4244-1693-6
Electronic_ISBN :
1530-2075
Type :
conf
DOI :
10.1109/IPDPS.2008.4536525
Filename :
4536525
Link To Document :
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