Parallel and Distributed Processing Symposium, International (2008)
Miami, FL, USA
Apr. 14, 2008 to Apr. 18, 2008
Kleanthis Psarris , Department of Computer Science, University of Texas at San Antonio, One UTSA Circle, 78249, USA
Yi-Gang Tai , Department of Computer Science, University of Texas at San Antonio, One UTSA Circle, 78249, USA
Chia-Tien Dan Lo , Department of Computer Science, University of Texas at San Antonio, One UTSA Circle, 78249, USA
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.
Kleanthis Psarris, Yi-Gang Tai, Chia-Tien Dan Lo, "Accelerating matrix decomposition with replications", Parallel and Distributed Processing Symposium, International, vol. 00, no. , pp. 1-8, 2008, doi:10.1109/IPDPS.2008.4536525