Optimal eigenvalue computation on distributed-memory MIMD multiprocessors

Conference · Thu Oct 01 04:00:00 EDT 1992

OSTI ID:10177289

Crivelli, S.; Jessup, E. R.

Simon proves that bisection is not the optimal method for computing an eigenvalue on a single vector processor. In this paper, we show that his analysis does not extend in a straightforward way to the computation of an eigenvalue on a distributed-memory MIMD multiprocessor. In particular, we show how the optimal number of sections (and processors) to use for multisection depends on variables such as the matrix size and certain parameters inherent to the machine. We also show that parallel multisection outperforms the variant of parallel bisection proposed by Swarztrauber or this problem on a distributed-memory MIMD multiprocessor. We present the results of experiments on the 64-processor Intel iPSC/2 hypercube and the 512-processor Intel Touchstone Delta mesh multiprocessor.

• Research Organization: Colorado Univ., Boulder, CO (United States). Dept. of Computer Science
• Sponsoring Organization: USDOE, Washington, DC (United States); National Science Foundation, Washington, DC (United States)
• DOE Contract Number: FG02-92ER25122; AC05-84OR21400
• OSTI ID: 10177289
• Report Number(s): DOE/ER/25122--4; CU-CS--617-92; CONF-930331--14; ON: DE93018337; CNN: Grant CCR-9109785
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
990200
ARRAY PROCESSORS
DISTRIBUTED DATA PROCESSING
EIGENVALUES
ITERATIVE METHODS
MATHEMATICS AND COMPUTERS
OPTIMIZATION
PARALLEL PROCESSING