PPSC · 1995 · 19 citations · 0 references
ComputingComputer ScienceHigh-performance ComputingCluster ComputingParallel AlgorithmsParallel ComputingDistributed ComputingDistributed Memory ParallelismDistributed Memory SystemsMatrix ComputationParallel Performance AnalysisPerformance ModelingDense Matrix AlgorithmsDistributed MemoryDistributed Memory AlgorithmsEigenvalue AlgorithmsEigenvaluesParallel Eigenvalue ComputationParallel Matrix ComputationSparse Matrix Computation
We discuss timing and performance modeling of a routine to find all the eigenvalues and eigenvectors of a dense symmetric matrix on distributed memory computers. The routine, PDSYEVX, is part of the ScaLAPACK library. It is based on bisection and inverse iteration, but is not designed to guarantee orthogonality of eigenvectors in the presence of clustered eigenvalues. We use our validated performance model to conclude that PDSYEVX is very efficient for large enough problem sizes, nearly independently of input and output data layouts. However, efficiency will be low if interprocessor communication is too slow, such as on conventional workstation networks, or if per processor memory is too small, such as on the Intel Gamma. Modeling also helps us choose the appropriate algorithm to deal with clusters.