ACM Transactions on Mathematical Software · 2013 · 214 citations · 37 references
ComputingComputer ScienceHigh-performance ComputingCluster ComputingComputer ArchitectureParallel ComputingDistributed ComputingDistributed Memory ParallelismDistributed Memory SystemsMatrix ComputationParallel ArchitecturesParallel Computer ArchitectureDistributed MemoryDistributed-memory ArchitecturesMany-core ArchitectureMany-core ProcessorsParallel Matrix AlgorithmsParallel Matrix ComputationParallel Sparse Linear AlgebraSparse Matrix Computation
Parallelizing dense matrix computations to distributed memory architectures is a well-studied subject and generally considered to be among the best understood domains of parallel computing. Two packages, developed in the mid 1990s, still enjoy regular use: ScaLAPACK and PLAPACK. With the advent of many-core architectures, which may very well take the shape of distributed memory architectures within a single processor, these packages must be revisited since the traditional MPI-based approaches will likely need to be extended. Thus, this is a good time to review lessons learned since the introduction of these two packages and to propose a simple yet effective alternative. Preliminary performance results show the new solution achieves competitive, if not superior, performance on large clusters.
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The Algebraic Eigenvalue Problem
E. I., J. H. Wilkinson · Mathematics of Computation · 1966 · 5.2K citations
A set of level 3 basic linear algebra subprograms
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