Elemental: A new framework for distributed memory dense matrix computations

Abstract 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 it will likely not be practical to use MPI-based implementations. Thus, this is a good time to review what lessons we have learned since the introduction of these two packages and to propose a simple yet effective alternative. Preliminary performance results show the new solution achieves considerably better performance than the previously developed libraries

Elemental: A new framework for distributed memory dense matrix computations

Abstract 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 it will likely not be practical to use MPI-based implementations. 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 (i.e., on two racks o