Level-Based Blocking for Sparse Matrices: Sparse Matrix-Power-Vector Multiplication

Decomposing Irregularly Sparse Matrices for Parallel Matrix-Vector Multiplication

In this work we show the de ciencies of the graph model for decomposing sparse matrices for parallel matrix vector multiplica tion Then we propose two hypergraph models which avoid all de cien cies of the graph model The proposed models reduce the decomposition problem to the well known hypergraph partitioning problem widely en countered in circuit partitioning in VLSI We have implemented fast ...

Sparse Matrix-Vector Multiplication for Circuit Simulation

Sparse Matrix-Vector Multiplication (SpMV) plays an important role in numerical algorithm in circuit simulation. In this report, we utilize Message Passing Interface (MPI) to parallelize the SpMV. In addition, resulting from the circuit simulation matrix formulation, the circuit systems are often represented as unstructured, not evenly-distributed sparse matrices. Therefore, we automatically de...

Autotuning Sparse Matrix-Vector Multiplication for Multicore

Sparse matrix-vector multiplication (SpMV) is an important kernel in scientific and engineering computing. Straightforward parallel implementations of SpMV often perform poorly, and with the increasing variety of architectural features in multicore processors, it is getting more difficult to determine the sparse matrix data structure and corresponding SpMV implementation that optimize performan...

Optimization Methods for Sparse Matrix-Vector Multiplication

GPU accelerated sparse matrix-vector multiplication and sparse matrix-transpose vector multiplication

Many high performance computing applications require computing both sparse matrix-vector product (SMVP) and sparse matrix-transpose vector product (SMTVP) for better overall performance. Under such a circumstance, it is critical to maintain a similarly high throughput for these two computing patterns with the underlying sparse matrix encoded in a single storage format. The compressed sparse blo...