Computations with sparse matrices on "multicore cache based" computers are affected by the irregularity of the problem at hand, and performance degrades easily. In this ...
In this paper, we propose a reconfigurable hardware accelerator for fixed-point-matrix-vector-multiply/add operations, capable to work on dense and sparse matrices formats. The pr...
Sparse matrix-vector multiplication forms the heart of iterative linear solvers used widely in scientific computations (e.g., finite element methods). In such solvers, the matrix-v...
—Sparse Matrix-Vector multiplication (SpMV) is a very challenging computational kernel, since its performance depends greatly on both the input matrix and the underlying architec...
Vasileios Karakasis, Georgios I. Goumas, Nectarios...
Cache-based, general purpose CPUs perform at a small fraction of their maximum floating point performance when executing memory-intensive simulations, such as those required for sp...