To solve large-scale matrix eigenvalue problems (EVPs), a two-step tridiagonalization method using the block Householder transformation (HT) is often employed. Although based on dense arithmetic requires memory storage of O(N2) and an operations O(N3), in this study, these were reduced by approximating low-rank (BLR-) matrices. A special HT for BLR-matrices it are proposed to EVP with real symm...

Leveraging optimization techniques (e.g., register blocking and double buffering) introduced in the context of KBLAS, a Level 2 BLAS high performance library on GPUs, the authors implement dense matrix-vector multiplications within a sparse-block structure. While these optimizations are important for high performance dense kernel executions, they are even more critical when dealing with sparse ...