Structural Agnostic SpMV: Adapting CSR-Adaptive for Irregular Matrices

Sparse matrix vector multiplication (SpMV) is an important linear algebra primitive. Recent research has focused on improving the performance of SpMV on GPUs when using compressed sparse row (CSR), the most frequently used matrix storage format on CPUs. Efficient CSR-based SpMV obviates the need for other GPU-specific storage formats, thereby saving runtime and storage overheads. However, existing CSR-based SpMV algorithms on GPUs perform poorly...