Abstract
We improve the performance of sparse matrix-vector multiplication(SpMV) on modern cache-based superscalar machines when the matrix structure consists of multiple, irregularly aligned rectangular blocks. Matrices from finite element modeling applications often have this structure. We split the matrix, A, into a sum, A 1 + A 2 + ... + A s , where each term is stored in a new data structure we refer to as unaligned block compressed sparse row (UBCSR) format. A classical approach which stores A in a BCSR can also reduce execution time, but the improvements may be limited because BCSR imposes an alignment of the matrix non-zeros that leads to extra work from filled-in zeros. Combining splitting with UBCSR reduces this extra work while retaining the generally lower memory bandwidth requirements and register-level tiling opportunities of BCSR. We show speedups can be as high as 2.1× over no blocking, and as high as 1.8× over BCSR as used in prior work on a set of application matrices. Even when performance does not improve significantly, split UBCSR usually reduces matrix storage.
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Vuduc, R.W., Moon, HJ. (2005). Fast Sparse Matrix-Vector Multiplication by Exploiting Variable Block Structure. In: Yang, L.T., Rana, O.F., Di Martino, B., Dongarra, J. (eds) High Performance Computing and Communications. HPCC 2005. Lecture Notes in Computer Science, vol 3726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11557654_91
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DOI: https://doi.org/10.1007/11557654_91
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