Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 513–521 (2017)
Abstract. The presented study analyses 563 representative benchmark sparse matrices with respect to their partitioning into uniformly-sized blocks. The aim is to minimize memory footprints of matrices. Different block sizes and different ways of storing blocks in memory are considered and statistically evaluated. Memory footprints of partitioned matrices are additionally compared with lower bounds and the CSR storage format. The average measured memory savings against CSR in case of single and double precision are 42.3 and 28.7 percents, respectively. The corresponding worst-case savings are 25.5 and 17.1 percents. Moreover, memory footprints of partitioned matrices were in average 5 times closer to their lower bounds than CSR. Based on the obtained results, we provide generic suggestions for efficient partitioning and storage of sparse matrices in a computer memory.
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