Bibliographic Details
| Title: |
ANALYSIS OF MEMORY FOOTPRINTS OF SPARSE MATRICES PARTITIONED INTO UNIFORMLY-SIZED BLOCKS. |
| Authors: |
LANGR, D.1,2 daniel.langr@fit.cvut.cz, ŠIMEČEK, I.1 |
| Source: |
Scalable Computing: Practice & Experience. Sep2018, Vol. 19 Issue 3, p275-291. 17p. |
| Subjects: |
Matrices software, Sparse matrices |
| Abstract: |
The presented study analyses memory footprints of 563 representative benchmark sparse matrices with respect to their partitioning into uniformly-sized blocks. Different block sizes and different ways of storing blocks in memory are considered and statistically evaluated. Memory footprints of partitioned matrices are then compared with their lower bounds and CSR, indexcompressed CSR, and EBF storage formats. The results show that block-based storage formats may significantly reduce memory footprints of sparse matrices arising from a wide range of application domains. Additionally, measured consistency of results is presented and discussed, benefits of individual formats for storing blocks are evaluated, and an analysis of best-case and worst-case matrices is provided for in-depth understanding of causes of memory savings of block-based formats. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
