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ANALYSIS OF MEMORY FOOTPRINTS OF SPARSE MATRICES PARTITIONED INTO UNIFORMLY-SIZED BLOCKS.

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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]
Copyright of Scalable Computing: Practice & Experience is the property of Scalable Computing: Practice & Experience and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: ANALYSIS OF MEMORY FOOTPRINTS OF SPARSE MATRICES PARTITIONED INTO UNIFORMLY-SIZED BLOCKS.
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  Data: <searchLink fieldCode="JN" term="%22Scalable+Computing%3A+Practice+%26+Experience%22">Scalable Computing: Practice & Experience</searchLink>. Sep2018, Vol. 19 Issue 3, p275-291. 17p.
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  Data: 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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  Data: <i>Copyright of Scalable Computing: Practice & Experience is the property of Scalable Computing: Practice & Experience and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.12694/scpe.v19i3.1358
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      – TitleFull: ANALYSIS OF MEMORY FOOTPRINTS OF SPARSE MATRICES PARTITIONED INTO UNIFORMLY-SIZED BLOCKS.
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              Text: Sep2018
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