Novel algebras for advanced analytics in Julia
A linear algebraic approach to graph algorithms that exploits the sparse adjacency matrix representation of graphs can provide a variety of benefits. These benefits include syntactic simplicity, easier implementation, and higher performance. One way to employ linear algebra techniques for graph algo...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
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Institute of Electrical and Electronics Engineers (IEEE)
2018
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| Online Access: | http://hdl.handle.net/1721.1/115964 https://orcid.org/0000-0001-7676-3133 |
| _version_ | 1826188430425456640 |
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| author | Shah, Viral B. Karpinski, Stefan Edelman, Alan Bezanson, Jeffrey Werner Kepner, Jeremy |
| author2 | Lincoln Laboratory |
| author_facet | Lincoln Laboratory Shah, Viral B. Karpinski, Stefan Edelman, Alan Bezanson, Jeffrey Werner Kepner, Jeremy |
| author_sort | Shah, Viral B. |
| collection | MIT |
| description | A linear algebraic approach to graph algorithms that exploits the sparse adjacency matrix representation of graphs can provide a variety of benefits. These benefits include syntactic simplicity, easier implementation, and higher performance. One way to employ linear algebra techniques for graph algorithms is to use a broader definition of matrix and vector multiplication. We demonstrate through the use of the Julia language system how easy it is to explore semirings using linear algebraic methodologies. |
| first_indexed | 2024-09-23T07:59:32Z |
| format | Article |
| id | mit-1721.1/115964 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T07:59:32Z |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1159642022-09-30T01:33:59Z Novel algebras for advanced analytics in Julia Shah, Viral B. Karpinski, Stefan Edelman, Alan Bezanson, Jeffrey Werner Kepner, Jeremy Lincoln Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Department of Mathematics Edelman, Alan Bezanson, Jeffrey Werner Kepner, Jeremy A linear algebraic approach to graph algorithms that exploits the sparse adjacency matrix representation of graphs can provide a variety of benefits. These benefits include syntactic simplicity, easier implementation, and higher performance. One way to employ linear algebra techniques for graph algorithms is to use a broader definition of matrix and vector multiplication. We demonstrate through the use of the Julia language system how easy it is to explore semirings using linear algebraic methodologies. 2018-05-30T15:02:25Z 2018-05-30T15:02:25Z 2013-11 2018-05-18T19:04:35Z Article http://purl.org/eprint/type/ConferencePaper 978-1-4799-1365-7 http://hdl.handle.net/1721.1/115964 Shah, Viral B., Alan Edelman, Stefan Karpinski, Jeff Bezanson, and Jeremy Kepner. “Novel Algebras for Advanced Analytics in Julia.” 2013 IEEE High Performance Extreme Computing Conference (HPEC) (September 2013). doi:10.1109/hpec.2013.6670347. https://orcid.org/0000-0001-7676-3133 http://dx.doi.org/10.1109/HPEC.2013.6670347 2013 IEEE High Performance Extreme Computing Conference (HPEC) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT Web Domain |
| spellingShingle | Shah, Viral B. Karpinski, Stefan Edelman, Alan Bezanson, Jeffrey Werner Kepner, Jeremy Novel algebras for advanced analytics in Julia |
| title | Novel algebras for advanced analytics in Julia |
| title_full | Novel algebras for advanced analytics in Julia |
| title_fullStr | Novel algebras for advanced analytics in Julia |
| title_full_unstemmed | Novel algebras for advanced analytics in Julia |
| title_short | Novel algebras for advanced analytics in Julia |
| title_sort | novel algebras for advanced analytics in julia |
| url | http://hdl.handle.net/1721.1/115964 https://orcid.org/0000-0001-7676-3133 |
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