Novel algebras for advanced analytics in Julia

Author(s)
Shah, Viral B.; Karpinski, Stefan; Edelman, Alan; Bezanson, Jeffrey Werner; Kepner, Jeremy
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Abstract
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.
Date issued
2013-11
URI
http://hdl.handle.net/1721.1/115964
Department
Lincoln Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Department of Mathematics
Journal
2013 IEEE High Performance Extreme Computing Conference (HPEC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
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.
Version: Author's final manuscript
ISBN
978-1-4799-1365-7
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