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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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.
DepartmentLincoln Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Department of Mathematics
2013 IEEE High Performance Extreme Computing Conference (HPEC)
Institute of Electrical and Electronics Engineers (IEEE)
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.
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