High Productivity Computing. Large-scale Knowledge Discovery: Co-evolving Algorithms and Mechanisms Steve Reinhardt Principal Architect Microsoft. Prof. John Gilbert, UCSB Dr. Viral Shah, UCSB. Context for Knowledge Discovery.
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Large-scale Knowledge Discovery:
Co-evolving Algorithms and Mechanisms
Prof. John Gilbert, UCSB
Dr. Viral Shah, UCSB
From Debbie Gracio and Ian Gorton, PNNL Data Intensive Computing Initiative
Need to get good (not perfect) scalable platforms in use to co-evolve towards best approaches and algorithms
Elementary Mode Analysis
All Pairs Shortest Path
All Pairs Shortest Path(Cray XMT)
(constructors, SpGEMM, SpMV, SpAdd, SpGEMM semi-rings, I/O)
(in-memory (Star-P) or out-of-memory (DryadLINQ-based))
Files, TCP, FIFO, Network
Star-P enables domain experts to use parallel, big-memory systems via productivity languages
(e.g., the M language of MATLAB)
Knowledge discovery scaling with Star-P
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