Nathan Brunelle, PhD Proposal Presentation


Date: Tuesday, November 15, 2016 at 9:30AM in Rice 242
Advisor: Gabriel Robins
Committee: James Cohoon (Committee Chair), Kevin Skadron, Mircea Stan (Minor Representative) and Ke Wang.

Scalable Algorithms for the Post Moore’s Law Era

As Moore’s Law wanes over the coming years, new approaches will be necessary to handle massive data volumes. We propose two general methods for scalability that can persist beyond Moore’s Law, namely (1) designing algorithms to operate directly on highly compressed data, thereby avoiding expensive decompression and re-compression, and (2) leveraging massively parallel finite automata-based architectures for specific problem domains. We evaluate the efficiency, extensibility, and generality of these non-traditional approaches in big data environments, and present promising preliminary results.

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