Nathan Brunelle: PhD Dissertation Defense

Monday, July 31, 2017 at 9:00 am in Rice 242
The committee members are: Gabriel Robins (Advisor), James Cohoon (Committee Chair), Kevin Skadron, Ke Wang and Mircea Stan (Minor Representative).

Title: Superscalable Algorithms

We propose two new highly-scalable approaches to effectively process massive data sets in the post- Moore’s Law era, namely (1) designing algorithms to operate directly on highly compressed data, and (2) leveraging massively parallel finite automata-based architectures for specific problem domains. The former method extends scalability by exploiting regularity in highly-compressible data, while also avoiding expensive decompression and re-compression. The latter hardware compactly encapsulates complex behaviors via simulation of non-deterministic finite-state automata. We evaluate the efficiency, extensibility, and generality of these non-traditional approaches in big data environments. By presenting both promising experimental results and theoretical impossibility arguments, we provide more comprehensive frameworks for future research in these areas.

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