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 succinctly 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.