Data-centric computing with the Netezza architecture.

While relational databases have become critically important in business applications and web services, they have played a relatively minor role in scientific computing, which has generally been concerned with modeling and simulation activities. However, massively parallel database architectures are beginning to offer the ability to quickly search through terabytes of data with hundred-fold or even thousand-fold speedup over server-based architectures. These new machines may enable an entirely new class of algorithms for scientific applications, especially when the fundamental computation involves searching through abstract graphs. Three examples are examined and results are reported for implementations on a novel, massively parallel database computer, which enabled very high performance. Promising results from (1) computation of bibliographic couplings, (2) graph searches for sub-circuit motifs within integrated circuit netlists, and (3) a new approach to word sense disambiguation in natural language processing, all suggest that the computational science community might be able to make good use of these new database machines.