Abstract
The amount of textual data has reached a new scale and continues to grow at an unprecedented rate. IBM's SystemT software is a powerful text-analytics system that offers a query-based interface to reveal the valuable information that lies within these mounds of data. However, traditional server architectures are not capable of analyzing so-called big data efficiently, despite the high memory bandwidth that is available. The authors show that by using a streaming hardware accelerator implemented in reconfigurable logic, the throughput rates of the SystemT's information extraction queries can be improved by an order of magnitude. They also show how such a system can be deployed by extending SystemT's existing compilation flow and by using a multithreaded communication interface that can efficiently use the accelerator's bandwidth.