UC Riverside

General purpose computing platforms have generally been favored over customized computational setups, due to the simplified usability and significant reduction of development time. These general purpose machines make use of the Von-Neumann architectural model which suffers from the sequential aspect of computing and heavy reliance on memory offloading.

This dissertation proposes the use of hardware accelerators such as Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) as a substitute or co-processor to general purpose CPUs, with a focus on database applications. Here, large amounts of data are queried in a time-critical manner. This dissertation shows that using hardware platforms allows processing data in a streaming (single pass) and massively parallel manner, hence speeding up computation by several orders of magnitude when compared to general purpose CPUs. The complexity of programming these parallel platforms is abstracted from the developers, as hardware constructs are automatically generated from high-level application languages and/or specifications.

This dissertation explores the hardware acceleration of XML path and twig filtering, using novel dynamic programming algorithms. Publish-subscribe systems present the state of the art in information dissemination to multiple users. Current XML-based publish-subscribe systems provide users with considerable flexibility allowing the formulation of complex queries on the content as well as the (tree) structure of the streaming messages. Messages that contain one or more matches for a given user profile (query) are forwarded to the user.

This dissertation further studies FPGA-based architectures for processing expressive motion patterns on continuous spatio-temporal streams. Complex motion patterns are described as substantially flexible variable-enhanced regular expressions over a spatial alphabet that can be implicitly or explicitly anchored to the time domain. Using FPGAs, thousands of queries are matched in parallel. The challenges in handling several constructs of the assumed query language are explored, with a study on the tradeoffs between expressiveness, scalability and matching accuracy (eliminating false-positives).

Finally, the first parallel Golomb-Rice (GR) integer decompression FPGA-based architecture is detailed, allowing the decoding of unmodified GR streams at the deterministic rate of several bytes (multiple integers) per hardware cycle. Integer decompression is a first step in the querying of inverted indexes.