Thesis: Interactive Data Analytics using GPUs

Modern GPUs can provide an order-of-magnitude higher memory bandwidth compared to CPUs which can enable interactive query runtimes on analytical queries. However, the massively parallel architecture of GPUs requires rearchitecting in-memory data analytics systems in order to achieve optimal performance. In my PhD, we developed Crystal, a framework for writing high performance SQL query implementations for GPU. We showed that using GPUs can lead to significant performance improvement (~24x in our setup). Finally, we developed theoretical models for estimating query runtime and showed that Crystal achieves these lower bounds.

Quickr: Approximating Big Data Queries

RDF Provisioning for the Internet of Things

Worked on bringing wireless sensor nodes into the IoT by having embedded tuplestore for RDF data storage on the nodes and interacting with outside world via CoAP.