In this seminar we survey recent research on using reconfigurable hardware accelerators, namely, Field Programmable Gate Arrays (FPGAs), to accelerate analytical data processing. Such accelerators are being adopted as a way of overcoming the recent stagnation in CPU performance because they can implement algorithms differently from traditional CPUs, breaking traditional trade-offs.
The purpose of the seminar is to familiarize students with the inner working of FPGAs and discuss their benefits in the context of analytical processing, both as an accelerator within a single node database and as part of distributed data analytics pipelines. The seminar also covers architectural trends that are propelling the rapid adoption of accelerators in datacenters and the cloud. We present guidelines for accelerator design, as well as examples of integration within full-fledged Relational Databases. We do so through the prism of recent research papers that explore how emerging compute-intensive operations in databases can benefit from FPGAs.
|Last offered||Winter Semester (22/23)|
|Lecturer||Profs. Zsolt István|
|The kickoff meeting will be announced in Moodle (link above).|