FPGA-Accelerated Analytics: From Single Nodes to Clusters

In this monograph, we survey recent research on using reconfigurable hardware accelerators, namely, Field Programmable Gate Arrays (FPGAs), to accelerate analytical 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. As such, it is timely to 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. We present guidelines for accelerator design in both scenarios, as well as, examples of integration within full-fledged Relational Databases. We do so through the prism of recent research projects that explore how emerging compute-intensive operations in databases can benefit from FPGAs. Finally, we highlight future research challenges in programmability and integration, and cover architectural trends that are propelling the rapid adoption of accelerators in datacenters and the cloud.