When Spark Meets FPGAs: A Case Study for Next-Generation DNA Sequencing Acceleration

Abstract

FPGA-enabled datacenters have shown great potential for providing performance and energy efficiency improvement, and captured a great amount of attention from both academia and industry. In this paper we aim to answer one key question: how can we efficiently integrate FPGAs into state-of-the-art big-data computing frameworks? Although very important, this problem has not been well studied, especially for the integration of fine-grained FPGA accelerators that have short execution time but will be invoked many times. To provide a generalized methodology and insight for efficient integration, we conduct an in-depth analysis of challenges and corresponding solutions of integration at single-thread, single-node multi-thread, and multi-node levels. With a step-by-step case study for the next-generation DNA sequencing application, we demonstrate how a straightforward integration with 1000x slowdown can be tuned into an efficient integration with 2.6x overall system speedup and 2.4x energy efficiency improvement.