High Performance Biological Pairwise Sequence Alignment: FPGA versus GPU versus Cell BE versus GPP

TLDR

Reconfigurable computing with FPGAs promises high‑performance, efficient computation, yet its benefits and drawbacks are still being evaluated. The study compares FPGAs, GPUs, and Cell BE implementations of the Smith‑Waterman pairwise sequence alignment algorithm against a general‑purpose processor baseline. The authors implemented Smith‑Waterman on each platform and benchmarked speed, energy consumption, and development and purchase costs. FPGAs achieve higher performance per watt and per dollar than GPUs and Cell BE, but to dominate on cost they must provide at least a 100‑fold speed‑up over general‑purpose processors and a 10‑fold speed‑up over GPUs.

Abstract

This paper explores the pros and cons of reconfigurable computing in the form of FPGAs for high performance efficient computing. In particular, the paper presents the results of a comparative study between three different acceleration technologies, namely, Field Programmable Gate Arrays (FPGAs), Graphics Processor Units (GPUs), and IBM’s Cell Broadband Engine (Cell BE), in the design and implementation of the widely-used Smith-Waterman pairwise sequence alignment algorithm, with general purpose processors as a base reference implementation. Comparison criteria include speed, energy consumption, and purchase and development costs. The study shows that FPGAs largely outperform all other implementation platforms on performance per watt criterion and perform better than all other platforms on performance per dollar criterion, although by a much smaller margin. Cell BE and GPU come second and third, respectively, on both performance per watt and performance per dollar criteria. In general, in order to outperform other technologies on performance per dollar criterion (using currently available hardware and development tools), FPGAs need to achieve at least two orders of magnitude speed-up compared to general-purpose processors and one order of magnitude speed-up compared to domain-specific technologies such as GPUs.