<sc>FHAST</sc>: FPGA-Based Acceleration of <sc>Bowtie</sc> in Hardware

Abstract

Abstract-While the sequencing capability of modern instruments continues to increase exponentially, the computational problem of mapping short sequenced reads to a reference genome still constitutes a bottleneck in the analysis pipeline. A variety of mapping tools (e.g., BOWTIE, BWA) is available for general-purpose computer architectures. These tools can take many hours or even days to deliver mapping results, depending on the number of input reads, the size of the reference genome and the number of allowed mismatches or insertion/deletions, making the mapping problem an ideal candidate for hardware acceleration. In this paper, we present FHAST (FPGA hardware accelerated sequence-matching tool), a drop-in replacement for BOWTIE that uses a hardware design based on field programmable gate arrays (FPGA). Our architecture masks memory latency by executing multiple concurrent hardware threads accessing memory simultaneously. FHAST is composed by multiple parallel engines to exploit the parallelism available to us on an FPGA. We have implemented and tested FHAST on the Convey HC-1 and later ported on the Convey HC-2ex, taking advantage of the large memory bandwidth available to these systems and the shared memory image between hardware and software. A preliminary version of FHASTrunning on the Convey HC-1 achieved up to 70x speedup compared to BOWTIE (single-threaded). An improved version of FHAST running on the Convey HC-2ex FPGAs achieved up to 12x fold speed gain compared to BOWTIE running eight threads on an eight-core conventional architecture, while maintaining almost identical mapping accuracy. FHAST is a drop-in replacement for BOWTIE, so it can be incorporated in any analysis pipeline that uses BOWTIE (e.g., TOPHAT).

3D-Stacked Many-Core Architecture for Biological Sequence Analysis Problems

“…Backward searching against FM-index would find out whether the read occurs in the reference genome and the corresponding location(s). Accelerating backward searching on FPGA has been proposed by Fernandez et al [20] using Convey HC-1 to process the whole short read. We follow the similar idea over hardware design, but also incorporate the method used in BWA-SW [36] that searching against FM-index would find out whether the head part of a read, called seed, occurs in the reference genome and the corresponding location(s).…”

“…A great number of papers have been published that either use FPGAs [16][17][18][19][20][21]55], or GPUs [28][29][30].…”

FPGAs for Software Programmers

“…Three applications have been implemented using the MT-FPGA model on the Convey HC-2ex: bioinformatics (short read matching) [FVLN15], sparse linear algebra (SpMV) [HVN14] and database (hash join operation) [HANT15]. In all three implementations we demonstrated a much higher throughput than state of the art high-end CPUs and GPUs (in the SpMV case).…”

Whisper: read sorting allows robust mapping of DNA sequencing data

“…Prominent examples are the tools SOAP3 (Liu et al, 2012) and SOAP3-dp (Luo et al, 2013), being about an order of magnitude faster than their CPU-based counterparts. The FPGA platform was also used for read mapping, with solutions involving a hash table (Olson et al, 2012) or an FM-index (Chen et al, 2013;Fernandez et al, 2015).…”