Fpga Acceleration for Dna Sequence Alignment

Abstract

In this paper, we present two new hardware architectures that implement the Smith–Waterman algorithm for DNA sequence alignment. Previous low-cost approaches based on Field Programmable Gate Array (FPGA) technology are reviewed in detail and then improved with the goal of increased performance at the same cost (i.e., area). This goal is achieved through low level optimizations aimed to adapt the systolic structure implementing the algorithm to the regular structure of FPGAs, essentially finding the optimum granularity of the systolic cells. The proposed architectures achieve processing rates close to 1 Gbps, clearly outperforming previous approaches. Comparing to the reported FPGA results of the computation of the edit-distance between two DNA sequences, throughput is doubled for the same clock frequency with a minimum area penalty. The design has been implemented on an FPGA-based prototyping board integrated into a bioinformatics system. This has allowed validating the approach in a real system (i.e., including I/O and database access), and comparing the proposed hardware solution to purely software approaches. As shown in the paper, the results are outstanding even for slow-rate buses.

Accelerating Smith-Waterman Alignment of Long DNA Sequences with OpenCL on FPGA

“…Meanwhile, Liu et al have presented SWAPHI-LS [12] a highly optimized hand-tuned implementation for Intel Xeon Phi accelerators. In addition, several proposals based on FPGA speedup sequence alignments [22,2] in the context of DNA comparison. Moreover, Wienbrandt presents a study of multi-FPGAs version in [21].…”

A Highly Parameterized and Efficient FPGA-Based Skeleton for Pairwise Biological Sequence Alignment

“…The implementation reported in [14] achieves 1260 GCUPS peak performance on a Xilinx XC2V6000-4 FPGA part. The implementations reported in [13] and [15] achieve over 3200 GCUPS on the same part. In comparison, our multi-purpose core achieves ~800 GCUPS on the same part.…”

“…The FPGA implementations presented in [13][14] [15] are restricted to DNA sequences, which are a special case of our implementation. The implementation reported in [14] achieves 1260 GCUPS peak performance on a Xilinx XC2V6000-4 FPGA part.…”

“…For exhaustive search algorithms, many Single Instruction Multiple Data (SIMD) and systolic architectures using special purpose hardware have been built to speed up their execution, often with one order of magnitude or more speed-up compared to a workstation based implementation [6][7] [8]. More recently, reconfigurable hardware in the form of Field Programmable Gate Arrays (FPGAs) has been used as a high performance programmable platform for sequence alignment algorithms [9][10] [11] [12] [13] [14] [15].…”

Hardware implementation of efficient path reconstruction for the Smith-Waterman algorithm

“…The processing element employed in this implementation is based on that proposed in [5] with some minor modifications (refer to Figure 6). It is the hardware synthesis of (1) after differential coding is applied.…”