Abstract
BackgroundThe Smith-Waterman (SW) algorithm is the best choice for searching similar regions between two DNA or protein sequences. However, it may become impracticable in some contexts due to its high computational demands. Consequently, the computer science community has focused on the use of modern parallel architectures such as Graphics Processing Units (GPUs), Xeon Phi accelerators and Field Programmable Gate Arrays (FGPAs) to speed up large-scale workloads.ResultsThis paper presents and evaluates SWIFOLD: a Smith-Waterman parallel Implementation on FPGA with OpenCL for Long DNA sequences. First, we evaluate its performance and resource usage for different kernel configurations. Next, we carry out a performance comparison between our tool and other state-of-the-art implementations considering three different datasets. SWIFOLD offers the best average performance for small and medium test sets, achieving a performance that is independent of input size and sequence similarity. In addition, SWIFOLD provides competitive performance rates in comparison with GPU-based implementations on the latest GPU generation for the large dataset.ConclusionsThe results suggest that SWIFOLD can be a serious contender for accelerating the SW alignment of DNA sequences of unrestricted size in an affordable way reaching on average 125 GCUPS and almost a peak of 270 GCUPS.
Highlights
The Smith-Waterman (SW) algorithm is the best choice for searching similar regions between two DNA or protein sequences
We evaluate the performance of SWIFOLD, an SW implementation for DNA sequences of unrestricted size, on Intel’s FPGA by means of the Open Computing Language (OpenCL) paradigm
Experimental platforms and tests carried out The experiments were performed on three systems equipped with different accelerator types, namely FPGA, Graphics Processing Units (GPUs) and Xeon Phi
Summary
The Smith-Waterman (SW) algorithm is the best choice for searching similar regions between two DNA or protein sequences. It may become impracticable in some contexts due to its high computational demands. One of the main challenges for the scientific community is to extract relevant information from these data in a reasonable time, which has motivated the collaboration of disciplines such as Biology. The parallelization of SW has been developed in two different alignment contexts: (i) a protein sequence against a genomic database; and (ii) two long DNA sequences. In the DNA case, a single pairwise alignment of Megabase DNA sequences could involve a matrix size of petabyte scale. The parallelization approaches in DNA alignment are based on the wavefront method [6], in which the matrix is calculated by diagonals and all cells in each diagonal are computed in parallel
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