Rucci Enzo, Garcia Carlos, Botella Guillermo, De Giusti Armando, Naiouf Marcelo, Prieto-Matias Manuel
III-LIDI, CONICET, Facultad de Informática, Universidad Nacional de La Plata, La Plata (Buenos Aires), 1900, Argentina.
Depto. Arquitectura de Computadores y Automática, Universidad Complutense de Madrid, Madrid, 28040, Spain.
BMC Syst Biol. 2018 Nov 20;12(Suppl 5):96. doi: 10.1186/s12918-018-0614-6.
The 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.
This 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.
The 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.
史密斯-沃特曼（SW）算法是搜索两条DNA或蛋白质序列之间相似区域的最佳选择。然而，由于其高计算需求，在某些情况下可能不切实际。因此，计算机科学界专注于使用现代并行架构，如图形处理单元（GPU）、至强融核加速器和现场可编程门阵列（FPGA）来加速大规模工作负载。
本文介绍并评估了SWIFOLD：一种基于FPGA、使用OpenCL针对长DNA序列的史密斯-沃特曼并行实现。首先，我们评估了其在不同内核配置下的性能和资源使用情况。接下来，我们在考虑三个不同数据集的情况下，将我们的工具与其他最先进的实现进行了性能比较。对于中小型测试集，SWIFOLD提供了最佳的平均性能，实现了与输入大小和序列相似性无关的性能。此外，与基于最新一代GPU针对大型数据集的实现相比，SWIFOLD也提供了具有竞争力的性能率。
结果表明，SWIFOLD可以成为以经济实惠的方式加速无限制大小DNA序列的SW比对的有力竞争者，平均可达125 GCUPS，峰值接近270 GCUPS。
