CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment

Abstract

Background: Searching for similarities in protein and DNA databases has become a routine procedure in Molecular Biology. The Smith-Waterman algorithm has been available for more than 25 years. It is based on a dynamic programming approach that explores all the possible alignments between two sequences; as a result it returns the optimal local alignment. Unfortunately, the computational cost is very high, requiring a number of operations proportional to the product of the length of two sequences. Furthermore, the exponential growth of protein and DNA databases makes the Smith-Waterman algorithm unrealistic for searching similarities in large sets of sequences. For these reasons heuristic approaches such as those implemented in FASTA and BLAST tend to be preferred, allowing faster execution times at the cost of reduced sensitivity. The main motivation of our work is to exploit the huge computational power of commonly available graphic cards, to develop high performance solutions for sequence alignment.

The impact of accelerator processors for high-throughput molecular modeling and simulation

“…Excellent performance speed-ups have been reported for such diverse cases as computational finance [22], fluid dynamics [23], sequence alignment [24] and quantum chemistry [25]. These notable achievements for such a variety of algorithms highlight the potential of APs for computational science in general.…”

Aplicación del modelo de programación CUDA en la simulación de la evolución de secuencias genéticas

“…CUDA (Compute Unified Device Architecture) es una plataforma de computación paralela y un modelo de programación inventado por NVIDIA ("CUDA Zone | NVIDIA Developer," 2011; Nvidia, 2011;NVIDIA, 2015) permite aumentos impresionantes en el rendimiento del computador al aprovechar las unidades de procesamiento gráfico (GPU) alojadas en la tarjeta gráfica. Actualmente se reportan varias aplicaciones de esta tecnología en el área de la Bioinformática: en el análisis de genes en microarreglos y alineación de secuencias (Yongchao Liu, Schmidt, & Maskell, 2012), simulación de sistemas biológicos y búsqueda de secuencias en bases de datos (Y Liu, Wirawan, & Schmidt, 2013), entre otras aplicaciones disponibles en el sitio para desarrolladores de NVIDIA ("CUDA Zone | NVIDIA Developer," 2011), (Hwu, 2012), (Schatz, Trapnell, Delcher, & Varshney, 2007), (Manavski & Valle, 2008).…”

High Performance Protein Sequence Database Scanning on the Cell Broadband Engine

“…Although, it achieves a high efficiency, programming in OpenGL requires specialized skills. Therefore, Manavski and Valle [14] re-implemented the SW algorithm on a GPU with the recently released C-based CUDA programming environment.…”

“…Hence, they are not suitable for many users. Recent usage of easily accessible accelerator technologies to improve the search time of the SW algorithm include Intel SSE2 [6] and GPUs [13,14].…”