Abstract
BackgroundThe maximal sensitivity for local alignments makes the Smith-Waterman algorithm a popular choice for protein sequence database search based on pairwise alignment. However, the algorithm is compute-intensive due to a quadratic time complexity. Corresponding runtimes are further compounded by the rapid growth of sequence databases.ResultsWe present CUDASW++ 3.0, a fast Smith-Waterman protein database search algorithm, which couples CPU and GPU SIMD instructions and carries out concurrent CPU and GPU computations. For the CPU computation, this algorithm employs SSE-based vector execution units as accelerators. For the GPU computation, we have investigated for the first time a GPU SIMD parallelization, which employs CUDA PTX SIMD video instructions to gain more data parallelism beyond the SIMT execution model. Moreover, sequence alignment workloads are automatically distributed over CPUs and GPUs based on their respective compute capabilities. Evaluation on the Swiss-Prot database shows that CUDASW++ 3.0 gains a performance improvement over CUDASW++ 2.0 up to 2.9 and 3.2, with a maximum performance of 119.0 and 185.6 GCUPS, on a single-GPU GeForce GTX 680 and a dual-GPU GeForce GTX 690 graphics card, respectively. In addition, our algorithm has demonstrated significant speedups over other top-performing tools: SWIPE and BLAST+.ConclusionsCUDASW++ 3.0 is written in CUDA C++ and PTX assembly languages, targeting GPUs based on the Kepler architecture. This algorithm obtains significant speedups over its predecessor: CUDASW++ 2.0, by benefiting from the use of CPU and GPU SIMD instructions as well as the concurrent execution on CPUs and GPUs. The source code and the simulated data are available at http://cudasw.sourceforge.net.
Highlights
The maximal sensitivity for local alignments makes the Smith-Waterman algorithm a popular choice for protein sequence database search based on pairwise alignment
We have evaluated the performance of CUDASW++ 3.0 and three other top-performing algorithms: CUDASW++ 2.0, SWIPE [19] and BLAST+ [32] using both the SwissProt protein database and a simulated database comprised of equal-length sequences
Other evaluations In addition to the performance based on hybrid CPUGPU parallelism, we have evaluated the performance of Graphics Processing Unit (GPU)-only CUDASW++ 3.0 by disabling Central Processing Unit (CPU) threads
Summary
The maximal sensitivity for local alignments makes the Smith-Waterman algorithm a popular choice for protein sequence database search based on pairwise alignment. Several heuristics such as FASTA [7] and BLAST [8,9] have been proposed to accelerate the sequence database search, but not guaranteeing to discover optimal local alignments. These heuristics usually produce considerably good results, but might fail to detect some distantly related sequences due to the loss of sensitivity. Recent acceleration approaches focus on the use of field programmable gate arrays (FPGAs), single instruction multiple data (SIMD) vector execution units on CPUs, multi-core Cell Broadband Engine (Cell/BE), and manycore general-purpose GPUs, especially based on the compute unified device architecture (CUDA)-enabled GPUs
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