Bertil Schmidt

CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units

BackgroundThe Smith-Waterman algorithm is one of the most widely used tools for searching biological sequence databases due to its high sensitivity. Unfortunately, the Smith-Waterman algorithm is computationally demanding, which is further compounded by the exponential growth of sequence databases. The recent emergence of many-core architectures, and their associated programming interfaces, provides an opportunity to accelerate sequence database searches using commonly available and inexpensive hardware.FindingsOur CUDASW++ implementation (benchmarked on a single-GPU NVIDIA GeForce GTX 280 graphics card and a dual-GPU GeForce GTX 295 graphics card) provides a significant performance improvement compared to other publicly available implementations, such as SWPS3, CBESW, SW-CUDA, and NCBI-BLAST. CUDASW++ supports query sequences of length up to 59K and for query sequences ranging in length from 144 to 5,478 in Swiss-Prot release 56.6, the single-GPU version achieves an average performance of 9.509 GCUPS with a lowest performance of 9.039 GCUPS and a highest performance of 9.660 GCUPS, and the dual-GPU version achieves an average performance of 14.484 GCUPS with a lowest performance of 10.660 GCUPS and a highest performance of 16.087 GCUPS.ConclusionCUDASW++ is publicly available open-source software. It provides a significant performance improvement for Smith-Waterman-based protein sequence database searches by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs.

CUDASW++ 2.0: enhanced Smith-Waterman protein database search on CUDA-enabled GPUs based on SIMT and virtualized SIMD abstractions

BackgroundDue to its high sensitivity, the Smith-Waterman algorithm is widely used for biological database searches. Unfortunately, the quadratic time complexity of this algorithm makes it highly time-consuming. The exponential growth of biological databases further deteriorates the situation. To accelerate this algorithm, many efforts have been made to develop techniques in high performance architectures, especially the recently emerging many-core architectures and their associated programming models.FindingsThis paper describes the latest release of the CUDASW++ software, CUDASW++ 2.0, which makes new contributions to Smith-Waterman protein database searches using compute unified device architecture (CUDA). A parallel Smith-Waterman algorithm is proposed to further optimize the performance of CUDASW++ 1.0 based on the single instruction, multiple thread (SIMT) abstraction. For the first time, we have investigated a partitioned vectorized Smith-Waterman algorithm using CUDA based on the virtualized single instruction, multiple data (SIMD) abstraction. The optimized SIMT and the partitioned vectorized algorithms were benchmarked, and remarkably, have similar performance characteristics. CUDASW++ 2.0 achieves performance improvement over CUDASW++ 1.0 as much as 1.74 (1.72) times using the optimized SIMT algorithm and up to 1.77 (1.66) times using the partitioned vectorized algorithm, with a performance of up to 17 (30) billion cells update per second (GCUPS) on a single-GPU GeForce GTX 280 (dual-GPU GeForce GTX 295) graphics card.ConclusionsCUDASW++ 2.0 is publicly available open-source software, written in CUDA and C++ programming languages. It obtains significant performance improvement over CUDASW++ 1.0 using either the optimized SIMT algorithm or the partitioned vectorized algorithm for Smith-Waterman protein database searches by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs.

Streaming Algorithms for Biological Sequence Alignment on GPUs

Sequence alignment is a common and often repeated task in molecular biology. Typical alignment operations consist of finding similarities between a pair of sequences (pairwise sequence alignment) or a family of sequences (multiple sequence alignment). The need for speeding up this treatment comes from the rapid growth rate of biological sequence databases: every year their size increases by a factor of 1.5 to 2. In this paper, we present a new approach to high-performance biological sequence alignment based on commodity PC graphics hardware. Using modern graphics processing units (GPUs) for high-performance computing is facilitated by their enhanced programmability and motivated by their attractive price/performance ratio and incredible growth in speed. To derive an efficient mapping onto this type of architecture, we have reformulated dynamic-programming-based alignment algorithms as streaming algorithms in terms of computer graphics primitives. Our experimental results show that the GPU-based approach allows speedups of more than one order of magnitude with respect to optimized CPU implementations.

MSAProbs: multiple sequence alignment based on pair hidden Markov models and partition function posterior probabilities

MOTIVATION Multiple sequence alignment is of central importance to bioinformatics and computational biology. Although a large number of algorithms for computing a multiple sequence alignment have been designed, the efficient computation of highly accurate multiple alignments is still a challenge. RESULTS We present MSAProbs, a new and practical multiple alignment algorithm for protein sequences. The design of MSAProbs is based on a combination of pair hidden Markov models and partition functions to calculate posterior probabilities. Furthermore, two critical bioinformatics techniques, namely weighted probabilistic consistency transformation and weighted profile-profile alignment, are incorporated to improve alignment accuracy. Assessed using the popular benchmarks: BAliBASE, PREFAB, SABmark and OXBENCH, MSAProbs achieves statistically significant accuracy improvements over the existing top performing aligners, including ClustalW, MAFFT, MUSCLE, ProbCons and Probalign. Furthermore, MSAProbs is optimized for multi-core CPUs by employing a multi-threaded design, leading to a competitive execution time compared to other aligners. AVAILABILITY The source code of MSAProbs, written in C++, is freely and publicly available from http://msaprobs.sourceforge.net.

SHREC: a short-read error correction method

MOTIVATION Second-generation sequencing technologies produce a massive amount of short reads in a single experiment. However, sequencing errors can cause major problems when using this approach for de novo sequencing applications. Moreover, existing error correction methods have been designed and optimized for shotgun sequencing. Therefore, there is an urgent need for the design of fast and accurate computational methods and tools for error correction of large amounts of short read data. RESULTS We present SHREC, a new algorithm for correcting errors in short-read data that uses a generalized suffix trie on the read data as the underlying data structure. Our results show that the method can identify erroneous reads with sensitivity and specificity of over 99% and 96% for simulated data with error rates of up to 3% as well as for real data. Furthermore, it achieves an error correction accuracy of over 80% for simulated data and over 88% for real data. These results are clearly superior to previously published approaches. SHREC is available as an efficient open-source Java implementation that allows processing of 10 million of short reads on a standard workstation.

CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions.

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.

Musket: a multistage k-mer spectrum-based error corrector for Illumina sequence data

MOTIVATION The imperfect sequence data produced by next-generation sequencing technologies have motivated the development of a number of short-read error correctors in recent years. The majority of methods focus on the correction of substitution errors, which are the dominant error source in data produced by Illumina sequencing technology. Existing tools either score high in terms of recall or precision but not consistently high in terms of both measures. RESULTS In this article, we present Musket, an efficient multistage k-mer-based corrector for Illumina short-read data. We use the k-mer spectrum approach and introduce three correction techniques in a multistage workflow: two-sided conservative correction, one-sided aggressive correction and voting-based refinement. Our performance evaluation results, in terms of correction quality and de novo genome assembly measures, reveal that Musket is consistently one of the top performing correctors. In addition, Musket is multi-threaded using a master-slave model and demonstrates superior parallel scalability compared with all other evaluated correctors as well as a highly competitive overall execution time. AVAILABILITY Musket is available at http://musket.sourceforge.net.

CUSHAW: a CUDA compatible short read aligner to large genomes based on the Burrows-Wheeler transform.

MOTIVATION New high-throughput sequencing technologies have promoted the production of short reads with dramatically low unit cost. The explosive growth of short read datasets poses a challenge to the mapping of short reads to reference genomes, such as the human genome, in terms of alignment quality and execution speed. RESULTS We present CUSHAW, a parallelized short read aligner based on the compute unified device architecture (CUDA) parallel programming model. We exploit CUDA-compatible graphics hardware as accelerators to achieve fast speed. Our algorithm uses a quality-aware bounded search approach based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini index to reduce the search space and achieve high alignment quality. Performance evaluation, using simulated as well as real short read datasets, reveals that our algorithm running on one or two graphics processing units achieves significant speedups in terms of execution time, while yielding comparable or even better alignment quality for paired-end alignments compared with three popular BWT-based aligners: Bowtie, BWA and SOAP2. CUSHAW also delivers competitive performance in terms of single-nucleotide polymorphism calling for an Escherichia coli test dataset. AVAILABILITY http://cushaw.sourceforge.net

Accelerating molecular dynamics simulations using Graphics Processing Units with CUDA

Molecular dynamics is an important computational tool to simulate and understand biochemical processes at the atomic level. However, accurate simulation of processes such as protein folding requires a large number of both atoms and time steps. This in turn leads to huge runtime requirements. Hence, finding fast solutions is of highest importance to research. In this paper we present a new approach to accelerate molecular dynamics simulations with inexpensive commodity graphics hardware. To derive an efficient mapping onto this type of computer architecture, we have used the new Compute Unified Device Architecture programming interface to implement a new parallel algorithm. Our experimental results show that the graphics card based approach allows speedups of up to factor nineteen compared to the corresponding sequential implementation.

Bio-sequence database scanning on a GPU

Protein sequences with unknown functionality are often compared to a set of known sequences to detect functional similarities. Efficient dynamic programming algorithms exist for this problem, however current solutions still require significant scan times. These scan time requirements are likely to become even more severe due to the rapid growth in the size of these databases. In this paper, we present a new approach to bio-sequence database scanning using computer graphics hardware to gain high performance at low cost. To derive an efficient mapping onto this type of architecture, we have reformulated the Smith-Waterman dynamic programming algorithm in terms of computer graphics primitives. Our OpenGL implementation achieves a speedup of approximately sixteen on a high-end graphics card over available straightforward and optimized CPU Smith-Waterman implementations