Accurate Scanning of Sequence Databases with the Smith-Waterman Algorithm

Ligowski, Lukasz; Rudnicki, Witold R.; Liu, Yongchao; Schmidt, Bertil

doi:10.1016/b978-0-12-384988-5.00011-5

Cited by 7 publications

(10 citation statements)

References 15 publications

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“…This algorithm is able to compute the optimal local alignment score of a pair of given sequences in linear space, but has a quadratic time complexity in terms of sequence length. The quadratic runtime makes the SW algorithm computationally demanding for big sequence databases, and has therefore motivated a substantial amount of research to reduce the runtime through parallelization on highperformance computing architectures such as clusters/clouds [11] and accelerators [12]. Recent research has mainly focused on the use of the accelerators, including field programmable gate arrays (FPGAs), single instruction multiple data (SIMD) vector processing units (VPUs) on CPUs, multi-core Cell Broadband Engine (Cell/BE), and general-purpose graphics processing units (GPUs), especially compute unified device architecture (CUDA)-enabled GPUs.…”

Section: Introductionmentioning

confidence: 99%

SWAPHI: Smith-waterman protein database search on Xeon Phi coprocessors

Liu

Schmidt

2014

2014 IEEE 25th International Conference on Application-Specific Systems, Architectures and Processors

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Abstract-The maximal sensitivity of the Smith-Waterman (SW) algorithm has enabled its wide use in biological sequence database search. Unfortunately, the high sensitivity comes at the expense of quadratic time complexity, which makes the algorithm computationally demanding for big databases. In this paper, we present SWAPHI, the first parallelized algorithm employing Xeon Phi coprocessors to accelerate SW protein database search. SWAPHI is designed based on the scale-andvectorize approach, i.e. it boosts alignment speed by effectively utilizing both the coarse -grained parallelism from the many coprocessing cores (scale) and the fine-grained parallelism from the 512-bit wide single instruction, multiple data (SIMD) vectors within each core (vectorize ). By searching against the large UniProtKB/TrEMBL protein database, SWAPHI achieves a performance of up to 58.8 billion cell updates per second (GCUPS) on one coprocessor and up to 228.4 GCUPS on four coprocessors. Furthermore, it demonstrates good parallel scalability on varying numbe r of coprocessors, and is also superior to both SWIPE on 16 high-end CPU cores and BLAST+ on 8 cores when using four coprocessors, with the maximum speedup of 1.52 and 1.86, respectively. SWAPHI is written in C++ language (with a set of SIMD intrinsics), and is freely available at http://swaphi.sourceforge.net.

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Section: Introductionmentioning

confidence: 99%

SWAPHI: Smith-waterman protein database search on Xeon Phi coprocessors

Liu

Schmidt

2014

2014 IEEE 25th International Conference on Application-Specific Systems, Architectures and Processors

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“…2.1.1 Related work. Acceleration of SW has been extensively studied [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. Optimization efforts target both intra-task and intertask parallelism.…”

Section: Smith-waterman Alignmentmentioning

confidence: 99%

Performance extraction and suitability analysis of multi- and many-core architectures for next generation sequencing secondary analysis

Misra

Pan

Mahadik

et al. 2018

Proceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques

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High-throughput next generation sequencers (NGS) can rapidly read billions of short DNA fragments, called reads, at low cost. Moreover, their throughput is increasing and cost is decreasing at rates much faster than the Moore's law. This demands commensurate acceleration for NGS secondary analysis that process the reads to identify variations between genomes. Conventional architectural improvements can at best improve performance at the rate of Moore's law even if the software tools efficiently utilize the underlying architecture. Unfortunately, most of the dozens of software products developed for this purpose fail to exploit the underlying architecture well. Therefore, to match the pace of development of the sequencers, we will need architecture that is more tailored for the computational requirements of NGS secondary analysis as well as software that uses the architecture optimally. To this end, in this work, we study the performance characteristics of NGS secondary analysis and investigate the suitability of modern Intel Xeon and Xeon Phi processors for the same. To keep the study manageable, we rely on recent studies that attribute a majority of the run-time to a few key kernels. We present detailed optimization efforts to accelerate these kernels on the latest Intel Xeon and Xeon Phi processors with the goal of extracting maximum performance. A comparison of our optimized implementations, along with published results on GPGPU implementations, shows that our * Kanak Mahadik was a research intern at Intel when she worked on this project.

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“…DNA sequences of hundreds of million nucleotides). The challenge of accelerating the SW algorithm has driven a substantial amount of research to reduce the runtime by parallelizing it on high-performance computing (HPC) architectures ranging from loosely-coupled clouds/clusters [12] to tightly-coupled accelerators [13]. Among existing HPC architectures, accelerators have been the predominant technique.…”

Section: Introductionmentioning

confidence: 99%

SWAPHI-LS: Smith-Waterman Algorithm on Xeon Phi coprocessors for Long DNA Sequences

Liu

Tran

Lauenroth

et al. 2014

2014 IEEE International Conference on Cluster Computing (CLUSTER)

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As an optimal method for sequence alignment, the Smith-Waterman (SW) algorithm is widely used. Unfortunately, this algorithm is computationally demanding, especially for long sequences. This has motivated the investigation of its acceleration on a variety of high-performance computing platforms. However, most work in the literature is only suitable for short sequences. In this paper, we present SWAPHI-LS, the first parallel SW algorithm exploiting emerging Xeon Phi coprocessors to accelerate the alignment of long DNA sequences. In SWAPHI-LS, we have investigated three parallelization approaches (naïve, tiled, and distributed) in order to deeply explore the inherent parallelism within Xeon Phis. To achieve high speed, we have explored two levels of parallelism within a single Xeon Phi and one more level of parallelism between Xeon Phis. Within a single Xeon Phi we exploit instruction-level parallelism within 512-bit single instruction multiple data (SIMD) instructions (vectorization) as well as thread-level parallelism over the many cores (multithreading). Between Xeon Phis we employ device-level parallelism in order to harness the compute power of Xeon Phi clusters (distributed computing based on the MPI offload model).The performance of our algorithm has been evaluated using a variety of genome sequences of lengths ranging from 4.4 million to 50 million nucleotides. Our performance evaluation reveals that our implementation achieves a stable performance of up to 30.1 billion cell updates per second (GCUPS) on a single Xeon Phi and up to 111.4 GCUPS on four Xeon Phis sharing the same host. SWAPHI-LS is written in C++ (with a set of SIMD intrinsics), OpenMP and MPI. The source code is publicly available at http://swaphi-ls.sourceforge.net.

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Accurate Scanning of Sequence Databases with the Smith-Waterman Algorithm

Cited by 7 publications

References 15 publications

SWAPHI: Smith-waterman protein database search on Xeon Phi coprocessors

SWAPHI: Smith-waterman protein database search on Xeon Phi coprocessors

Performance extraction and suitability analysis of multi- and many-core architectures for next generation sequencing secondary analysis

SWAPHI-LS: Smith-Waterman Algorithm on Xeon Phi coprocessors for Long DNA Sequences

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