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Large-Scale Pairwise Alignments on GPU Clusters: Exploring the Implementation Space

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Abstract

Several problems in computational biology require the all-against-all pairwise comparisons of tens of thousands of individual biological sequences. Each such comparison can be performed with the well-known Needleman-Wunsch alignment algorithm. However, with the rapid growth of biological databases, performing all possible comparisons with this algorithm in serial becomes extremely time-consuming. The massive computational power of graphics processing units (GPUs) makes them an appealing choice for accelerating these computations. As such, CPU-GPU clusters can enable all-against-all comparisons on large datasets. In this work, we present four GPU implementations for large-scale pairwise sequence alignment: TiledDScan-mNW, DScan-mNW, RScan-mNW and LazyRScan-mNW. The proposed GPU kernels exhibit different parallelization patterns: we discuss how each parallelization strategy affects the memory accesses and the utilization of the underlying GPU hardware. We evaluate our implementations on a variety of low- and high-end GPUs with different compute capabilities. Our results show that all the proposed solutions outperform the existing open-source implementation from the Rodinia Benchmark Suite, and LazyRScan-mNW is the preferred solution for applications that require performing the trace-back operation only on a subset of the considered sequence pairs (for example, the pairs whose alignment score exceeds a predefined threshold). Finally, we discuss the integration of the proposed GPU kernels into a hybrid MPI-CUDA framework for deployment on CPU-GPU clusters. In particular, our proposed distributed design targets both homogeneous and heterogeneous clusters with nodes that differ amongst themselves in their hardware configuration.

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Acknowledgments

We thank the reviewers for their feedback. This work has been supported by NSF award CNS-1216756 and by equipment donations from Nvidia Corporation.

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Authors and Affiliations

  1. MU Informatics Institute, University of Missouri, Columbia, USA

    Huan Truong, Gavin Conant & Michela Becchi

  2. Department of Electrical and Computer Engineering, University of Missouri, Columbia, USA

    Da Li, Kittisak Sajjapongse & Michela Becchi

  3. Division of Animal Sciences, University of Missouri, Columbia, USA

    Gavin Conant

Authors
  1. Huan Truong
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  2. Da Li
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  3. Kittisak Sajjapongse
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  4. Gavin Conant
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  5. Michela Becchi
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Correspondence to Michela Becchi.

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Truong, H., Li, D., Sajjapongse, K. et al. Large-Scale Pairwise Alignments on GPU Clusters: Exploring the Implementation Space. J Sign Process Syst 77, 131–149 (2014). https://doi.org/10.1007/s11265-014-0883-2

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  • DOI: https://doi.org/10.1007/s11265-014-0883-2

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