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

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.