![]() ![]() ConclusionsĪDEPT demonstrates a performance that is either comparable or better than existing GPU strategies. Overall ADEPT shows 10x faster performance in a node-to-node comparison against a corresponding SIMD CPU implementation. We have shown that the ADEPT based Smith-Waterman algorithm demonstrates a peak performance of 360 GCUPS and 497 GCUPs for protein based and DNA based datasets respectively on a single GPU node (8 GPUs) of the Cori Supercomputer. ADEPT’s driver enables it to scale across multiple GPUs and allows easy integration into software pipelines which utilize large scale computational systems. We demonstrate the feasibility of this strategy by implementing the Smith-Waterman algorithm and comparing it to similar CPU strategies as well as the fastest known GPU methods for each domain. Our proposed strategy uses GPU specific optimizations that do not rely on the nature of sequence. In this paper, we present ADEPT, a new sequence alignment strategy for GPU architectures that is domain independent, supporting alignment of sequences from both genomes and proteins. Existing GPU based strategies have either been optimized for a specific type of characters (Nucleotides or Amino Acids) or for only a handful of application use-cases. However, with the move of HPC facilities towards accelerator based architectures, a need for an efficient GPU accelerated strategy has emerged. Multiple SIMD strategies for the Smith-Waterman algorithm are available for CPUs. With the advent of modern sequencing technologies and increasing size of both genome and protein databases, a need for faster Smith-Waterman implementations has emerged. This task is performed with the Smith-Waterman algorithm, which is a dynamic programming based method. At the core of most of these tools, a significant portion of execution time is spent in determining optimal local alignment between two sequences. Bioinformatic workflows frequently make use of automated genome assembly and protein clustering tools. ![]()
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