k-mismatch shortest unique substring (SUS) queries have been proposed and studied very recently due to its useful 5 applications in the subfield of computational biology. The k-mismatch SUS query over one given position of a string asks for a shortest 6 substring that covers the given position and does not have a duplicate (within a Hamming distance of k) elsewhere in the string. The 7 challenge in SUS query is to collectively find the SUS for every position of a massively long string in a both time-and space-efficient 8 manner. All known efforts and results have been focused on improving and optimizing the time and space efficiency of SUS 9 computation in the sequential CPU model. In this work, we propose the first parallel approach for k-mismatch SUS queries, particularly 10 leveraging on the massive multi-threading architecture of the graphic processing unit (GPU) technology. Experimental study performed 11 on a mid-end GPU using real-world biological data shows that our proposal is consistently faster than the fastest CPU solution by a 12 factor of at least 6 for exact SUS queries (k ¼ 0) and at least 23 for approximate SUS queries over DNA sequences (k > 0), while 13 maintaining nearly the same peak memory usage as the most memory-efficient sequential CPU proposal. Our work provides 14 practitioners a faster tool for SUS finding on massively long strings, and indeed provides the first practical tool for approximate SUS 15 computation, because the any-case quadratical time cost of the state-of-the-art sequential CPU method for approximate SUS queries 16 does not scale well even to modestly long strings.
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