{'fraction_non_alphanumeric': 0.018437225636523266, 'fraction_numerical': 0.0, 'mean_word_length': 5.514285714285714, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Design-Space Exploration of Systolic Arrays for Biosequence Algorithms ( Invited Paper )

Next-generation sequencing techologies have dramatically increased the amount of new DNA and protein sequence being produced by biologists. These vast and growing data volumes challenge existing computational approaches to analyzing experimentally produced biosequences. One way to make very large analyses practical is to design customized computational accelerators to implement them on, e.g., FPGAs or chip multiprocessors. However, the cognitive burden associated with programming these devices limits their utility for bioinformaticians. In this abstract, we describe a high-level approach to the design of accelerators for biosequence analyses based on dynamic programming. These computations can be described at the level of recurrences, for which we can find the best mapping to a given hardware platform using the tools of polyhedral analysis. We recount recent advances by our group in automated design of efficient accelerators from recurrences and describe a vision for an end-to-end suite of tools for bioinformatics accelerator design.