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FPGA-Based Accelerator Development for Non-Engineers
Uliana, David Christopher
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In today's world of big-data computing, access to massive, complex data sets has reached an unprecedented level, and the task of intelligently processing such data into useful information has become a growing concern to the high-performance computing community. However, domain experts, who are the brains behind this processing, typically lack the skills required to build FPGA-based hardware accelerators ideal for their applications, as traditional development flows targeting such hardware require digital design expertise. This work proposes a usable, end-to-end accelerator development methodology that attempts to bridge this gap between domain-experts and the vast computational capacity of FPGA-based heterogeneous platforms. To accomplish this, two development flows were assembled, both targeting the Convey Hybrid-Core HC-1 heterogeneous platform and utilizing existing graphical design environments for design entry. Furthermore, incremental implementation techniques were applied to one of the flows to accelerate bitstream compilation, improving design productivity. The efficacy of these flows in extending FPGA-based acceleration to non-engineers in the life sciences was informally tested at two separate instances of an NSF-funded summer workshop, organized and hosted by the Virginia Bioinformatics Institute at Virginia Tech. In both workshops, groups of four or five non-engineer participants made significant modifications to a bare-bones Smith-Waterman accelerator, extending functionality and improving performance.
- Masters Theses