Type of Document Master's Thesis Author Uliana, David Christopher Author's Email Address firstname.lastname@example.org URN etd-04252014-202538 Title FPGA-Based Accelerator Development for Non-Engineers Degree Master of Science Department Electrical and Computer Engineering Advisory Committee
Advisor Name Title Peter Athanas Committee Chair Krzysztof Kepa Committee Member Liqing Zhang Committee Member Thomas Martin Committee Member Wu-Chun Feng Committee Member Keywords
- Heterogeneous Computing
- Life Sciences
Date of Defense 2014-04-15 Availability unrestricted AbstractIn 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.
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