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In the current era of big-data computing, most non-engineer domain experts lack the skills needed to design FPGA-based hardware accelerators to address big-data problems. This work presents bFlow, a development environment that facilitates the assembly of such accelerators, specifically those targeting FPGA-based hybrid computing platforms, such as the Convey HC series. This framework attempts to address the above problem by making use of an abstracted, graphical front-end more friendly to users without computer engineering backgrounds than traditional tools, as well as by accelerating bitstream compilation by means of incremental implementation techniques. bFlow's performance, usability, and application to big-data life-science problems was tested by participants of an NSF-funded Summer Institute organized by the Virginia Bioinformatics Institute (VBI). In about one week, a group of four non-engineering participants made significant modifications to a reference Smith-Waterman implementation, adding functionality and scaling theoretical throughput by a factor of 32.

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