This effort will develop a testbed that can aggressively address these three issues. The activity will leverage the extensive knowledge base available within the Center for Subsurface Sensing and Imaging Systems (CenSSIS), an ERC at Northeastern University (CenSSIS), and Engineering Research Center (ERC) to accelerate a range of key biomedical imaging applications/algorithms. The goals are: (1) develop a methodology for rapid parallelization of biomedical imaging applications by following a set of prescribed steps, and then applying best practices in GPU programming, (2) produce a rich library of parallelized biomedical imaging codes, (3) provide the capability to “right-size” a multi-GPU system to best meet the goals of any biomedical imaging application, (4) deliver these capabilities in a web-based framework that will allow a larger community to leverage the technology available in this Testbed. The project will develop a distributed Testbed where each partner will provide either biomedical imaging or GPU parallelization expertise (or both). The outcome should include a new set of parallel libraries for the biomedical research community, as well as a Testbed model that can be replicated across other research communities that require acceleration using many-core platforms.
Northeastern University’s College of Engineering is home to numerous federally-funded research centers and an array of leading-edge projects and initiatives that advance discovery and new knowledge in health, sustainability, and security.