The research supported by the funded computing infrastructure focuses on biomedical applications that must (i) process images or graph data of enormous size, (ii) handle data with noise and deformation, and (iii) require tremendous computational capability. One example problem being investigated is studying breast cancer brain metastasis using large 3D microscopy fluorescence images. Such biomedical applications can benefit greatly from both appropriate heterogeneous computing infrastructure and concerted effort spanning from algorithm development to effective mapping of algorithms to the computing infrastructure. Furthermore, the infrastructure will provide a platform for implementing a CS training plan of sufficient rigor for faculty, postdoctoral researchers and students to share with peer institutions, and will be integrated into multiple existing outreach programs to underrepresented minority, high school teachers and students such as the Notre Dame Summer Scholars Research Computing Track and the NSF REU Site program in Computational Science. Â A top-down, vertically integrated theme is adopted in the proposed research effort. Successful completion of the work will advance computational approaches for tackling fundamental problems in treating cancer and other diseases, and for discovering new classes of algorithms that mimic the sensory processing and reasoning abilities of biological systems. The effort will also offer powerful visualization and graph analysis techniques for processing and making inferences from large and complex image/graph data, which are essential for many biomedical and other applications. Finally, it will lead to the development of application-driven resource management mechanisms that are indispensable for effectively exploiting any GPU-based heterogeneous computing infrastructure.
