The analysis of high-resolution images in both two and three-dimensions is becoming important for many scientific areas, such as in medicine, astronomy and engineering. Discoveries in these disciplines often require analyzing millions of images. The analysis of these images is complex and requires many steps on powerful computers. Some of these steps require looking through lots of images while some of these steps require deep analysis of each image. In many cases, these analyses have to be completed quickly, i.e. in "real-time", so that information and insights can be provided to humans as they do their work. These kinds of operations require powerful computers consisting of many different, heterogeneous but simple computing components. These components need to be configured and reconfigured so that they can efficiently work together to do these large-scale analyses. In addition, the software that controls these computers also has to be intelligently designed so that these analyses can be run on the right types of configurations. This project aims to acquire the necessary computing components and assemble such a powerful computer (named RADiCAL). Research done using RADiCAL will result in important scientific discoveries that will make us more prosperous, improve our health, and enable us to better understand the world and universe around us. Doing this research will also educate many students, including those from under-represented groups, who will become part of a highly-trained workforce capable of addressing our nation's needs long into the future.
The intellectual merit of RADiCAL is in the design a novel, high-performance, next-generation, heterogeneous, reconfigurable hardware and software stack to provide real-time interaction, analytics, machine/deep learning (ML/DL) and computing support for disciplines that involve massive observational and/or simulation data. RADiCAL will be built from commodity hardware, and designed for reconfiguration and observability. RADiCAL will enable a comprehensive research agenda on software that will facilitate rapid and flexible construction of analytics workflows and their scalable execution. Specific software research include: 1) a library with support for storage and retrieval of multi-resolution, multi-dimensional datasets, 2) scalable learning and inference modules, 3) data analytics middleware systems, and 4) context-sensitive human-in-the-loop ML models and libraries that encode domain expertise, coupling tightly with both lower level layers and the hardware components to facilitate scalable analysis and explainability. With the proposed hardware acquisition and software research, the transformative goal will be to facilitate decision-making and discovery in Computational Fluid Dynamics (CFD) and medicine (pathology). With respect to broader impacts, RADiCAL will provide a unique research, testing, and training infrastructure that will catalyze research in multiple disciplines as well as facilitate convergent research across disciplines. The advanced imaging applications and techniques for expert-assisted image analysis will be broadly applicable to other human-in-the-loop systems and have the potential to advance medicine and health. Projects that use RADiCAL will also provide unique test-beds for valuable empirical research on human-computer interaction and software engineering best practices. Well-established initiatives at The Ohio State University will facilitate the recruitment of graduate and undergraduate students from underrepresented groups for involvement in using the cyberinfrastructure. The heterogeneous and reconfigurable research instrument will be utilized to create sophisticated educational modules on how to co-design computational science experiments from the science goals to the underlying cyberinfrastructure. Tutorials and workshops will be organized at PEARC, Supercomputing and other conferences to share the research results and experience with the community.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
