The world of computing has entered the multi-core age. In addition to multi-core CPUs, co-processors containing thousands of computing cores in a single chip have become popular platforms for general-purpose computing. With the aggregated computing capabilities increasing at a steep rate, computing communities are still in an early stage in developing software systems, frameworks and applications to take full advantage of these new platforms. The co-existence of several different multi-core systems, including the Graphics Processing Units (GPUs), Intel?s Many Integrated Core (MIC) cards, and Accelerated Processing Units (APUs), further complicates the issue. This, on the other hand, provides opportunities for interesting research that spans different layers of the software stack. This infrastructure will support multiple, coordinated research projects that will develop frameworks and software systems enabling a new class of applications requiring high-performance computing capabilities.
The main goal of this project is to build a computer cluster with heterogeneous, massive parallel computing capabilities to accelerate existing research and enable ground-breaking new research that shares the same need for intensive computation at the University of South Florida (USF). This project brings together eight USF investigators with research projects in several core disciplines of computer science and engineering: big data management, scientific computing, system security, hardware design, data mining, computer vision and pattern recognition. Specifically, the requested cluster supports research in: (1) design and optimization of a novel data stream management system architecture in a heterogeneous many-core hardware environment; (2) coarse-grained molecular simulation approach that allows accurate simulation of large-scale atomistic systems; (3) new system to deploy security policies that excel in both policy composition and runtime performance; (4) efficient modeling and design of energy-efficient and secure hardware systems; (5) automated interpretation of activities using pattern theory; (6) fast large scale clustering; and (7) pattern identification from biomedical image data. The intellectual merit of this project derives from the innovations of the individual projects and from the potential cross-disciplinary ideas it can germinate in the future. The infrastructure is expected to facilitate collaboration and cross-pollination of algorithms, models, representations, and data sets across individual project areas, building a collaborative network across the investigators. Furthermore, the cluster is expected to impact over a dozen application domains via on-going and planned research projects among the investigators and their collaborators throughout the USF system.
Direct benefits to education and research will also be extended to the larger community through the applied aspects of projects, teaching and training. Project results and media content of the cluster will be showcased in the popular USF Engineering EXPO event, which seeks to educate and motivate K-12 students on math, science, engineering, and technology subjects.
