Large-scale graph analytics, the class of big data analytics that essentially explores the relationship among a vast collection of interconnected entities (e.g., "friends" in a social network), is becoming increasingly important due to its broad applicability, from machine learning to web search, precision medicine, and social sciences. However, the performance of graph processing systems is severely limited by the irregular data access patterns in graph computations. The existing solutions that have been developed for mainstream parallel computing are generally ineffective for massive, sparse real-world graphs due to the conventional computer architecture (i.e., von Neumann architecture) itself. In this project, new, fundamental methods will be explored in both theoretical and practical implementations to address this problem. It uniquely advances multiple fundamental cross-disciplinary areas in device, circuit, computer-aided design, and computer architecture and can be applied to address some of the most challenging "big data" problems ranging from fundamental research to everyday life. The research framework will be extended into an educational platform, providing a user-friendly framework for a laboratory-based curriculum and will serve the educational objectives for K-12 students, undergraduate and graduate students.
In this research, a new computing paradigm will be developed to fundamentally address the challenge in processing large-scale graphs and to achieve ultra-high computing efficiency, orders of magnitude higher in performance per watt than state-of-art mainstream computer. To this end, a holistic co-design and optimization of algorithm, software and hardware will be developed to leverage the great potential of emerging nonvolatile memory technology. A new computing model will be proposed and theoretically proven to be more efficient in runtime/area/energy than traditional von Neumann architecture in performing graph computation. Detailed micro-architectures and circuits will be designed and evaluated to best implement the proposed computing model for concept proof.
