CSR: Small: IO-Efficient Computer System for Graph Analytics

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As graphs are widely used to represent entities and relationships in information, social, biological, and cyber-physical systems, knowledge generated from graph analytics is crucial to the success of sciences, commerce, health care, and national security. In graph analytics systems, input/output (IO) remains the main research challenge because graph processing requires a relatively small amount of CPU cycles. This research investigates multiple facets of the IO challenge from cache, memory to disks. The proposed research delivers IO-efficient computer systems for graph analytics, advancing the state of the art in this field. The research produces a comprehensive performance analysis of graph analytics systems. A novel IO architecture for graph data and metadata is developed to efficiently utilize IO bandwidth of underlying storage devices and maximize the hit ratios in memory and caches. New designs of graph systems deliver transformative innovations in the area of big data processing. During the course of this project, undergraduate and graduate students, especially under-represented students, participate in computer systems and big data research. In addition, the PI mentors students in local high schools and collaborates with industry.