EAGER: A Data Flow Approach to Meet the Challenges of Big Data Analytics

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The National Science Foundation uses the Early-concept Grants for Exploratory Research (EAGER) funding mechanism to support exploratory work in its early stages on untested, but potentially transformative, research ideas or approaches. This EAGER project was awarded as a result of the invitation in the Dear Colleague Letter NSF 16-080 to proposers from Historically Black Colleges and Universities to submit proposals that would strengthen research capacity of faculty at the institution. The project at Prairie View A & M University aims to implement machine learning algorithms on the data flow architecture and to conduct comprehensive performance and energy consumption studies in comparison with those of classical von Neumann computer architectures. The project outcomes can address the challenges modern computers based on the von Neumann architecture are facing to pertaining memory and power walls in the big data era. The project is the first attempt to implement machine-learning algorithms on the data flow architecture. The results of the project will demonstrate if the data flow model can be used successfully to meet the performance, energy efficiency, and scalability requirements of widely used big data analytics and machine learning applications. Once demonstrated, the work can lead to the design and implementation of better and faster high performance computers and Big Data Analytics applications. In addition, the project will also increase the research capability at Historically Black Colleges and Universities in High Performance Computing, Computer Architecture, and Big Data Analytics.This EAGER project is funded by the Directorate for Computer and Information Science and Engineering.