SPX: Toward Network Level Parallel Computing: Security, Efficiency and Scalability

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The requirement on timely processing and analysis of huge volumes of data generated by various applications in our daily lives has driven an steady but urgent need to speed up the computing power. However, due to the physical limits in transistor scaling and the cross-core interference in multicore systems, the computer processor design for large-scale parallel computing is facing its limits. A new network-level parallel computing architecture and innovative performance optimization algorithms developed in this project can free the speedup of computing power and completely break the barriers in computing processor hardware design. The new technologies resulted from this project can be widely used in many parallel computing related applications for timely analysis of big data and secure data storage. Moreover, by integrating the technological advances resulting from this project into the undergraduate/graduate curricula and outreach activities, this project has significant impacts on the training of a highly-skilled and diverse workforce for high-performance computing.The goal of this project is to achieve a theoretically-optimal speedup of computing power by leveraging the computing capability of the edge and cloud computing resources in the computing network, so as to enable efficient, scalable and secure parallel computing at the network level. More specifically, (i) this project develops new network-level parallel computing architectures and innovative performance optimization algorithms that can achieve a linear speedup of computing power by leveraging the computing capability of the edge nodes and cloud computing servers in the network; (ii) this project breaks new ground in developing secure parallel cloud computing and data storage schemes. The new techniques make it possible to implement efficient network-level parallel computing schemes without compromising the security requirements, and hence pave the path for the wide adoption of network-level parallel computing in future big-data applications.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.