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

日常生活中各种应用程序产生的海量数据需要及时处理和分析，这使得人们对计算能力的提升有着稳定而迫切的需求。然而，由于晶体管尺度的物理限制和多核系统的交叉核干扰，大规模并行计算的计算机处理器设计面临着局限性。本课题开发的新型网络级并行计算架构和创新的性能优化算法，可以解放计算能力的加速，彻底打破计算处理器硬件设计的壁垒。该项目所产生的新技术可广泛应用于许多与并行计算相关的应用中，以实现大数据的及时分析和数据的安全存储。此外，通过将该项目产生的技术进步整合到本科/研究生课程和推广活动中，该项目对高性能计算高技能和多样化劳动力的培训产生了重大影响。该项目的目标是通过利用计算网络中边缘和云计算资源的计算能力，实现理论上最优的计算能力加速，从而实现网络级高效、可扩展和安全的并行计算。更具体地说，(i)本项目开发新的网络级并行计算架构和创新的性能优化算法，通过利用网络中边缘节点和云计算服务器的计算能力，实现计算能力的线性加速；（ii）本项目在发展安全并行云计算和数据存储方案方面开辟了新天地。这些新技术使得在不影响安全要求的情况下实现高效的网络级并行计算方案成为可能，从而为在未来大数据应用中广泛采用网络级并行计算铺平了道路。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.