PPoSS: Planning: Data Centric Computing for Scalable Heterogeneous Memory and Storage Systems Architecture

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This award supports planning for a large-grant project addressing the scalability issue among heterogeneous memory and storage systems that are composed of many parallel memory banks and disks, specifically for big-data and deep-learning applications. Emerging big-data and learning applications significantly increase both frequency and volume of data transfers over time in computer systems, which consequently result in non-negligible contention and load-imbalance issues among large-scale parallel memory storage systems. Such concerns can derail computing scalability. To grow to the requisite scale for real-world impact, there is a need for a cross-layer approach that optimizes the entire hardware/software stack. This project supports an interdisciplinary planning process, with researchers from computer architecture, high-performance computing (HPC), security and privacy, systems, and algorithms. This project will also contribute to society through engaging under-represented groups from a Hispanic Serving Institution and research dissemination for computer science and engineering education and training.The grant will plan to build a research team with the capability to address the interdisciplinary scalability challenges to large-scale heterogeneous memory and storage systems. The PIs exploit application hints such as additional sampling flexibility at the full hardware/software stack to reduce the amount of data-transfer traffic among memory and Input/Output. This is intended to realize the ultimate objective, namely maintaining a shared-nothing architecture in low-level memory and storage and thus achieving high scalability. Specific research thrusts are: 1) conducting workload and application-characteristics studies for various applications such as autonomous driving and high-performance computing, 2) exploring an innovative way to develop an application-aware, data-centric task scheduler for parallel memory banks and disks to achieve high-scalability, 3) developing several machine-learning algorithms to enhance task scheduling, and 4) investigating the applications of homomorphic encryption for sensitive datasets in data analytics and sampling 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.