In modern computer systems, the growing disparity in rapidly increasing computational speeds and slowly improving data transfer rates to/from off-chip memory and disk drives is a long-standing research challenge, often referred to as the Input/Output wall problem. This problem has become severe in today's big data and big compute era. Despite the rapid evolution in data storage technologies in recent years, the increasing heterogeneity and diversity in machines and workloads, coupled with the continued data explosion, exacerbate the speed gap between computing and disk-drive storage. There is an increasing need to develop a high-performance, and cost-effective architecture for emerging large-scale and diverse applications that is not affected by the Input/Output wall problem. The goal of this project is to leverage existing big compute resources such as graphic processing units (GPUs) and deep learning techniques to speed up the secondary storage system performance without adding new hardware. 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.
This project proposes to develop new GPU-enabled online learned I/O architecture using artificial intelligence. This project entails three research thrusts: First, it will develop custom machine learning and deep learning algorithms and models for the storage system. For example, it will design a temporal-aware classification technique to attack the extreme-scale learning problem. Second, it will develop new learning solutions for core storage system modules such as prefetching, log management and garbage collection. Third, it will integrate these proposed interwoven modules into a heterogeneous GPU machine and a GPU cluster at scale. It will develop optimal parallelism solutions for internal solid-state disk (SSD) devices and Non-Volatile Memory Express (NVMe) and Peripheral Connection Interface Express (PCIe) protocol to construct an express channel, moving data directly between storage and GPUs.
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.
