Today, enterprises are increasingly looking inward at their huge stores of under-processed or throwaway data, treating them as resources to be mined. As such, an emergent field called memory-intensive computing has ignited interest among industry and academia, largely driven by various emerging non-volatile memory technologies (NVMs). Machine Learning (ML) applications are being targeted by memory-intensive computing, leveraging unique properties of ML applications to improve their distributed performance by orders of magnitude. It is therefore highly desirable for ML applications executing in such memory intensive computing environments to be efficient, flexible and scalable. ML applications crunch a lot of data from disk drives, increasing latency due to disk access delays. This project explores and designs new techniques that let ML applications fully exploit the benefits of persistence for intermediate data in NVMs, which significantly reduces disk I/Os and hence data processing times. This project also involves curriculum development, and provides more avenues to bring women, minority, and underrepresented students into research and graduate programs.
This project focuses on an open challenge for memory intensive computing systems how to offer ML applications with high efficiency, low cost and more flexibility, especially under the heterogeneous environment. This project proposes a hybrid NVM based computing architecture with effective data sharing and communication strategy to optimize file management, resource allocation and data communication for ML applications. This research centers on two key designs: 1) a new file and data management system based on the hybrid NVM pool consisting of Byte and Block addressable devices; 2) an efficient data sharing and communication management among memories to guarantee data consistency in the hybrid NVM memory pool.
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.
