Memory-Driven Computing

Kimberly Keeton, Hewlett Packard Labs

Abstract: 

Data growth and data analytics requirements are outpacing the compute and storage technologies that have provided the foundation of processor-driven architectures for the last five decades. This divergence requires a deep rethinking of how we build systems, and points towards a memory-driven architecture where memory is the key resource and everything else, including processing, revolves around it. Memory-driven computing (MDC) brings together byte-addressable persistent memory, a fast memory fabric, task-specific processing, and a new software stack to address these data growth and analysis challenges. At Hewlett Packard Labs, we are exploring MDC hardware and software design through The Machine. This talk will review the trends that motivate MDC, illustrate how MDC benefits applications, provide highlights from our Machine-related work in data management and programming models, and outline challenges that MDC presents for the FAST community.

Kimberly Keeton, Hewlett Packard Labs

Dr. Kimberly Keeton is a Distinguished Technologist at Hewlett Packard Labs. She holds a Ph.D. and an M.S. in Computer Science from the University of California, Berkeley, and a B.S. in Computer Engineering and Engineering and Public Policy from Carnegie Mellon University. Her recent research is in the areas of NVM-aware data stores and data analytics frameworks. She has also worked in the areas of storage and information management, NoSQL databases, storage dependability, intelligent storage, and workload characterization. She was a co-architect of the Express Query database, which provides metadata services for HPE's StoreAll archiving solution. She is an ACM Distinguished Scientist and a Senior Member of the IEEE, and has served as Technical Program Committee Chair for multiple USENIX, ACM, IEEE and IFIP sponsored conferences.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

BibTeX
@conference {201588,
author = {Kimberly Keeton},
title = {Memory-Driven Computing},
year = {2017},
address = {Santa Clara, CA},
publisher = {{USENIX} Association},
month = feb,
}

Presentation Audio