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Eric Barton

dblp:00/1217 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 3 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Storage systems · 70% High-performance computing · 30%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Storage systems
distributed storage
0.212016
DAOS and friends: a proposal for an exascale storage system · SC 2016
High-performance computing › supercomputing
exascale computing
0.212016
DAOS and friends: a proposal for an exascale storage system · SC 2016
Storage systems › distributed storage
exascale storage system
0.212016
DAOS and friends: a proposal for an exascale storage system · SC 2016
Storage systems
metadata management
0.112016
DAOS and friends: a proposal for an exascale storage system · SC 2016

Methods — techniques the papers use, named apart from their topics

performance evaluation · 0.2
YearPublicationVenuePosition
2016 DAOS and friends: a proposal for an exascale storage system
abstract
The DOE Extreme-Scale Technology Acceleration Fast Forward Storage and IO Stack project is going to have significant impact on storage systems design within and beyond the HPC community. With phase two of the project starting, it is an excellent opportunity to explore the complete design and how it will address the needs of extreme scale platforms. This paper examines each layer of the proposed stack in some detail along with cross-cutting topics, such as transactions and metadata management. This paper not only provides a timely summary of important aspects of the design specifications but also captures the underlying reasoning that is not available elsewhere. We encourage the broader community to understand the design, intent, and future directions to foster discussion guiding phase two and the ultimate production storage stack based on this work. An initial performance evaluation of the early prototype implementation is also provided to validate the presented design.
Jay F. Lofstead, Ivo Jimenez, Carlos Maltzahn, Quincey Koziol, John Bent, Eric Barton
SC6
2014 An innovative storage stack addressing extreme scale platforms and Big Data applications
abstract
Current production HPC IO stack design is unlikely to offer sufficient features and performance to adequately serve extreme scale science platform requirements as well as Big Data problems. A joint effort between the US Department of Energy's Office of Advanced Simulation and Computing and Advanced Scientific Computing Research commissioned a project to develop a design and prototype for an IO stack suitable for the extreme scale environment. It will be referred to as the Fast Forward Storage and IO (FFSIO) project. This is a joint effort led by Lawrence Livermore National Laboratory, with the DOE Data Management Nexus leads Rob Ross and Gary Grider as coordinators and contract lead Mark Gary.
Jay F. Lofstead, Ivo Jimenez, Carlos Maltzahn, Quincey Koziol, John Bent, Eric Barton
CLUSTER6
1994 Message Passing on the Meiko CS-2
Eric Barton, James Cownie, Moray McLaren
Parallel Comput.1