Demo Data from EDBT 2026 / reviewers in the wild / expert
Eric Barton
dblp:00/1217
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems
distributed storage |
0.2 | 1 | 2016 | DAOS and friends: a proposal for an exascale storage system · SC 2016 |
High-performance computing › supercomputing
exascale computing |
0.2 | 1 | 2016 | DAOS and friends: a proposal for an exascale storage system · SC 2016 |
Storage systems › distributed storage
exascale storage system |
0.2 | 1 | 2016 | DAOS and friends: a proposal for an exascale storage system · SC 2016 |
Storage systems
metadata management |
0.1 | 1 | 2016 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | DAOS and friends: a proposal for an exascale storage systemabstractThe 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 |
SC | 6 |
| 2014 | An innovative storage stack addressing extreme scale platforms and Big Data applicationsabstractCurrent 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 |
CLUSTER | 6 |
| 1994 | Message Passing on the Meiko CS-2
Eric Barton, James Cownie, Moray McLaren |
Parallel Comput. | 1 |