Demo Data from SIGIR 2026 / reviewers in the wild / expert
James L. Tomkins
dblp:25/2950
· DBLP profile ↗
4ranked-venue papers
1as first author
0since 2021 · last 2007
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 4 · 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 |
Parallel and multicore computing · 70% Embedded and real-time systems · 30% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Parallel and multicore computing › multiprocessor system › MIMD multiprocessor
massively parallel MIMD |
0.0 | 1 | 1991 | Massively parallel computing and the mid-course tracking problem · SC 1991 |
Parallel and multicore computing › parallel computing
parallel scientific computing |
0.0 | 1 | 1991 | Massively parallel computing and the mid-course tracking problem · SC 1991 |
Embedded and real-time systems › real-time computer vision
real-time tracking |
0.0 | 1 | 1991 | Massively parallel computing and the mid-course tracking problem · SC 1991 |
Parallel and multicore computing
parallel program transformation |
0.0 | 1 | 1991 | Massively parallel computing and the mid-course tracking problem · SC 1991 |
Methods — techniques the papers use, named apart from their topics
trace-driven simulation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | Red storm IO performance analysisabstractThis paper will summarize an IO1performance analysis effort performed on Sandia National Laboratories Red Storm platform. Our goal was to examine the IO system performance and identify problems or bottle-necks in any aspect of the IO sub-system. Our process examined the entire IO path from application to disk both in segments and as a whole. Our final analysis was performed at scale employing parallel IO access methods typically used in High Performance Computing applications. James H. Laros III, Lee Ward, Ruth Klundt, Sue Kelly, James L. Tomkins, Brian R. Kellogg |
CLUSTER | 5 |
| 2005 | Architectural specification for massively parallel computers: an experience and measurement-based approachabstractAbstract In this paper, we describe the hardware and software architecture of the Red Storm system developed at Sandia National Laboratories. We discuss the evolution of this architecture and provide reasons for the different choices that have been made. We contrast our approach of leveraging high‐volume, mass‐market commodity processors to that taken for the Earth Simulator. We present a comparison of benchmarks and application performance that support our approach. We also project the performance of Red Storm and the Earth Simulator. This projection indicates that the Red Storm architecture is a much more cost‐effective approach to massively parallel computing. Published in 2005 by John Wiley & Sons, Ltd. Ron Brightwell, William J. Camp, Benjamin Cole, Erik DeBenedictis, Robert W. Leland, James L. Tomkins, Arthur B. Maccabe |
Concurr. Pract. Exp. | 6 |
| 1999 | Massively parallel computing: A Sandia perspective
David E. Womble, Sudip S. Dosanjh, Bruce Hendrickson, Michael A. Heroux, Steven J. Plimpton, James L. Tomkins, David S. Greenberg |
Parallel Comput. | 6 |
| 1991 | Massively parallel computing and the mid-course tracking problemabstractThe Strategic Defense Initiative (SDI) mid-course tracking problem is a very computationally intensive problem that must be solved in real time.It is generally believed that a real-time solution is only possible through massively parallel computing.This paper discusses tti implementation of the TRC code, a mid-course tracking code, on a 1024 processor nCUBEYten computez A brief discussion of the modeling approach used in the TRC is provided together with a discussion of how the ZRC was restructured to make it run e~iently in parallel.The penjormance of the parallel version of the TRC over a wide range of problem sizes is discussed and results obtained on a Cray X-MP single processor are compared to the nCUBE/ten results.These results demonstrate the applicability of massively parallel MIMD computing to the SDI mid-course tracking problem.1.0 James L. Tomkins, J. P. VanDyke |
SC | 1 |