Demo Data from VLDB 2026 / reviewers in the wild / expert
J. P. VanDyke
dblp:60/5452
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1991
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1
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 |
|---|---|---|---|
| 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 | 2 |