Demo Data from EDBT 2026 / reviewers in the wild / expert
G. R. Withers
dblp:125/2428
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1989
—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 |
Performance modeling and evaluation · 44% High-performance computing · 44% Parallel and multicore computing · 13% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation › parallel system performance
parallel performance modeling |
0.0 | 1 | 1989 | Computing performance as a function of the speed, quantity, and cost of the processors · SC 1989 |
High-performance computing › supercomputing
supercomputing systems |
0.0 | 1 | 1989 | Computing performance as a function of the speed, quantity, and cost of the processors · SC 1989 |
Methods — techniques the papers use, named apart from their topics
performance modeling · 0.0cost-performance analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1989 | Computing performance as a function of the speed, quantity, and cost of the processorsabstractEveryone wants more computing power for their applications, and the industry has responded in two ways: first by increasing the speed of single CPU's, and second by deploying multiple processors in parallel. Much controversy exists over how best to balance processor speed against the number of processors employed. Is it better to have a single, very fast and very expensive CPU, thousands of very slow but very cheap CPU's, or is there some optimal mix in between? The value of single processors, measured in floating-point performance per dollar, is relatively easy to assess, but the corresponding value of parallel systems is obscured by the fact that applications are not generally perfectly parallel, with some loss in efficiency occurring due to sequential bottlenecks and communication overhead. M. L. Barton, G. R. Withers |
SC | 2 |