Demo Data from VLDB 2026 / reviewers in the wild / expert
Stefan P. Feer
dblp:141/0401
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
—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 · 61% High-performance computing · 30% Performance modeling and evaluation · 9% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Parallel and multicore computing
non-contiguous allocations |
0.2 | 1 | 2014 | Task mapping stencil computations for non-contiguous allocations · PPoPP 2014 |
High-performance computing
stencil computation |
0.2 | 1 | 2014 | Task mapping stencil computations for non-contiguous allocations · PPoPP 2014 |
Parallel and multicore computing
task allocation |
0.2 | 1 | 2014 | Task mapping stencil computations for non-contiguous allocations · PPoPP 2014 |
Methods — techniques the papers use, named apart from their topics
task mapping algorithms · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Task mapping stencil computations for non-contiguous allocationsabstractWe examine task mapping algorithms for systems that allocate jobs non-contiguously. Several studies have shown that task placement affects job running time. We focus on jobs with a stencil communication pattern and use experiments on a Cray XE to evaluate novel task mapping algorithms as well as some adapted to this setting. This is done with the miniGhost miniApp which mimics the performance of CTH, a shock physics application. Our strategies improve average and single-run times by as much as 28% and 36% over a baseline strategy, respectively. Vitus J. Leung, David P. Bunde, Jonathan Ebbers, Stefan P. Feer, Nickolas W. Price, Zachary D. Rhodes, Matthew Swank |
PPoPP | 4 |