EDBT 2026 Demo / reviewers in the wild / expert
Samuel Thayer
dblp:259/4845
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 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 · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Concurrent programming · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Concurrent programming › concurrency bug detection
data race detection |
0.4 | 1 | 2020 | ArcherGear: data race equivalencing for expeditious HPC debugging · PPoPP 2020 |
Parallel and multicore computing › parallel computing
parallel program debugging |
0.4 | 1 | 2020 | ArcherGear: data race equivalencing for expeditious HPC debugging · PPoPP 2020 |
Parallel and multicore computing › parallel programming models › directive-based programming
OpenMP |
0.1 | 1 | 2020 | ArcherGear: data race equivalencing for expeditious HPC debugging · PPoPP 2020 |
Parallel and multicore computing › parallel programming models
shared-memory parallelization |
0.1 | 1 | 2020 | ArcherGear: data race equivalencing for expeditious HPC debugging · PPoPP 2020 |
Methods — techniques the papers use, named apart from their topics
data race equivalencing · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | ArcherGear: data race equivalencing for expeditious HPC debuggingabstractThere is growing uptake of shared memory parallelism in high performance computing, and this has increased the need for data race checking during the creation of new parallel codes or parallelizing existing sequential codes. While race checking concepts and implementations have been around for many concurrency models, including tasking models such as Cilk and PThreads (e.g., the Thread Sanitizer tool), practically usable race checkers for other APIs such as OpenMP have been lagging. For example, the OpenMP parallelization of an important library (namely Hypre) was initially unsuccessful due to inexplicable nondeterminism introduced when the code was optimized, and later root-caused to a race by the then recently developed OpenMP race checker Archer [2]. The open-source Archer now enjoys significant traction within several organizations. Samuel Thayer, Ganesh Gopalakrishnan, Ian Briggs, Michael Bentley, Dong H. Ahn, Ignacio Laguna, Gregory L. Lee |
PPoPP | 1 |