Demo Data from EDBT 2026 / reviewers in the wild / expert
Richard Neill
dblp:97/8109
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2017
—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 |
Performance modeling and evaluation · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation › performance monitoring
hardware performance counters |
0.3 | 1 | 2017 | Fuse: Accurate Multiplexing of Hardware Performance Counters Across Executions · ACM Trans. Archit. Code Optim. 2017 |
Performance modeling and evaluation
profiling |
0.3 | 1 | 2017 | Fuse: Accurate Multiplexing of Hardware Performance Counters Across Executions · ACM Trans. Archit. Code Optim. 2017 |
Performance modeling and evaluation
benchmarking |
0.1 | 1 | 2017 | Fuse: Accurate Multiplexing of Hardware Performance Counters Across Executions · ACM Trans. Archit. Code Optim. 2017 |
Methods — techniques the papers use, named apart from their topics
statistical distribution comparison · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Fuse: Accurate Multiplexing of Hardware Performance Counters Across ExecutionsabstractCollecting hardware event counts is essential to understanding program execution behavior. Contemporary systems offer few Performance Monitoring Counters (PMCs), thus only a small fraction of hardware events can be monitored simultaneously. We present new techniques to acquire counts for all available hardware events with high accuracy by multiplexing PMCs across multiple executions of the same program, then carefully reconciling and merging the multiple profiles into a single, coherent profile. We present a new metric for assessing the similarity of statistical distributions of event counts and show that our execution profiling approach performs significantly better than Hardware Event Multiplexing. Richard Neill, Andi Drebes, Antoniu Pop |
ACM Trans. Archit. Code Optim. | 1 |