Demo Data from EDBT 2026 / reviewers in the wild / expert
Xirui Shui
dblp:421/2328
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0007-8867-3718ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021
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 · 77% Parallel and multicore computing · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation
trace analysis |
0.9 | 1 | 2025 | TraceFlow: Efficient Trace Analysis for Large-Scale Parallel Applications via Interaction Pattern-Aware Trace Distribution · SC 2025 |
Parallel and multicore computing › parallel computing › parallel applications
large-scale parallel applications |
0.3 | 1 | 2025 | TraceFlow: Efficient Trace Analysis for Large-Scale Parallel Applications via Interaction Pattern-Aware Trace Distribution · SC 2025 |
Methods — techniques the papers use, named apart from their topics
trace distribution · 0.9parallel trace analysis · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | TraceFlow: Efficient Trace Analysis for Large-Scale Parallel Applications via Interaction Pattern-Aware Trace DistributionabstractTrace analysis of large-scale parallel applications is crucial for understanding and optimizing performance. It primarily focuses on the interaction behaviors between different parallel processes, such as synchronization waits and asynchronous overlaps. The trace size explodes as the parallel scale of applications, thus current methods analyze traces in parallel to ensure analysis speed. However, due to the interaction pattern-agnostic trace distribution, they often introduce inter-process communications to fetch non-local event data during interaction analysis, leading to excessively long trace analysis time. Yuyang Jin 0001, Xirui Shui, Mingshu Zhai, Zan Zong, Feng Zhang 0007, Felix Wolf 0001, Jidong Zhai |
SC | 2 |