Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Xirui Shui

dblp:421/2328 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Performance modeling and evaluation
trace analysis
0.912025
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
0.312025
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
YearPublicationVenuePosition
2025 TraceFlow: Efficient Trace Analysis for Large-Scale Parallel Applications via Interaction Pattern-Aware Trace Distribution
abstract
Trace 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
SC2