Demo Data from SIGIR 2026 / reviewers in the wild / expert
Qigang Yang
dblp:421/2339
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0000-2995-9213ORCID · 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.
| Artificial intelligence
1 paper |
Deep learning architectures and training · 67% Efficient and distributed learning · 33% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Parallel and multicore computing · 100% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Deep learning architectures and training
attention mechanism |
0.9 | 1 | 2025 | UltraAttn: Efficiently Parallelizing Attention through Hierarchical Context-Tiling · SC 2025 |
Machine learning › Deep learning architectures and training › attention mechanism › sparse attention
block-sparse attention |
0.9 | 1 | 2025 | UltraAttn: Efficiently Parallelizing Attention through Hierarchical Context-Tiling · SC 2025 |
Methods — techniques the papers use, named apart from their topics
context parallelism · 1.7block-sparse attention · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | UltraAttn: Efficiently Parallelizing Attention through Hierarchical Context-TilingabstractLong-context comprehension is critical for large language models. Context parallelism and irregular block-sparse attention are keyss to accelerating long-context training and inference. Existing context parallelism suffers from poor scalability due to the striped-like partition pattern, which causes high communication traffic, and the ring-based communication pattern, which limits kernel granularity, reduces device utilization, and incurs redundant communication. Zan Zong, Yuyang Jin 0001, Kinman Lei, Jiaao He, Qigang Yang, Jidong Zhai |
SC | 6 |