Demo Data from SIGIR 2026 / reviewers in the wild / expert
Guanbin Xu
dblp:308/1966
· DBLP profile ↗
3ranked-venue papers
1as first author
3since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2 · 2 since 2021Computer networks · 1 · 1 first-author · 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
3 papers |
Distributed systems · 49% Parallel and multicore computing · 32% High-performance computing · 19% | |
| Artificial intelligence
2 papers |
Efficient and distributed learning · 54% Deep learning architectures and training · 46% |
Topics — the 5 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Parallel and multicore computing › parallel computing › parallel machine learning
distributed training and inference |
1.5 | 2 | 2024 | Efficient Schedule Construction for Distributed Execution of Large DNN Models · IEEE Trans. Parallel Distributed Syst. 2024 Tessel: Boosting Distributed Execution of Large DNN Models via Flexible Schedule Search · HPCA 2024 |
High-performance computing
collective communication |
0.9 | 1 | 2025 | AutoCCL: Automated Collective Communication Tuning for Accelerating Distributed and Parallel DNN Training · NSDI 2025 |
Distributed systems › distributed machine learning
distributed deep learning |
0.8 | 1 | 2024 | Efficient Schedule Construction for Distributed Execution of Large DNN Models · IEEE Trans. Parallel Distributed Syst. 2024 |
Distributed systems › distributed scheduling
operator placement |
0.8 | 1 | 2024 | Tessel: Boosting Distributed Execution of Large DNN Models via Flexible Schedule Search · HPCA 2024 |
Machine learning › Efficient and distributed learning
distributed training |
0.3 | 1 | 2025 | AutoCCL: Automated Collective Communication Tuning for Accelerating Distributed and Parallel DNN Training · NSDI 2025 |
Methods — techniques the papers use, named apart from their topics
schedule completion · 1.5repetend construction · 1.5schedule search · 0.8repetend-based search · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | AutoCCL: Automated Collective Communication Tuning for Accelerating Distributed and Parallel DNN Training
Guanbin Xu, Zhihao Le, Yinhe Chen, Zewen Jin, Youshan Miao, Cheng Li 0001 |
NSDI | 1 |
| 2024 | Tessel: Boosting Distributed Execution of Large DNN Models via Flexible Schedule SearchabstractIncreasingly complex and diverse deep neural network (DNN) models necessitate distributing the execution across multiple devices for training and inference tasks, and also require carefully planned schedules for performance. However, existing practices often rely on predefined schedules that may not fully exploit the benefits of emerging diverse model-aware operator placement strategies. Handcrafting high-efficiency schedules can be challenging due to the large and varying schedule space. This paper presents Tessel, an automated system that searches for efficient schedules for distributed DNN training and inference for diverse operator placement strategies. To reduce search costs, Tessel leverages the insight that the most efficient schedules often exhibit repetitive pattern (repetend) across different data inputs. This leads to a two-phase approach: repetend construction and schedule completion. By exploring schedules for various operator placement strategies, Tessel significantly improves both training and inference performance. Experiments with representative DNN models demonstrate that Tessel achieves up to 5.5 x training performance speedup and up to 38 % inference latency reduction. Youshan Miao, Guanbin Xu, Cheng Li 0001, Olli Saarikivi, Saeed Maleki, Fan Yang 0024 |
HPCA | 3 |
| 2024 | Efficient Schedule Construction for Distributed Execution of Large DNN ModelsabstractIncreasingly complex and diverse deep neural network (DNN) models necessitate distributing the execution across multiple devices for training and inference tasks, and also require carefully planned schedules for performance. However, existing practices often rely on predefined schedules that may not fully exploit the benefits of emerging diverse model-aware operator placement strategies. Handcrafting high-efficiency schedules can be challenging due to the large and varying schedule space. This paper presents Tessel, an automated system that searches for efficient schedules for distributed DNN training and inference for diverse operator placement strategies. To reduce search costs, Tessel leverages the insight that the most efficient schedules often exhibit repetitive pattern (repetend) across different data inputs. This leads to a two-phase approach: repetend construction and schedule completion. By exploring schedules for various operator placement strategies, Tessel significantly improves both training and inference performance. Experiments with representative DNN models demonstrate that Tessel achieves up to 5.5× training performance speedup and up to 38% inference latency reduction. Youshan Miao, Guanbin Xu, Cheng Li 0001, Olli Saarikivi, Saeed Maleki, Fan Yang 0024 |
IEEE Trans. Parallel Distributed Syst. | 3 |