EDBT 2026 Demo / reviewers in the wild / expert
Lingkun Bu
dblp:327/7614
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · unresolved
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 |
High-performance computing · 67% GPUs and heterogeneous computing · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
GPUs and heterogeneous computing
multi-GPU computing |
0.7 | 1 | 2023 | ANT-MOC: Scalable Neutral Particle Transport Using 3D Method of Characteristics on Multi-GPU Systems · SC 2023 |
High-performance computing
nuclear reactor simulation |
0.7 | 1 | 2023 | ANT-MOC: Scalable Neutral Particle Transport Using 3D Method of Characteristics on Multi-GPU Systems · SC 2023 |
High-performance computing
scientific computing systems |
0.7 | 1 | 2023 | ANT-MOC: Scalable Neutral Particle Transport Using 3D Method of Characteristics on Multi-GPU Systems · SC 2023 |
Methods — techniques the papers use, named apart from their topics
track management · 0.7multi-level load mapping · 0.7method of characteristics · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | ANT-MOC: Scalable Neutral Particle Transport Using 3D Method of Characteristics on Multi-GPU SystemsabstractThe Method Of Characteristic (MOC) to solve the Neutron Transport Equation (NTE) is the core of full-core simulation for reactors. High resolution is enabled by discretizing the NTE through massive tracks to traverse the 3D reactor geometry. However, the 3D full-core simulation is prohibitively expensive because of the high memory consumption and the severe load imbalance. To deal with these challenges, we develop ANT-MOC1. Specifically, we build a performance model for memory footprint, computation and communication, based on which a track management strategy is proposed to overcome the resolution bottlenecks caused by limited GPU memory. Furthermore, we implement a novel multi-level load mapping strategy to ensure load balancing among nodes, GPUs, and CUs. ANT-MOC enables a 3D full-core reactor simulation with 100 billion tracks on 16,000 GPUs, with 70.69% and 89.38% parallel efficiency for strong scalability and weak scalability, respectively. Shunde Li, Zongguo Wang, Lingkun Bu, Jue Wang 0013, Zhikuang Xin, Shigang Li 0002, Yangang Wang 0002, Yangde Feng, Peng Shi 0006, Xuebin Chi |
SC | 3 |