Demo Data from VLDB 2026 / reviewers in the wild / expert
Mingxu Liang
dblp:401/6807
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 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 |
Storage systems · 44% Hardware accelerators and domain-specific architectures · 44% GPUs and heterogeneous computing · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Hardware accelerators and domain-specific architectures › accelerator offloading
DPU offloading |
0.9 | 1 | 2025 | OS2G: A High-Performance DPU Offloading Architecture for GPU-based Deep Learning with Object Storage · ASPLOS (2) 2025 |
Storage systems
object storage |
0.9 | 1 | 2025 | OS2G: A High-Performance DPU Offloading Architecture for GPU-based Deep Learning with Object Storage · ASPLOS (2) 2025 |
GPUs and heterogeneous computing
deep learning on GPUs |
0.3 | 1 | 2025 | OS2G: A High-Performance DPU Offloading Architecture for GPU-based Deep Learning with Object Storage · ASPLOS (2) 2025 |
Methods — techniques the papers use, named apart from their topics
object storage client offloading · 0.9DPU offloading · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | OS2G: A High-Performance DPU Offloading Architecture for GPU-based Deep Learning with Object StorageabstractObject storage is increasingly attractive for deep learning (DL) applications due to its cost-effectiveness and high scalability. However, it exacerbates CPU burdens in DL clusters due to intensive object storage processing and multiple data movements. Data processing unit (DPU) offloading is a promising solution, but naively offloading the existing object storage client leads to severe performance degradation. Besides, only offloading the object storage client still involves redundant data movements, as data must first transfer from the DPU to the host and then from the host to the GPU, which continues to consume valuable host resources. Zhen Jin 0008, Yiquan Chen, Mingxu Liang, Guoju Fang, Keyao Zhang, Jiexiong Xu, Wenhai Lin, Yiquan Lin, Shushu Zhao, Wenkai Shi, Zhenhua He, Shishun Cai, Wenzhi Chen |
ASPLOS (2) | 3 |