Demo Data from EDBT 2026 / reviewers in the wild / expert
Yulu Jia
dblp:115/9013
· DBLP profile ↗
2ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-authorComputer networks · 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
2 papers |
High-performance computing · 61% Distributed systems · 33% Hardware reliability and fault tolerance · 6% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed systems
distributed coordination |
0.8 | 1 | 2024 | MegaScale: Scaling Large Language Model Training to More Than 10, 000 GPUs · NSDI 2024 |
High-performance computing
fault tolerance at scale |
0.8 | 1 | 2024 | MegaScale: Scaling Large Language Model Training to More Than 10, 000 GPUs · NSDI 2024 |
High-performance computing › large-scale training
large-scale training systems |
0.8 | 1 | 2024 | MegaScale: Scaling Large Language Model Training to More Than 10, 000 GPUs · NSDI 2024 |
Hardware reliability and fault tolerance › software fault tolerance
algorithm-based fault tolerance |
0.2 | 1 | 2013 | Parallel reduction to hessenberg form with algorithm-based fault tolerance · SC 2013 |
High-performance computing › numerical linear algebra
dense linear algebra |
0.2 | 1 | 2013 | Parallel reduction to hessenberg form with algorithm-based fault tolerance · SC 2013 |
Distributed systems
fault tolerance |
0.2 | 1 | 2013 | Parallel reduction to hessenberg form with algorithm-based fault tolerance · SC 2013 |
Methods — techniques the papers use, named apart from their topics
pipeline parallelism · 0.8mixed precision training · 0.8data parallelism · 0.8diskless checkpointing · 0.2checksum · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | MegaScale: Scaling Large Language Model Training to More Than 10, 000 GPUs
Ziheng Jiang, Haibin Lin, Yinmin Zhong, Qi Huang 0001, Yangrui Chen, Zhi Zhang 0005, Yanghua Peng, Xiang Li 0067, Shibiao Nong, Yulu Jia, Sun He, Hongmin Chen, Zhihao Bai, Qi Hou, Shipeng Yan, Yiyao Sheng, Zhuo Jiang, Haohan Xu, Zhang Zhang 0003, Pengfei Nie, Leqi Zou, Sida Zhao, Zherui Liu, Xiaoying Jia 0001, Jianxi Ye, Xin Jin 0008, Xin Liu 0086 |
NSDI | 11 |
| 2013 | Parallel reduction to hessenberg form with algorithm-based fault toleranceabstractThis paper studies the resilience of a two-sided factorization and presents a generic algorithm-based approach capable of making two-sided factorizations resilient. We establish the theoretical proof of the correctness and the numerical stability of the approach in the context of a Hessenberg Reduction (HR) and present the scalability and performance results of a practical implementation. Our method is a hybrid algorithm combining an Algorithm Based Fault Tolerance (ABFT) technique with diskless checkpointing to fully protect the data. We protect the trailing and the initial part of the matrix with checksums, and protect finished panels in the panel scope with diskless checkpoints. Compared with the original HR (the ScaLAPACK PDGEHRD routine) our fault-tolerant algorithm introduces very little overhead, and maintains the same level of scalability. We prove that the overhead shows a decreasing trend as the size of the matrix or the size of the process grid increases. Yulu Jia, George Bosilca, Piotr Luszczek, Jack J. Dongarra |
SC | 1 |