Demo Data from EDBT 2026 / reviewers in the wild / expert
Shengyou Lu
dblp:402/4132
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0008-4526-9202ORCID · 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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
High-performance computing · 56% Performance modeling and evaluation · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation › benchmarking
MPI benchmarking |
0.9 | 1 | 2025 | Critique of "Productivity, Portability, Performance Data-Centric Python" by SCC Team From Sun Yat-sen University · IEEE Trans. Parallel Distributed Syst. 2025 |
High-performance computing › performance engineering
performance reproducibility |
0.9 | 1 | 2025 | Critique of "Productivity, Portability, Performance Data-Centric Python" by SCC Team From Sun Yat-sen University · IEEE Trans. Parallel Distributed Syst. 2025 |
High-performance computing
scientific computing systems |
0.3 | 1 | 2025 | Critique of "Productivity, Portability, Performance Data-Centric Python" by SCC Team From Sun Yat-sen University · IEEE Trans. Parallel Distributed Syst. 2025 |
Methods — techniques the papers use, named apart from their topics
reproducibility study · 0.9cluster benchmarking · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Critique of "Productivity, Portability, Performance Data-Centric Python" by SCC Team From Sun Yat-sen UniversityabstractIn SC21, Ziogas et al. proposed Data-Centric (DaCe) Python. It attains high performance and portability, and further extends the original productivity of Python. This paper analyzes the reproducibility of the DaCe paper as part of the SC22 Student Cluster Competition (SCC). The reproduction experiments are conducted on the Azure CycleCloud. Different from the DaCe paper, we use AMD EPYC 7V73X processors for CPU-based experiments. We successfully reproduce most of the results of the DaCe paper. The remaining results are also explainable. Tengyang Zheng, Tianxing Yang, Siran Liu, Shengyou Lu, Guangnan Feng, Zhiguang Chen 0001, Dan Huang 0001 |
IEEE Trans. Parallel Distributed Syst. | 7 |