Demo Data from EDBT 2026 / reviewers in the wild / expert
Emir Isman
dblp:313/3008
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-0159-6243ORCID · 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 · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing › performance engineering
performance reproducibility |
0.6 | 1 | 2022 | Critique of "MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization" by SCC Team From ETH Zürich · IEEE Trans. Parallel Distributed Syst. 2022 |
High-performance computing
scientific computing systems |
0.2 | 1 | 2022 | Critique of "MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization" by SCC Team From ETH Zürich · IEEE Trans. Parallel Distributed Syst. 2022 |
High-performance computing › scientific computing systems
X-ray CT reconstruction |
0.2 | 1 | 2022 | Critique of "MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization" by SCC Team From ETH Zürich · IEEE Trans. Parallel Distributed Syst. 2022 |
Methods — techniques the papers use, named apart from their topics
strong scaling analysis · 0.6performance benchmarking · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Critique of "MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization" by SCC Team From ETH ZürichabstractThis report analyzes the reproducibility of the paper "MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization" by Hidayetolu et al. in the cloud as part of the SC20 Virtual Student Cluster Competition (VSCC). To reproduce the results from the original work, the ETH Zrich SC20 VSCC team performed a series of CT reconstructions and performed a scaling study using three provided sinograms. All experimental runs were performed during the SC20 VSCC on an HPC cluster hosted in the Microsoft Azure CycleCloud. In this paper, we describe discrepancies in results as a factor of the differences in experiment environments and insufficient parameter tuning. We successfully reproduce the single device performance and partially reproduce the strong scaling behavior. Jan Kleine, Rahul Steiger, Simon Wachter, Emir Isman, Simon Jacob, Dario Romaniello |
IEEE Trans. Parallel Distributed Syst. | 4 |