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Emir Isman

dblp:313/3008 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
High-performance computing › performance engineering
performance reproducibility
0.612022
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
0.212022
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
0.212022
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
YearPublicationVenuePosition
2022 Critique of "MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization" by SCC Team From ETH Zürich
abstract
This 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