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Martin Kandes

dblp:270/5931 · also Martin C. Kandes, Marty Kandes · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-0721-025XORCID · 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 University of California San Diego · 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 University of California San Diego · 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 University of California San Diego · 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 University of California San Diego
abstract
In this article, we describe our efforts to reproduce results reported in the SC19 article by Hidayetoğluet al., titled“MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization”.MemXCT's single-device performance, parallelized via OpenMP and MPI, was characterized using AMD Zen2 CPU cores and NVIDIA V100 GPU devices running on the Microsoft Azure cloud. We were able to reproduce most of the results, and exceed the performance of larger inputs, on an AMD EPYC HBv2 cluster. We were also able to reproduce the strong scaling trends for optimized CPU and GPU versions. Slight variations in performance of the CPU version were observed due to differences in the underlying hardware, input size, and number of available nodes. Digital artifacts from these experiments are available at: 10.5281/zenodo.5598108
Maximilian Apodaca, Arunav Gupta, Zihao Kong, Hongyi Pan, Mary P. Thomas, Martin Kandes, Mahidhar Tatineni, Lewis Carroll
 IEEE Trans. Parallel Distributed Syst.8