Paper Info
Title
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 Hidayetoğlu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , titled <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization”</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MemXCT</i> '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
Year
DOI
Venue
2022
10.1109/TPDS.2021.3128840
IEEE Transactions on Parallel and Distributed Systems
Keywords
DocType
Volume
Performance evaluation,random access memory,hardware,codes,graphics processing units,bandwidth,optimization
Journal
33
Issue
ISSN
Citations 
9
1045-9219
0
PageRank 
References 
Authors
0.34
0
11
Name
Order
Citations
PageRank
Xiaochen Li100.34
Maximilian Apodaca200.34
Arunav Gupta300.34
Zihao Kong400.34
Hongyi Pan500.34
Hongyu Zhou600.34
Mary Thomas700.34
Martin Kandes800.34
Zhaoyi Li900.34
Mahidhar Tatineni1000.34
Lewis Carroll1100.34