Critique of “MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization” by SCC Team From University of California San Diego

Xiaochen Li,Maximilian Apodaca,Arunav Gupta,Zihao Kong, Hongyi Pan, Hongyu Zhou,Mary Thomas,Martin Kandes,Zhaoyi Li,Mahidhar Tatineni, Lewis Carroll

IEEE Transactions on Parallel and Distributed Systems（2022）

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摘要

In this article, we describe our efforts to reproduce results reported in the SC19 article by Hidayetoğlu et 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

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关键词

Performance evaluation,random access memory,hardware,codes,graphics processing units,bandwidth,optimization

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