Critique of "MemXCT: Memory-Centric X-Ray CT Reconstruction With Massive Parallelization" by SCC Team From Nanyang Technological University

• Published in: IEEE transactions on parallel and distributed systems 2022-09, Vol.33 (9), p.2058-2061
• Main Authors: Li, Shenggui, Lee, Bu-Sung
• Format: Article
• Language: English
• Subjects: Bandwidth; Central processing units; Clusters; Computed tomography; Computer memory; CPUs; Experiments; Graphics processing units; Image reconstruction; memory; Nodes; Performance evaluation; Reconstruction; reproducible computation; SC20; Scalability; Sockets; Supercomputers; X-ray CT
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2021.3128040

In this technical report, we focus on reproducing the results reported in the paper "MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization" [1]. MemXCT is a scalable approach to X-ray Computed Tomography reconstruction which removes redundant computation. We reproduced the single CPU/GPU performance as well as strong scaling experiments. We set up our configurations on Microsoft Azure CycleCloud and have two clusters. One cluster has 4 nodes with 60 CPUs on each node and the other cluster has 4 nodes with 4 NVIDIA V100 GPUs on each node. Both clusters come with InfiniBand. The original author conducted his experiments on Theta and Blue Waters supercomputers. We were able to reproduce part of the results in the original paper, however, failed to produce similar performance on other experiments. This report was submitted as part of the reproducibility challenge in SC20 Student Cluster Competition. Digital artifacts from these experiments are available at: 10.5281/zenodo.5598108.