LAUSR

Hidayetoğlu et al. propose a novel memory-centric algorithm to reconstruct X-ray CT images in the SC19 article entitled “MemXCT: Memory-Centric X-ray CT Reconstruction with Massive Parallelization”. They formulate the reconstruction with several SpMVs, and propose two memory-centric optimizations to improve cache locality for better memory bandwidth utilization, i.e., a two-level pseudo-Hilbert ordering and a multi-stage input buffering. In this article, we present our results on reproducing that article to show its effectiveness and generality, as part of the SC20 Student Cluster Competition Reproducibility Challenge. We reproduce the execution time and memory bandwidth tests in that article on various architectures, including Intel CPUs, AMD CPUs, and NVIDIA GPUs. We further analyze the bottleneck on different architectures by comparing the achieved memory bandwidth with the peak bandwidth on those architectures. We then reproduce the strong scaling test on CPU and GPU clusters with different scales, and use the proposed algorithm to reconstruct three new X-ray computed tomograms.