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UID:submissions.supercomputing.org_SC20_sess147_pap586@linklings.com
SUMMARY:Petascale XCT: 3D Image Reconstruction with Hierarchical Communica
 tions on Multi-GPU Nodes
DESCRIPTION:Paper\n\nPetascale XCT: 3D Image Reconstruction with Hierarchi
 cal Communications on Multi-GPU Nodes\n\nHidayetoglu, Bicer, Garcia de Gon
 zalo, Ren, De Andrade...\n\nX-ray computed tomography is a commonly used t
 echnique for noninvasive imaging at synchrotron facilities. Iterative tomo
 graphic reconstruction algorithms are often preferred for recovering high 
 quality 3D volumetric images from 2D X-ray images; their use, however, has
  been limited to small/medium datasets due to their computational requirem
 ents. In this paper, we propose a high-performance iterative reconstructio
 n system for terabyte(s)-scale 3D volumes. Our design involves three novel
  optimizations: (1) optimization of (back)projection operators by extendin
 g the 2D memory-centric approach to 3D; (2) inclusion of hierarchical comm
 unications by exploiting “fat-node” architecture with many GPUs; (3) utili
 zation of mixed-precision types while preserving convergence rate and qual
 ity. We extensively evaluate the proposed optimizations and scaling on the
  Summit supercomputer. Our largest reconstruction is a mouse brain volume 
 with 9K×11K×11K voxels, where the total reconstruction time is under three
  minutes using 24,576 GPUs, reaching 65 PFLOPS; 34% of Summit's peak perfo
 rmance.\n\nTag: Accelerators, FPGA, and GPUs, Algorithms, Applications, Sc
 alable Computing\n\nRegistration Category: Tech Program Reg Pass\n\nAward 
 Finalist: Best Paper Finalist, Best Student Paper Finalists
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