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DTSTAMP:20211207T055352Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20211117T083000
DTEND;TZID=America/Chicago:20211117T170000
UID:submissions.supercomputing.org_SC21_sess255_drs110@linklings.com
SUMMARY:Memory-Centric 3D Image Reconstruction with Hierarchical Communica
 tions on Multi-GPU Node Architecture
DESCRIPTION:Doctoral Showcase, Posters\n\nMemory-Centric 3D Image Reconstr
 uction with Hierarchical Communications on Multi-GPU Node Architecture\n\n
 Hidayetoglu, Hwu\n\nX-ray computed tomography is a commonly used technique
  for noninvasive imaging at synchrotron facilities. Iterative tomographic 
 reconstruction algorithms are often preferred for recovering high quality 
 3D volumetric images from 2D X-ray images, however, their use has been lim
 ited to small/medium datasets due to their computational requirements. In 
 this work, we propose a high-performance iterative reconstruction system f
 or terabyte(s)-scale 3D volumes. Our design involves three novel optimizat
 ions: (1) optimization of (back)projection operators by extending the 2D m
 emory-centric approach to 3D; (2) performing hierarchical communications b
 y exploiting “fat-node" architecture with many GPUs; (3) utilization
  of mixed-precision types while preserving convergence rate and quality. W
 e extensively evaluate the proposed optimizations and scaling on the Summi
 t supercomputer. Our largest reconstruction is a mouse brain volume with 9
 K×11K×11K voxels, where the total reconstruction time is under
  three minutes using 24,576 GPUs, reaching 65 PFLOPS: 34% of Summit’
 s peak performance.\n\nTag: In-Person Only\n\nRegistration Category: Tech 
 Program Reg Pass, Exhibit Hall Only
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