Petascale XCT: 3D Image Reconstruction with Hierarchical Communications on Multi-GPU Nodes
Best Paper & Best Student Paper Finalist
   
Publication Year:
  2020
Authors
  Mert Hidayetoglu, Tekin Bicer, Simon Garcia de Gonzalo, Bin Ren, Vincent De Andrade, Doga Gursoy, Rajkumar Kettimuthu, Ian Foster, Wen-mei Hwu
   
Published:
  In the proceedings of the 2020 ACM International Conference for High Performance Computing, Networking, Storage and Analysis. ACM (SC20)
   
Abstract:

X-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; their use, however, has been limited to small/medium datasets due to their computational requirements. In this paper, we propose a high-performance iterative reconstruction system for terabyte(s)-scale 3D volumes. Our design involves three novel optimizations: (1) optimization of (back)projection operators by extending the 2D memory-centric approach to 3D; (2) inclusion of hierarchical communications by exploiting fat-node architecture with many GPUs; (3) utilization of mixed-precision types while preserving convergence rate and quality. 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 performance.