IMPORTANT DATES
Dates currently being confirmed; check back.
 

2022
Call for Papers Announced 2 May
Journal-first (JIST/JPI) Submissions

∙ Submission site Opens 2 May 
∙ Journal-first (JIST/JPI) Submissions Due 1 Aug
∙ Final Journal-first manuscripts due 28 Oct
Conference Papers Submissions
∙ Abstract Submission Opens 1 June
∙ Priority Decision Submission Ends 15 July
∙ Extended Submission Ends  19 Sept
∙ FastTrack Conference Proceedings Manuscripts Due 25 Dec 
∙ All Outstanding Proceedings Manuscripts Due
 6 Feb 2023
Registration Opens 1 Dec
Demonstration Applications Due 19 Dec
Early Registration Ends 18 Dec


2023
Hotel Reservation Deadline 6 Jan
Symposium begins
15 Jan


No content found

High Performance Computing for Imaging 2023 Preliminary Program

Monday 16 January 2023

AI Methods for Imaging (M1)

9:05 – 10:20 AM

Conference Welcome

HPC+AI-enabled real-time nanoscale x-ray imaging (Invited), Mathew Cherukara, Argonne National Laboratory (United States)

Physics guided machine learning for image-based material decomposition of tissues from simulated breast models with calcifications, Muralikrishnan Gopalakrishnan Meena1, Amir K. Ziabari1, Singanallur Venkatakrishnan1, Isaac R. Lyngaas1, Matthew R. Norman1, Balint Joo1, Thomas L. Beck1, Charles A. Bouman2, Anuj Kapadia1, and Xiao Wang1; 1Oak Ridge National Laboratory and 2Purdue University (United States)

WearMask: Fast in-browser face mask detection with serverless edge computing for COVID-19, Zekun Wang1, Pengwei Wang2, Peter C. Louis3, Lee E. Wheless3, and Yuankai Huo1; 1Vanderbilt University (United States), 2Shandong University (China), and 3Vanderbilt University Medical Center (United States)



Ptychographic Imaging (M2)

10:50 AM – 12:10 PM

High-performance ptychographic reconstruction using GPUs (Invited), Tekin Bicer, Argonne National Laboratory (United States)

Image gradient decomposition for parallel and memory-efficient ptychographic reconstruction (Invited), Xiao Wang, Oak Ridge National Laboratory (United States)

AI-assisted automated workflow for real-time x-ray ptychography data analysis via federated resources, Anakha V Babu, Tekin Bicer, Saugat Kandel, Tao Zhou, Daniel J. Ching, Steven Henke, Sinisa Veseli, Ryan Chard, Antonio Miceli, and Mathew Cherukara, Argonne National Laboratory (United States)



Tuesday 17 January 2023

KEYNOTE: High-Performance Imaging (T1)

8:50 – 10:20 AM

KEYNOTE: Reducing the barriers to high performance imaging, Charles A. Bouman, Purdue University (United States)

High-performance embedded imaging: An optics, sensor, and computing co-design approach (Invited), Yuhao Zhu, University of Rochester (United States)




X-ray Scatter and MR Imaging (T2)

10:50 AM – 12:10 PM

A system for large-scale inverse multiple-scattering imaging on GPU supercomputers with real data (Invited), Mert Hidayetoglu1,2; 1Stanford University and 2SLAC National Accelerator Laboratory (United States)

Fast massively parallel physics-based algorithm for modeling multi-order scatter in CT (Invited), Venkatesh Sridhar1, Xin Liu1, and Kyle Champley2; 1Lawrence Livermore National Laboratory and 2Ziteo Medical (United States)

Clinical validation of rapid GPU-enabled DTI tractography of the brain, Felix Liu1, Vanitha Sankaranarayanan1, Javier Villanueva-Meyer1, Shawn Hervey-Jumper1, James Hawkins1, Pablo Damasceno2, Mauro Bisson3, Josh Romero3, Thorsten Kurth3, Massimiliano Fatica3, Eleftherios Garyfallidis4, Ariel Rokem5, Jason Crane1, and Sharmila Majumdar1; 1University of California, San Francisco, 2Janssen Pharmaceutical, 3NVIDIA Corporation, 4Indiana University, and 5University of Washington (United States)



High Performance Tomographic Reconstruction (T3)

3:30 – 5:30 PM

Training end-to-end unrolled iterative neural networks for SPECT image reconstruction: A fast and memory efficient Julia toolbox (Invited), Zongyu Li, Yuni K. Dewaraja, and Jeff Fessler, University of Michigan (United States)

Fast GPU-based tomographic reconstruction with efficient data transfers between CPU, GPU, and NVMe SSDs (Invited), Viktor Nikitin, Argonne National Laboratory (United States)

Hierarchical communications for 3D image reconstruction with synchrotron light source and 24,576 GPUs (Invited), Mert Hidayetoglu1,2; 1Stanford University and 2SLAC National Accelerator Laboratory (United States)

High-performance image reconstruction on GPU-accelerated supercomputers (Invited), Peng Chen1, Mohamed Wahib2, Xiao Wang3, Jintao Meng4, and Yusuke Tanimura1; 1Japan National Lab (AIST) (Japan), 2RIKEN Center for Computational Science (Japan), 3Oak Ridge National Laboratory (United States), and 4Shenzhen Institute of Advanced Technology, CAS (China)



Wednesday 18 January 2023

PANEL: High-Performance Computing in Imaging: from Academia to Industry (W1)

Panelists: Yuankai Huo, Vanderbilt University (United States); Yucheng Tang, NVIDIA Corporation (United States); and Xiao Wang, Oak Ridge National Laboratory (United States)
9:10 – 10:10 AM



AI Acceleration & System Design (W2)

10:50 AM – 12:30 PM

TVM enabled automatic kernel generation for irregular GEMM optimization on ARM architectures (Invited), Du Wu1,2, Chen Zhuang2, Haidong Lan3, Wenxi Zhu3, Minwen Deng3, Peng Chen4, Mohamed Wahib5, Jintao Meng2, Bingqiang Wang6, Yanjie Wei2, Yi Pan2, and Shengzhong Feng7; 1Southern University of Science and Technology of China, 2Shenzhen Institue of Advanced Technology, Chinese Academy of Science, 3Tencent AI Lab, Shenzhen, China, 4Japan National Lab (AIST), 5RIKEN Center for Computational Science (Japan), 6Peng Cheng Laboratory, Shenzhen, China, and 7National Supercomputer Center in Shenzhen, China (China)

Towards real-time formula driven dataset feed for large scale deep learning training, Edgar Josafat Martinez Noriega1 and Rio Yokota2; 1National Institute of Advanced Industrial Science and Technology and 2Tokyo Institute of Technology (Japan)

Algorithmic enhancements to data colocation grid frameworks for big data medical image processing (Invited), Shunxing Bao, Vanderbilt University (United States)

Bridging the gap between high-performance computing and high-performance imaging applications, Mohamed Wahib, RIKEN Center for Computational Science (Japan)



No content found

No content found