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
∙ Regular Submission Ends  15 Aug
∙ FastTrack Conference Proceedings Manuscripts Due 16 Dec 
∙ All Outstanding Proceedings Manuscripts Due
 6 Feb 2023
Registration Opens late summer
Demonstration Applications Due 9 Dec
Early Registration Ends 18 Dec


2023
Hotel Reservation Deadline 6 Jan
Symposium begins
15 Jan

High Performance Computing for Imaging 2023 (HPCI)

Conference keywords: 2D/3D imaging, high performance computing, imaging systems, efficient computations and storage

On this page

ATTENTION: EI 2023 will occur IN-PERSON. 

Conference Overview

In recent years, the rapid development of imaging systems and the growth of compute-intensive imaging algorithms have led to a strong demand for High Performance Computing (HPC) for efficient image processing. However, the two communities, imaging and HPC, have largely remained separate, with little synergy. This conference focuses on research topics that converge HPC and imaging research with an emphasis on advanced HPC facilities and techniques for imaging systems/algorithms and applications. In addition, the conference provides a unique platform that brings imaging and HPC people together and discusses emerging research topics and techniques that benefit both the HPC and imaging community. Papers are solicited on all aspects of research, development, and application of high-performance computing or efficient computing algorithms and systems for imaging applications.

2023 Conference Topics

Algorithms and Methodologies

  • Large-scale imaging algorithms on distributed systems (e.g. supercomputers, clusters, and clouds)
  • Efficient computational imaging algorithms using a variant of accelerators such as CPU, GPU (Graphics Processing Unit), and FPGA.(Field-Programmable Gate Array)
  • Imaging algorithms for hybrid and heterogeneous computing systems
  • High performance computing and parallel computing for image processing
  • AI (Artificial Intelligence) optimized imaging algorithms

Architecture

  • Architectural support for large-scale imaging applications with parallel computing
  • Architectural support for rapid imaging applications with limited computing resources such as edge devices

Data Storage and Informatics Systems

  • Scalable and structured storage for imaging applications
  • Data reduction/compression on HPC and clouds for imaging sensor data
  • The next-generation informatics system for medical imaging

Post-MOORE Computing for Imaging

  • Quantum computing for imaging applications
  • Beyond von-Neumann computer architectures for imaging systems

Applications

Massively parallel algorithms for imaging applications, or compute efficient imaging algorithms with high memory throughputs and high computation throughputs on a small number of CPUs or GPUs, for the following applications:

  • Additive manufacturing imaging
  • Biomedical image reconstruction and image analyses
  • X-ray and electron microscopy
  • Neutron imaging
  • Geophysical imaging
  • Security and surveillance imaging
  • Computational photography
  • Scientific imaging for material science and biomedical science
 

2023 Committee

Conference Chairs

Xiao Wang, Oak Ridge National Laboratory (United States)
Peng Chen, The National Institute of Advanced Industrial Science and Technology (Japan)
Yuankai Huo, Vanderbilt University (United States)

Program Committee

Shunxing Bao, Vanderbilt University (United States)
Tekin Bicer, Argonne National Laboratory (United States)
Ana Gainaru, Oak Ridge National Laboratory (United States)
Hongyang Sun, University of Kansas (United States)
Singanallur Venkatakrishnan, Oak Ridge National Laboratory (United States)
Mohamed Wahib, RIKEN Center for Computational Science (Japan)
Lipeng Wan, Georgia State University (United States)
Yuhao Zhu, University of Rochester (United States)

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