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.
2024 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
2024 Special Sessions
Computing Infrastructure for Democratizing High-Performance Imaging
Session Organizing Chair:
Yuhao Zhu, University of Rochester (United States)
This special session is dedicated to discussing what's needed in our community to enable portable, reusable, and highly efficient imaging systems, which necessarily span data, software (algorithms, library packages, profiling tools, simulators), and hardware. We will discuss the lay of the land in tools and frameworks for developing imaging algorithms, and brainstorm collectively what is needed to make developing high-performance and reusable imaging systems easier and more accessible.
Awards
One Best Paper Award
Two Best Paper Runner-Up Awards
Past winners
| 2023 |
Best Paper Award
Muralikrishnan Gopalakrishnan Meena¹, Amir K. Ziabari¹, Signanallur 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) for their work on "Physics guided machine learning for image-based material decomposition of tissues from simulated breast models with calcifications." |
| 2023 |
Best Paper Award Runner-up
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) for their work on "AI-assisted automated workflow for real-time x-ray ptychography data analysis via federated resources." |
| 2023 |
Best Paper Award Runner-up
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) for their work on "WearMask: Fast in-browser face mask detection with serverless edge computing for COVID-19." |
2024 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)