Large-scale HPC simulations with their inherent I/O bottleneck have made in situ an essential approach for data analysis. The workshop "In Situ Visualization: Introduction and Applications" provides a venue for speakers to share practical expertise and experience with in situ visualization approaches. We encourage contributed talks on methods and workflows that have been applied in this scenario.
For this 3rd edition of the workshop, we additionally encourage submissions on approaches that either did not work at all or did not live up to their expectations. We therefore expect to get first-hand reports on lessons learned. Speakers should detail if and how the application drove abstractions or other kinds of data reductions and how these interacted with the expressiveness and flexibility of the visualization for exploratory analysis or why the approach failed.
Our goal is to appeal to a wide-ranging audience of visualization scientists, computational scientists, and simulation developers, who have to collaborate in order to develop, deploy, and maintain in situ visualization approaches on HPC infrastructures. We hope to provide practical take-away techniques and insights that serve as inspiration for attendees to implement or refine in their own HPC environments and to avoid pitfalls.
Areas of interest for WOIV include, but are not limited to:
In situ infrastructures
Current Systems: production quality, research prototypes
Successful and unsuccessful approaches, dead ends
Opportunities / Gaps
System resources, hardware, and emerging architectures
Hardware and architectures that provide opportunities for in situ processing, such as burst buffers, staging computations on I/O nodes, sharing cores within a node for both simulation and in situ processing
Methods / algorithms / applications / Case studies
Analysis: feature detection, statistical methods, temporal methods, geometric methods
Visualization: information visualization, scientific visualization, time-varying methods
Data reduction / compression
Examples/case studies of solving a specific science challenge with in situ methods / infrastructure.
Integration: data modeling, software-engineering
Resilience: error detection, fault recovery
Workflows for supporting complex in situ processing pipelines
Preserve important elements
Significantly reduce the data size
Flexibility for post-processing exploration
Location: Marriott Frankfurt Hotel
Workshop registration: ISC website