Large-scale HPC simulations with their inherent I/O bottleneck have made in situ visualization an essential approach for data analysis, although the idea of in situ visualization dates back to the golden era of coprocessing in the 1990s. In situ coupling of analysis and visualization to a live simulation circumvents writing raw data to disk for post-mortem analysis – an approach that is already inefficient for today’s very large simulation codes. Instead, with in situ visualization, data abstracts are generated that provide a much higher level of expressiveness per byte. Therefore, more details can be computed and stored for later analysis, providing more insight than traditional methods.
We encourage contributed talks on methods and workflows that have been used for large-scale parallel visualization, with a particular focus on the in situ case. Presentations on codes that closely couple numerical methods and visualization are particularly welcome. 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. Presentations on codes that closely couple numerical methods and visualization are particularly welcome. Speakers should detail frameworks used and data reductions applied. They should also indicate how these impacted the flexibility of the visualization for exploratory analysis.
For the submissions we are not only looking for success stories, but are also particularly interested in those experiments that started with a certain goal or idea in mind, but later got shattered by reality or insufficient hardware/software.
Areas of interest for WOIV include, but are not limited to:
- Techniques and paradigms for in situ visualization.
- Algorithms relevant to in situ visualization. These could include algorithms empowered by in situ visualization or algorithms that overcome limitations of in situ visualization.
- Systems implementing in situ visualization. These include both general purpose and bespoke implementations.
- Workflow management.
- Use of in situ visualization for application science or other examples of using in situ visualization.
- Performance studies of in situ systems. Comparisons between in situ systems or techniques or comparisons between in situ and alternatives (such as post hoc) are particularly encouraged.
- The impact of hardware changes on in situ visualization.
- The online visualization of experimental data.
- Reports of in situ visualization failures
- Emerging issues with in situ visualization.
Held in conjunction with ISC High Performance 2020: The Event for High Performance Computing, Networking and Storage https://www.isc-hpc.com/