Research Perspectives Toward Autonomic Optimization of In Situ Analysis and Visualization
DescriptionIn situ approaches enable performing data analysis/visualization (ana/vis) close to the data source and running them on the same system. However, variations in the simulation data and the diversity of underlying HPC environments increase the difficulty of adjusting the in situ processing configurations adaptively. Triggers are an emerging strategy that follows the autonomic computing paradigm to optimize when and how to execute in situ ana/vis tasks. By inspecting indicators, the trigger can flexibly issue customized control instructions to optimize the execution of in situ ana/vis tasks in real-time. This position paper formalizes the elements of the trigger mechanism according to the definition of autonomic computing. It uses the formalization as a guideline to summarize the research status of different aspects of the trigger mechanism for in situ processing, including (1) where to execute ana/vis tasks, (2) resource allocation of ana/vis tasks, and (3) when to execute ana/vis tasks.
Event Type
TimeSunday, 13 November 20229:35am - 10am CST
Registration Categories
Accelerator-based Architectures
Data Analytics
In Situ Processing
Scientific Computing
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