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Data staging and in-situ/in-transit data processing are emerging as attractive approaches for supporting extreme scale scientific workflows. These approaches improve end-to-end performance by enabling runtime data sharing between coupled simulations and data analytics components of the workflow. However, the complex and dynamic data exchange patterns exhibited by the workflows coupled with the varied...
With the onset of extreme-scale computing, I/O constraints make it increasingly difficult for scientists to save a sufficient amount of raw simulation data to persistent storage. One potential solution is to change the data analysis pipeline from a post-process centric to a concurrent approach based on either in-situ or in-transit processing. In this context computations are considered in-situ if...
Simulations of turbulent flames have used particles to capture the dynamic behavior of combustion in next-generation engines. Each particle includes a history of its movement positions and changing thermochemical states. Analyzing such a set of many millions of particles helps scientists understand turbulence. A dual-space method enables effective visual analysis of both the spatial movement and attribute...
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