Kilian Werner(AG Scientific Visualization, Prof. Garth)
hosted by PhD Program in CS @ TU KL
"Task-Based Visualization Methods for Scalable Analysis of Large Data"
With increasing size and complexity of measurements and simulations of scientific phenomenons, scientific visualization and visual analysis are becoming ever more important tools for scientific data exploration, understanding and interpretation. At the same time well-established methods and implementations in this field are failing to perform on this very increased size and complexity of data within feasible runtime and memory constraints. Efforts to scale these methods through parallelism and onto distributed memory systems, accelerators and clusters therefore received increasing attention recently, but usually stayed at a machine-oriented low level which requires expensive custom implementations for each individual application. In this project, it is evaluated how well-established methods in visualizing scientific data can be tailored to and implemented in a high-level abstraction to formulating parallel and distributed algorithms: the task-parallel ParallaX paradigm. The ultimate goal of these efforts is to concept-proof a portable because high-level algorithmic structure for a task-parallel, scalable pipeline of common scientific visualization methods for large and complex data.
Bio: In this talk I will explain firstly one of these methods on which work has already started namely the contour tree construction, secondly the ParallaX paradigm and its implementation in the HPX-Framework and finally my ongoing efforts to reinvent, tailor and implement the former within the latter.
|Time:||Tuesday, 04.06.2019, 13:45|