Intel® Parallel Computing Center at University of Stuttgart

已发布:10/13/2017   最后更新时间:12/04/2018

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Principal Investigator:

Guido Reina

Guido Reina is a postdoctoral researcher at the Visualization Research Center of the University of Stuttgart (VISUS). He defended his PhD thesis in computer science in 2008 at the University of Stuttgart, Germany. His research interests include rendering and analysis of particle-based datasets, large displays, and the optimization of parallel methods.

Description:

MegaMol was designed as a modular, GPU-centric visualization framework aimed chiefly at interactive post-mortem analysis of particle-based data sets on the personal workstations of researchers. Its flexible design allowed the core framework to reach a mature and stable state while still supporting a rapid prototyping workflow via plugins, which provide cutting-edge algorithms and novel analysis capabilities for application scenarios.

While the design rationale and narrow focus allows MegaMol to have an advantage over more general frameworks, the current trend towards ever-increasing simulation sizes has made post-mortem analysis costly and, in some scenarios, even impossible. With this Intel® Parallel Computing Center(s) (Intel® PCC) for Visualization, we aim to modernize and restructure the MegaMol architecture to scale to current data set sizes and make MegaMol capable of running headless and in situ, either on the simulation nodes or a separate rendering cluster. This means that the data flow and modular composition of a MegaMol instance need to be adapted to make use of OSPRay without additional overhead, especially with regard to data management.

We also plan to port application-specific abstractions for molecular data from MegaMol into OSPRay, to allow for more expressive visualizations. One example is the direct raytracing of solvent excluded surfaces on the basis of the original particle data and next to no overhead. Finally, we will investigate how parallelization libraries from Intel, for example the spatial data structures in Embree, can help to better utilize CPU cores for analysis and pre-processing in general.

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