Visual interrogation of gyrokinetic particle simulations

Abstract

Abstract. Gyrokinetic particle simulations are critical to the study of anomalous energy transport associated with plasma microturbulence in magnetic confinement fusion experiments. The simulations are conducted on massively parallel computers and produce large quantities of particles, variables, and time steps, thus presenting a formidable challenge to data analysis tasks. We present two new visualization techniques for scientists to improve their understanding of the time-varying, multivariate particle data. One technique allows scientists to examine correlations in multivariate particle data with tightly coupled views of the data in both physical space and variable space, and to visually identify and track features of interest. The second technique, built into SCIRun, allows scientists to perform range-based queries over a series of time slices and visualize the resulting particles using glyphs. The ability to navigate the multiple dimensions of the particle data, as well as query individual or a collection of particles, enables scientists to not only validate their simulations but also discover new phenomena in their data.

System-Level Support for Composition of Applications

“…A bitmap indexing technique [31] is used to avoid scanning the whole particle array and multiple array chunks are merged to speed up bulk loading. The third task is to generate 1D histograms and 2D histograms on attributes of particles [17] to enable online monitoring of the running GTC simulation. 2D histograms can also be used for visualizing parallel coordinates [17] in subsequent analysis.…”

“…The third task is to generate 1D histograms and 2D histograms on attributes of particles [17] to enable online monitoring of the running GTC simulation. 2D histograms can also be used for visualizing parallel coordinates [17] in subsequent analysis. Our example focuses on integrating the 1D and 2D histograms with GTC.…”

Finding regions of interest on toroidal meshes

“…Visualizing the GTC data has been an integral part of the data analysis (Crawford et al 2004, Jones et al 2007, Klasky et al 2003. Common approaches include rendering the whole volume with transparency and displaying surface of selected regions.…”

“…Our approach provides an efficient way of selecting a subset of the data, which complements the existing visualization techniques (Post et al 2003, Jones et al 2007. After the selection process, only a smaller, presumably more interesting, volume of data is rendered or analyzed.…”

“…In published work, the Cartesian coordinates are used define how the GTC mesh points are connected to each other, typically by tessellating space using the mesh points as input (Crawford et al 2004, Jones et al 2007, Sinha et al 2009). This produces an irregular graph that connects the mesh points.…”