CPU Volume Rendering of Adaptive Mesh Refinement Data

Wald, Ingo; Brownlee, Carson; Usher, Will; Knoll, Aaron

doi:10.1145/3139295.3139305

CPU Volume Rendering of Adaptive Mesh Refinement Data

Conference · Sun Nov 26 23:00:00 EST 2017 · SA '17: SIGGRAPH Asia 2017 Symposium on Visualization

DOI:https://doi.org/10.1145/3139295.3139305· OSTI ID:1756041

Wald, Ingo ^[1]; Brownlee, Carson ^[2]; Usher, Will ^[3]; Knoll, Aaron ^[3]

Intel Corp., Mountain View, CA (United States); University of Utah
Intel Corp., Mountain View, CA (United States)
Univ. of Utah, Salt Lake City, UT (United States)

Adaptive Mesh Refinement (AMR) methods are widespread in scientific computing, and visualizing the resulting data with efficient and accurate rendering methods can be vital for enabling interactive data exploration. In this work, we detail a comprehensive solution for directly volume rendering block-structured (Berger-Colella) AMR data in the OSPRay interactive CPU ray tracing framework. In particular, we contribute a general method for representing and traversing AMR data using a kd-tree structure, and four different reconstruction options, one of which in particular (the basis function approach) is novel compared to existing methods. Here, we demonstrate our system on two types of block-structured AMR data and compressed scalar field data, and show how it can be easily used in existing production-ready applications through a prototypical integration in the widely used visualization program ParaView.

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Research Organization:: Univ. of Utah, Salt Lake City, UT (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

DOE Contract Number:: NA0002375

OSTI ID:: 1756041

Journal Information:: SA '17: SIGGRAPH Asia 2017 Symposium on Visualization, Journal Name: SA '17: SIGGRAPH Asia 2017 Symposium on Visualization

Country of Publication:: United States

Language:: English

References (14)

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