Uncertainty Estimation Techniques

With large computational simulations there is substantial uncertainty inherent in any prediction of sciencebased systems. A number of factors contribute to uncertainty, including experimental measurements, mathematical formulation, and the way different processes are coupled together in the numerical approach for simulation. Tracking of and analysis of this uncertainly is critical to any work that will truly impact the creation of future energy systems.

Large-Scale Ray Tracing Visualization

We plan on investigating ray-tracing for the analysis of large-scale data. In particularly, ray-tracing is efficient for datasets best represented by a large number of spherical glyphs such as MPM simulations such as those in Uintah as well as moleculate simulations. We will investigate the use of state of the art NUMA architectures for interactive ray-tracing of massive datasets. We will investigate the optimal acceleration structure by studying the scaling of both grid-based methods and bounding volume hierarchies.

Dynamic Task Scheduling for Scalable Parallel AMR

Uintah is a general purpose, fluid-structure interaction code has been used to characterize a wide array of physical systems and processes, examples include stage-separation in rockets, the biomechanics of microvessels the effects of wounding on heart tissue the properties of foam under large deformation, and evolution of transportation fuel fires.

Solving PDEs on GPUs

Most current streaming processors rely on a SIMD programming model in which a single kernel is applied to a very large number of data items with no data dependencies among streams. Extensions to the C language, such as CUDA, provide this SIMD capability on standard C data structures, and the interested parties seem to be converging on open standards (e.g. OpenCL) for general purpose computing on the streaming architectures. The future of high-performance computing promises a variety of new architectures that embody the streaming paradigm.

Provence Enabling Uintah

Exploration of large-scale scientific systems using computational simulations produces massive amounts of data that must be managed and analyzed. Because of the volume of data manipulated, and the complexity of the simulations and analysis workflows it is crucial to maintain detailed provenance (i.e., an audit trail) of the derived results. Provenance is necessary to ensure reproducibility as well as enable verification and validation of the simulation codes and results.

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