Eric Brugger

A contract based system for large data visualization

VisIt is a richly featured visualization tool that is used to visualize some of the largest simulations ever run. The scale of these simulations requires that optimizations are incorporated into every operation VisIt performs. But the set of applicable optimizations that VisIt can perform is dependent on the types of operations being done. Complicating the issue, VisIt has a plugin capability that allows new, unforeseen components to be added, making it even harder to determine which optimizations can be applied. We introduce the concept of a contract to the standard data flow network design. This contract enables each component of the data flow network to modify the set of optimizations used. In addition, the contract allows for new components to be accommodated gracefully within VisIts data flow network system.

BOUT++: Recent and current developments

BOUT++ is a 3D nonlinear finite-difference plasma simulation code, capable of solving quite general systems of Partial Differential Equations (PDEs), but targeted particularly on studies of the edge region of tokamak plasmas. BOUT++ is publicly available, and has been adopted by a growing number of researchers worldwide. Here we present improvements which have been made to the code since its original release, both in terms of structure and its capabilities. Some recent applications of these methods are reviewed, and areas of active development are discussed. We also present algorithms and tools which have been developed to enable creation of inputs from analytic expressions and experimental data, and for processing and visualisation of output results. This includes a new tool Hypnotoad for the creation of meshes from experimental equilibria. Algorithms have been implemented in BOUT++ to solve a range of linear algebraic problems encountered in the simulation of reduced Magnetohydrodynamics (MHD) and gyro-fluid models: A preconditioning scheme is presented which enables the plasma potential to be calculated efficiently using iterative methods supplied by the PETSc library (the Portable, Extensible Toolkit for Scientific Computation) (Balay et al. 2014), without invoking the Boussinesq approximation. Scaling studies are also performed of a linear solver used as part of physics-based preconditioning to accelerate the convergence of implicit time-integration schemes.

Strawman: A Batch In Situ Visualization and Analysis Infrastructure for Multi-Physics Simulation Codes

We present Strawman, a system designed to explore the in situ visualization and analysis needs of simulation code teams planning for multi-physics calculations on exascale architectures. Strawmans design derives from key requirements from a diverse set of simulation code teams, including lightweight usage of shared resources, batch processing, ability to leverage modern architectures, and ease-of-use both for software integration and for usage during simulation runs. We describe the Strawman system, the key technologies it depends on, and our experiences integrating Strawman into three proxy simulations. Our findings show that Strawmans design meets our target requirements, and that some of its concepts may be worthy of integration into our community in situ implementations.

A hierarchical error controlled octree data structure for large-scale visualization

The fields of medical imaging, vector field visualization, flow simulation, and computational fluid dynamics (CFD) produce large data sets. These datasets cannot be rendered in a reasonable amount of time. Rendering a complete data set at high resolution is often timeconsuming and intractable. Moreover, if a user is interested in only one small portion of a data set, it is wasteful to render the whole data set at high resolution. For example, a medical researcher might be interested not in an entire MRI data set, but only in a certain subregion.

Fast Multiresolution Reads of Massive Simulation Datasets

Todays massively parallel simulation codes can produce output ranging up to many terabytes of data. Utilizing this data to support scientific inquiry requires analysis and visualization, yet the sheer size of the data makes it cumbersome or impossible to read without computational resources similar to the original simulation. We identify two broad classes of problems for reading data and present effective solutions for both. The first class of data reads depends on user requirements and available resources. Tasks such as visualization and user-guided analysis may be accomplished using only a subset of variables with a restricted spatial extent at a reduced resolution. The other class of reads requires full resolution multivariate data to be loaded, for example to restart a simulation. We show that utilizing the hierarchical multiresolution IDX data format enables scalable and efficient serial and parallel read access on a variety of hardware from supercomputers down to portable devices. We demonstrate interactive view-dependent visualization and analysis of massive scientific datasets using low-power commodity hardware, and we compare read performance with other parallel file formats for both full and partial resolution data.

The ALPINE In Situ Infrastructure: Ascending from the Ashes of Strawman

This paper introduces ALPINE, a flyweight in situ infrastructure. The infrastructure is designed for leading-edge supercomputers, and has support for both distributed-memory and shared-memory parallelism. It can take advantage of computing power on both conventional CPU architectures and on many-core architectures such as NVIDIA GPUs or the Intel Xeon Phi. Further, it has a flexible design that supports for integration of new visualization and analysis routines and libraries. The paper describes ALPINEs interface choices and architecture, and also reports on initial experiments performed using the infrastructure.

Constructing isosurfaces in a localized fashion using an underlying octree data structure

We present an octree-based approach for iso surface extraction from large volumetric scalar-valued data. Given scattered points with associated function values, we impose an octree structure of relatively low resolution. Octree construction is controlled by original data resolution and cell-specific error values. For each cell in the octree, we compute an average function value and additional statistical data for the original points inside the cell. Once a specific iso value is specified, we adjust the initial octree by expanding its leaves based on a comparison of the statics with the iso value. We tetrahedrize the centers of the octrees cells to determine tetrahedral meshes decomposing the entire spatial domain of the dat, including a possibly specified region of interest (ROI). Extracted iso surfaces are crack-free inside an ROI, but cracks can appear at the boundary of an ROI. The initial iso surface is an approximation of the exact one, but its quality suffices for a viewer to identify an ROI where more accuracy is desirable. In the refinement process, we refine affected octree nodes and update the triangulation locally to produce better iso surface representations. This adaptive and user- driven refinement provides a means for interactive data exploration via real-time and local iso surface extraction.