The recent successes of genome-wide expression profiling in biology tend to overlook the power of genetics. We here propose a merger of genomics and genetics into ‘genetical genomics’. This involves expression profiling and marker-based fingerprinting of each individual of a segregating population, and exploits all the statistical tools used in the analysis of quantitative trait loci. Genetical genomics will combine the power of two different worlds in a way that is likely to become instrumental in the further unravelling of metabolic, regulatory and developmental pathways.

Genetical genomics : the added value from segregation

Two important tools for manipulating polygonal models are simplification and repair and we present voxel-based methods for performing both of these tasks. We describe a method for converting polygonal models to a volumetric representation in a way that handles models with holes, double walls, and intersecting parts. This allows us to perform polygon model repair simply by converting a model to and from the volumetric domain. We also describe a new topology-altering simplification method that is based on 3D morphological operators. Visually unimportant features such as tubes and holes may be eliminated from a model by the open and close morphological operators. Our simplification approach accepts polygonal models as input, scan converts these to create a volumetric description, performs topology modification, and then converts the results back to polygons. We then apply a topology-preserving polygon simplification technique to produce a final model. Our simplification method produces results that are everywhere manifold.

https://www.cc.gatech.edu/~turk/my_papers/volumetric_simp.pdf

Simplification and repair of polygonal models using volumetric techniques

Computational science is paramount to the understanding of underlying processes in internal combustion engines of the future that will utilize non-petroleum-based alternative fuels, including carbon-neutral biofuels, and burn in new combustion regimes that will attain high efficiency while minimizing emissions of particulates and nitrogen oxides. Next-generation engines will likely operate at higher pressures, with greater amounts of dilution and utilize alternative fuels that exhibit a wide range of chemical and physical properties. Therefore, there is a significant role for high-fidelity simulations, direct numerical simulations (DNS), specifically designed to capture key turbulence-chemistry interactions in these relatively uncharted combustion regimes, and in particular, that can discriminate the effects of differences in fuel properties. In DNS, all of the relevant turbulence and flame scales are resolved numerically using high-order accurate numerical algorithms. As a consequence terascale DNS are computationally intensive, require massive amounts of computing power and generate tens of terabytes of data. Recent results from terascale DNS of turbulent flames are presented here, illustrating its role in elucidating flame stabilization mechanisms in a lifted turbulent hydrogen/air jet flame in a hot air coflow, and the flame structure of a fuel-lean turbulent premixed jet flame. Computing at this scale requires close collaborations between computer and combustion scientists to provide optimized scaleable algorithms and software for terascale simulations, efficient collective parallel I/O, tools for volume visualization of multiscale, multivariate data and automating the combustion workflow. The enabling computer science, applied to combustion science, is also required in many other terascale physics and engineering simulations. In particular, performance monitoring is used to identify the performance of key kernels in the DNS code, S3D and especially memory intensive loops in the code. Through the careful application of loop transformations, data reuse in cache is exploited thereby reducing memory bandwidth needs, and hence, improving S3D's nodal performance. To enhance collective parallel I/O in S3D, an MPI-I/O caching design is used to construct a two-stage write-behind method for improving the performance of write-only operations. The simulations generate tens of terabytes of data requiring analysis. Interactive exploration of the simulation data is enabled by multivariate time-varying volume visualization. The visualization highlights spatial and temporal correlations between multiple reactive scalar fields using an intuitive user interface based on parallel coordinates and time histogram. Finally, an automated combustion workflow is designed using Kepler to manage large-scale data movement, data morphing, and archival and to provide a graphical display of run-time diagnostics.

https://iopscience.iop.org/article/10.1088/1749-4699/2/1/015001/pdf

Terascale direct numerical simulations of turbulent combustion using S3D

Computational science is paramount to the understanding of underlying processes in internal combustion engines of the future that will utilize non-petroleum-based alternative fuels, including carbon-neutral biofuels, and burn in new combustion regimes that will attain high efficiency while minimizing emissions of particulates and nitrogen oxides. Next-generation engines will likely operate at higher pressures, with greater amounts of dilution and utilize alternative fuels that exhibit a wide range of chemical and physical properties. Therefore, there is a significant role for high-fidelity simulations, direct numerical simulations (DNS), specifically designed to capture key turbulence-chemistry interactions in these relatively uncharted combustion regimes, and in particular, that can discriminate the effects of differences in fuel properties. In DNS, all of the relevant turbulence and flame scales are resolved numerically using high-order accurate numerical algorithms. As a consequence terascale DNS are computationally intensive, require massive amounts of computing power and generate tens of terabytes of data. Recent results from terascale DNS of turbulent flames are presented here, illustrating its role in elucidating flame stabilization mechanisms in a lifted turbulent hydrogen/air jet flame in a hot air co-flow, and the flame structure of a fuel-lean turbulent premixed jet flame. Computing at this scale requires close collaborations betweenmore » computer and combustion scientists to provide optimized scaleable algorithms and software for terascale simulations, efficient collective parallel I/O, tools for volume visualization of multiscale, multivariate data and automating the combustion workflow. The enabling computer science, applied to combustion science, is also required in many other terascale physics and engineering simulations. In particular, performance monitoring is used to identify the performance of key kernels in the DNS code, S3D and especially memory intensive loops in the code. Through the careful application of loop transformations, data reuse in cache is exploited thereby reducing memory bandwidth needs, and hence, improving S3D's nodal performance. To enhance collective parallel I/O in S3D, an MPI-I/O caching design is used to construct a two-stage write-behind method for improving the performance of write-only operations. The simulations generate tens of terabytes of data requiring analysis. Interactive exploration of the simulation data is enabled by multivariate time-varying volume visualization. The visualization highlights spatial and temporal correlations between multiple reactive scalar fields using an intuitive user interface based on parallel coordinates and time histogram. Finally, an automated combustion workflow is designed using Kepler to manage large-scale data movement, data morphing, and archival and to provide a graphical display of run-time diagnostics.« less

Terascale direct numerical simulations of turbulent combustion using S3D.

The representation of geometric objects based on volumetric data structures has advantages in many geometry processing applications that require, e.g., fast surface interrogation or boolean operations such as intersection and union. However, surface based algorithms like shape optimization (fairing) or freeform modeling often need a topological manifold representation where neighborhood information within the surface is explicitly available. Consequently, it is necessary to find effective conversion algorithms to generate explicit surface descriptions for the geometry which is implicitly defined by a volumetric data set. Since volume data is usually sampled on a regular grid with a given step width, we often observe severe alias artifacts at sharp features on the extracted surfaces. In this paper we present a new technique for surface extraction that performs feature sensitive sampling and thus reduces these alias effects while keeping the simple algorithmic structure of the standard Marching Cubes algorithm. We demonstrate the effectiveness of the new technique with a number of application examples ranging from CSG modeling and simulation to surface reconstruction and remeshing of polygonal models.

/pdf/feature-sensitive-surface-extraction-from-volume-data-3gg76bc6no.pdf

Feature sensitive surface extraction from volume data

This paper evaluates and compares four volume rendering algorithms that have become rather popular for rendering datasets described on uniform rectilinear grids: raycasting, splatting, shear-warp, and hardware-assisted 3D texture-mapping. In order to assess both the strengths and the weaknesses of these algorithms in a wide variety of scenarios, a set of real-life benchmark datasets with different characteristics was carefully selected. In the rendering, all algorithm-independent image synthesis parameters, such as viewing matrix, transfer functions, and optical model, were kept constant to enable a fair comparison of the rendering results. Both image quality and computational complexity were evaluated and compared, with the aim of providing both researchers and practitioners with guidelines on which algorithm is most suited in which scenario. Our analysis also indicates the current weakness in each algorithm's pipeline, and possible solutions to these as well as pointers for future research are offered.

/pdf/a-practical-evaluation-of-popular-volume-rendering-17wqe18bn9.pdf

A practical evaluation of popular volume rendering algorithms

The process of generating discrete surfaces in a volumetric representation, termed voxelization, is confronted with topological considerations as well as accuracy and efficiency requirements. The authors introduce a new method for voxelizing planar objects which, unlike existing methods, provides topological conformity through geometric measures. They extend their approach to provide, for the first time, an accurate and coherent method for voxelizing polygon meshes. This method eliminates common voxelization artifacts at edges and vertices. They prove the method's topological attributes and report performance of their implementation. Finally, they demonstrate that this approach forms a basis for a new set of voxelization algorithms by voxelizing an example cubic object.

/pdf/an-accurate-method-for-voxelizing-polygon-meshes-1mbhh0ny68.pdf

An accurate method for voxelizing polygon meshes

Splatting is a popular volume rendering algorithm that pairs good image quality with an efficient volume projection scheme. The current axis-aligned sheet-buffer approach, however, bears certain inaccuracies. The effect of these is less noticeable in still images, but clearly revealed in animated viewing, where disturbing popping of object brightness occurs at certain view angle transitions. In previous work, we presented a new variant of sheet-buffered splatting in which the compositing sheets are oriented parallel to the image plane. This scheme not only eliminates the condition for popping, but also produces images of higher quality. In this paper, we summarize this new paradigm and extend it in a number of ways. We devise a new solution to render rectilinear grids of equivalent cost to the traditional approach that treats the anisotropic volume as being warped into a cubic grid. This enables us to use the usual radially symmetric kernels, which can be projected without inaccuracies. Next, current splatting approaches necessitate the projection of all voxels in the iso-interval(s), although only a subset of these voxels may eventually be visible in the final image. To eliminate these wasteful computations we propose a novel front-to-back approach that employs an occlusion map to determine if a splat contributes to the image before it is projected, thus skipping occluded splats. Additional measures are presented for further speedups. In addition, we present an efficient list-based volume traversal scheme that facilitates the quick modification of transfer functions and iso-values.

High-quality splatting on rectilinear grids with efficient culling of occluded voxels

Particle tracing for streamline and path line generation is a common method of visualizing vector fields in scientific data, but it is difficult to parallelize efficiently because of demanding and widely varying computational and communication loads. In this paper we scale parallel particle tracing for visualizing steady and unsteady flow fields well beyond previously published results. We configure the 4D domain decomposition into spatial and temporal blocks that combine in-core and out-of-core execution in a flexible way that favors faster run time or smaller memory. We also compare static and dynamic partitioning approaches. Strong and weak scaling curves are presented for tests conducted on an IBM Blue Gene/P machine at up to 32 K processes using a parallel flow visualization library that we are developing. Datasets are derived from computational fluid dynamics simulations of thermal hydraulics, liquid mixing, and combustion.

/pdf/a-study-of-parallel-particle-tracing-for-steady-state-and-4ev0x4h77t.pdf

A Study of Parallel Particle Tracing for Steady-State and Time-Varying Flow Fields

Distance fields are an important volume representation. A high quality distance field facilitates accurate surface characterization and gradient estimation. However, due to Nyquist's Law, no existing volumetric methods based on the linear sampling theory can fully capture surface details, such as corners and edges, in 3D space. We propose a novel complete distance field representation (CDFR) that does not rely on Nyquist's sampling theory. To accomplish this, we construct a volume where each voxel has a complete description of all portions of surface that affect the local distance field. For any desired distance, we are able to extract a surface contour in true Euclidean distance, at any level of accuracy, from the same CDFR representation. Such point-based iso-distance contours have faithful per-point gradients and can be interactively visualized using splatting, providing per-point shaded image quality. We also demonstrate applying CDFR to a cutting edge design for manufacturing application involving high-complexity parts at un-precedented accuracy using only commonly available computational resources.

/pdf/a-complete-distance-field-representation-53sc74dk44.pdf

Jian Huang

Papers

A practical evaluation of popular volume rendering algorithms

An accurate method for voxelizing polygon meshes

High-quality splatting on rectilinear grids with efficient culling of occluded voxels

A Study of Parallel Particle Tracing for Steady-State and Time-Varying Flow Fields

A complete distance field representation