Hardware-accelerated interactive data visualization for neuroscience in Python.

Large datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization

From Big Data to Big Displays: High-Performance Visualization at Blue Brain

Blue Brain has pushed high-performance visualization (HPV) to complement its HPC strategy since its inception in 2007. In 2011, this strategy has been accelerated to develop innovative visualization solutions through increased funding and strategic partnerships with other research institutions. We present the key elements of this HPV ecosystem, which integrates C++ visualization applications with novel collaborative display systems. We motivate how our strategy of transforming visualization engines into services enables a variety of use cases, not only for the integration with high-fidelity displays, but also to build service oriented architectures, to link into web applications and to provide remote services to Python applications.Comment: ISC 2017 Visualization at Scale worksho

Doctor of Philosophy

dissertationRay tracing presents an efficient rendering algorithm for scientific visualization using common visualization tools and scales with increasingly large geometry counts while allowing for accurate physically-based visualization and analysis, which enables enhanced rendering and new visualization techniques. Interactivity is of great importance for data exploration and analysis in order to gain insight into large-scale data. Increasingly large data sizes are pushing the limits of brute-force rasterization algorithms present in the most widely-used visualization software. Interactive ray tracing presents an alternative rendering solution which scales well on multicore shared memory machines and multinode distributed systems while scaling with increasing geometry counts through logarithmic acceleration structure traversals. Ray tracing within existing tools also provides enhanced rendering options over current implementations, giving users additional insight from better depth cues while also enabling publication-quality rendering and new models of visualization such as replicating photographic visualization techniques

Tessellated Voxelization for Global Illumination using Voxel Cone Tracing

Modeling believable lighting is a crucial component of computer graphics applications, including games and modeling programs. Physically accurate lighting is complex and is not currently feasible to compute in real-time situations. Therefore, much research is focused on investigating efficient ways to approximate light behavior within these real-time constraints. In this thesis, we implement a general purpose algorithm for real-time applications to approximate indirect lighting. Based on voxel cone tracing, we use a filtered representation of a scene to efficiently sample ambient light at each point in the scene. We present an approach to scene voxelization using hardware tessellation and compare it with an approach utilizing hardware rasterization. We also investigate possible methods of warped voxelization. Our contributions include a complete and open-source implementation of voxel cone tracing along with both voxelization algorithms. We find similar performance and quality with both voxelization algorithms

GPU voxelization

Given a triangulated model, we want to identify which voxels of a voxel grid are intersected by the boundary of this model. There are other branch of implemented voxelizations, in which not only the boundary is detected, also the interior of the model. Often these voxels are cubes. But it is not a restriction, there are other presented techniques in which the voxel grid is the view frustum, and voxels are prisms. There are di erent kind of voxelizations depending on the rasterization behavior. Approximate rasterization is the standard way of rasterizing fragments in GPU. It means only those fragments whose center lies inside the projection of the primitive are identi ed. Conservative rasterization (Hasselgren et al. , 2005) involves a dilation operation over the primitive. This is done in GPU to ensure that in the rasterization stage all the intersected fragments have its center inside the dilated primitive. However, this can produce spurious fragments, non-intersected pixels. Exact voxelization detects only those voxels that we need.

GPU-Accelerated Point-Based Color Bleeding

Traditional global illumination lighting techniques like Radiosity and Monte Carlo sampling are computationally expensive. This has prompted the development of the Point-Based Color Bleeding (PBCB) algorithm by Pixar in order to approximate complex indirect illumination while meeting the demands of movie production; namely, reduced memory usage, surface shading independent run time, and faster renders than the aforementioned lighting techniques. The PBCB algorithm works by discretizing a scene’s directly illuminated geometry into a point cloud (surfel) representation. When computing the indirect illumination at a point, the surfels are rasterized onto cube faces surrounding that point, and the constituent pixels are combined into the final, approximate, indirect lighting value. In this thesis we present a performance enhancement to the Point-Based Color Bleeding algorithm through hardware acceleration; our contribution incorporates GPU-accelerated rasterization into the cube-face raster phase. The goal is to leverage the powerful rasterization capabilities of modern graphics processors in order to speed up the PBCB algorithm over standard software rasterization. Additionally, we contribute a preprocess that generates triangular surfels that are suited for fast rasterization by the GPU, and show that new heterogeneous architecture chips (e.g. Sandy Bridge from Intel) simplify the code required to leverage the power of the GPU. Our algorithm reproduces the output of the traditional Monte Carlo technique with a speedup of 41.65x, and additionally achieves a 3.12x speedup over software-rasterized PBCB

Practical line rasterization for multi-resolution textures

Draping 2D vectorial information over a 3D terrain elevation model is usually performed by real-time rendering to texture. In the case of linear feature representation, there are several specific problems using the texturing approach, specially when using multi-resolution textures. These problems are related to visual quality, aliasing artifacts and rendering performance. In this paper, we address the problems of 2D line rasterization on a multi-resolution texturing engine from a pragmatical point of view; some alternative solutions are presented, compared and evaluated. For each solution we have analyzed the visual quality, the impact on the rendering performance and the memory consumption. The study performed in this work is based on an OpenGL implementation of a clipmap-based multi-resolution texturing system, and is oriented towards the use of inexpensive consumer graphics hardware. 1