48 research outputs found
Parallel Rendering and Large Data Visualization
We are living in the big data age: An ever increasing amount of data is being
produced through data acquisition and computer simulations. While large scale
analysis and simulations have received significant attention for cloud and
high-performance computing, software to efficiently visualise large data sets
is struggling to keep up.
Visualization has proven to be an efficient tool for understanding data, in
particular visual analysis is a powerful tool to gain intuitive insight into
the spatial structure and relations of 3D data sets. Large-scale visualization
setups are becoming ever more affordable, and high-resolution tiled display
walls are in reach even for small institutions. Virtual reality has arrived in
the consumer space, making it accessible to a large audience.
This thesis addresses these developments by advancing the field of parallel
rendering. We formalise the design of system software for large data
visualization through parallel rendering, provide a reference implementation of
a parallel rendering framework, introduce novel algorithms to accelerate the
rendering of large amounts of data, and validate this research and development
with new applications for large data visualization. Applications built using
our framework enable domain scientists and large data engineers to better
extract meaning from their data, making it feasible to explore more data and
enabling the use of high-fidelity visualization installations to see more
detail of the data.Comment: PhD thesi
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
Physically-based in silico light sheet microscopy for visualizing fluorescent brain models
We present a physically-based computational model of the light sheet fluorescence microscope (LSFM). Based on Monte Carlo ray tracing and geometric optics, our method simulates the operational aspects and image formation process of the LSFM. This simulated, in silico LSFM creates synthetic images of digital fluorescent specimens that can resemble those generated by a real LSFM, as opposed to established visualization methods producing visually-plausible images. We also propose an accurate fluorescence rendering model which takes into account the intrinsic characteristics of fluorescent dyes to simulate the light interaction with fluorescent biological specimen
A Distributed GPU-based Framework for real-time 3D Volume Rendering of Large Astronomical Data Cubes
We present a framework to interactively volume-render three-dimensional data
cubes using distributed ray-casting and volume bricking over a cluster of
workstations powered by one or more graphics processing units (GPUs) and a
multi-core CPU. The main design target for this framework is to provide an
in-core visualization solution able to provide three-dimensional interactive
views of terabyte-sized data cubes. We tested the presented framework using a
computing cluster comprising 64 nodes with a total of 128 GPUs. The framework
proved to be scalable to render a 204 GB data cube with an average of 30 frames
per second. Our performance analyses also compare between using NVIDIA Tesla
1060 and 2050 GPU architectures and the effect of increasing the visualization
output resolution on the rendering performance. Although our initial focus, and
the examples presented in this work, is volume rendering of spectral data cubes
from radio astronomy, we contend that our approach has applicability to other
disciplines where close to real-time volume rendering of terabyte-order 3D data
sets is a requirement.Comment: 13 Pages, 7 figures, has been accepted for publication in
Publications of the Astronomical Society of Australi
Parallel Rendering on Hybrid Multi-GPU Clusters
Achieving efficient scalable parallel rendering for interactive visualization applications on medium-sized graphics clusters remains a challenging problem. Framerates of up to 60hz require a carefully designed and fine-tuned parallel rendering implementation that fits all required operations into the 16ms time budget available for each rendered frame. Furthermore, modern commodity hardware embraces more and more a NUMA architecture, where multiple processor sockets each have their locally attached memory and where auxiliary devices such as GPUs and network interfaces are directly attached to one of the processors. Such so called fat NUMA processing and graphics nodes are increasingly used to build cost-effective hybrid shared/distributed memory visualization clusters. In this paper we present a thorough analysis of the asynchronous parallelization of the rendering stages and we derive and implement important optimizations to achieve highly interactive framerates on such hybrid multi-GPU clusters. We use both a benchmark program and a real-world scientific application used to visualize, navigate and interact with simulations of cortical neuron circuit models
A Process for Digitizing and Simulating Biologically Realistic Oligocellular Networks Demonstrated for the Neuro-Glio-Vascular Ensemble
One will not understand the brain without an integrated exploration of structure and function, these attributes being two sides of the same coin: together they form the currency of biological computation. Accordingly, biologically realistic models require the re-creation of the architecture of the cellular components in which biochemical reactions are contained. We describe here a process of reconstructing a functional oligocellular assembly that is responsible for energy supply management in the brain and creating a computational model of the associated biochemical and biophysical processes. The reactions that underwrite thought are both constrained by and take advantage of brain morphologies pertaining to neurons, astrocytes and the blood vessels that deliver oxygen, glucose and other nutrients. Each component of this neuro-glio-vasculature ensemble (NGV) carries-out delegated tasks, as the dynamics of this system provide for each cell-type its own energy requirements while including mechanisms that allow cooperative energy transfers. Our process for recreating the ultrastructure of cellular components and modeling the reactions that describe energy flow uses an amalgam of state-of the-art techniques, including digital reconstructions of electron micrographs, advanced data analysis tools, computational simulations and in silico visualization software. While we demonstrate this process with the NGV, it is equally well adapted to any cellular system for integrating multimodal cellular data in a coherent framework
Reconstruction and simulation of neocortical microcircuitry
We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm3 containing ∼31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ∼8 million connections with ∼37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies