4 research outputs found
Parallel volume rendering for large scientific data
Data sets of immense size are regularly generated by large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effectively visualized on standard workstations is now commonplace.
One solution to this problem is to employ a \u27visualization cluster,\u27 a small to medium scale cluster dedicated to performing visualization and analysis of massive data sets generated on larger scale supercomputers. These clusters are designed to fulfill a different need than traditional supercomputers, and therefore their design mandates different hardware choices, such as increased memory, and more recently, graphics processing units (GPUs). While there has been much previous work on distributed memory visualization as well as GPU visualization, there is a relative dearth of algorithms which effectively use GPUs at a large scale in a distributed memory environment. In this work, we study a common visualization technique in a GPU-accelerated, distributed memory setting, and present performance characteristics when scaling to extremely large data sets
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
Cross-segment load balancing in parallel rendering
With faster graphics hardware comes the possibility to realize even more complicated applications that require more detailed data and provide better presentation. The processors keep being challenged with bigger amount of data and higher resolution outputs, requiring more research in the parallel/distributed rendering domain. Optimizing resource usage to improve throughput is one important topic, which we address in this article for multi-display applications, using the Equalizer parallel rendering framework. This paper introduces and analyzes cross-segment load balancing which efficiently assigns all available shared graphics resources to all display output segments with dynamical task partitioning to improve performance in parallel rendering