EGPGV17: Eurographics Symposium on Parallel Graphics and Visualization

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2631432

Browse

Browsing EGPGV17: Eurographics Symposium on Parallel Graphics and Visualization by Subject "I.3.3 [Computer Graphics]"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Prediction of Distributed Volume Visualization Performance to Support Render Hardware Acquisition
    (The Eurographics Association, 2017) Tkachev, Gleb; Frey, Steffen; Müller, Christoph; Bruder, Valentin; Ertl, Thomas; Alexandru Telea and Janine Bennett
    We present our data-driven, neural network-based approach to predicting the performance of a distributed GPU volume renderer for supporting cluster equipment acquisition. On the basis of timing measurements from a single cluster as well as from individual GPUs, we are able to predict the performance gain of upgrading an existing cluster with additional or faster GPUs, or even purchasing of a new cluster with a comparable network configuration. To achieve this, we employ neural networks to capture complex performance characteristics. However, merely relying on them for the prediction would require the collection of training data on multiple clusters with different hardware, which is impractical in most cases. Therefore, we propose a two-level approach to prediction, distinguishing between node and cluster level. On the node level, we generate performance histograms on individual nodes to capture local rendering performance. These performance histograms are then used to emulate the performance of different rendering hardware for cluster-level measurement runs. Crucially, this variety allows the neural network to capture the compositing performance of a cluster separately from the rendering performance on individual nodes. Therefore, we just need a performance histogram of the GPU of interest to generate a prediction. We demonstrate the utility of our approach using different cluster configurations as well as a range of image and volume resolutions.
  • Thumbnail Image
    Item
    Progressive CPU Volume Rendering with Sample Accumulation
    (The Eurographics Association, 2017) Usher, Will; Amstutz, Jefferson; Brownlee, Carson; Knoll, Aaron; Wald, Ingo; Alexandru Telea and Janine Bennett
    We present a new method for progressive volume rendering by accumulating object-space samples over successively rendered frames. Existing methods for progressive refinement either use image space methods or average pixels over frames, which can blur features or integrate incorrectly with respect to depth. Our approach stores samples along each ray, accumulates new samples each frame into a buffer, and progressively interleaves and integrates these samples. Though this process requires additional memory, it ensures interactivity and is well suited for CPU architectures with large memory and cache. This approach also extends well to distributed rendering in cluster environments. We implement this technique in Intel's open source OSPRay CPU ray tracing framework and demonstrate that it is particularly useful for rendering volumetric data with costly sampling functions.