Prediction of Distributed Volume Visualization Performance to Support Render Hardware Acquisition

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dc.contributor.authorTkachev, Gleben_US
dc.contributor.authorFrey, Steffenen_US
dc.contributor.authorMüller, Christophen_US
dc.contributor.authorBruder, Valentinen_US
dc.contributor.authorErtl, Thomasen_US
dc.contributor.editorAlexandru Telea and Janine Bennetten_US
dc.date.accessioned2017-06-12T05:12:19Z
dc.date.available2017-06-12T05:12:19Z
dc.date.issued2017
dc.description.abstractWe 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.en_US
dc.description.sectionheadersPerformance Modeling and Optimization
dc.description.seriesinformationEurographics Symposium on Parallel Graphics and Visualization
dc.identifier.doi10.2312/pgv.20171089
dc.identifier.isbn978-3-03868-034-5
dc.identifier.issn1727-348X
dc.identifier.pages11-20
dc.identifier.urihttps://doi.org/10.2312/pgv.20171089
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pgv20171089
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.3 [Computer Graphics]
dc.subjectPicture/Image Generation
dc.subjectViewing algorithms
dc.subjectI.3.2 [Computer Graphics]
dc.subjectGraphics Systems
dc.subjectDistributed/network graphics
dc.titlePrediction of Distributed Volume Visualization Performance to Support Render Hardware Acquisitionen_US
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EGPGV17: Eurographics Symposium on Parallel Graphics and Visualization