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.3.2 [Computer Graphics]"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    PaViz: A Power-Adaptive Framework for Optimizing Visualization Performance
    (The Eurographics Association, 2017) Labasan, Stephanie; Larsen, Matthew; Childs, Hank; Rountree, Barry; Alexandru Telea and Janine Bennett
    Power consumption is widely regarded as one of the biggest challenges to reaching the next generation of high-performance computing. One strategy for achieving an exaflop given limited power is hardware overprovisioning. In this model, the theoretical peak power usage of the system is greater than the maximum allowable power usage, and a central manager keeps the aggregate power usage at the maximum by enforcing power caps on each node in the system. For this model to be effective, the central manager must be able to make informed trade-offs between power usage and performance. With this work, we introduce PaViz, a software framework designed to optimize the distribution of power for visualization algorithms, which have different characteristics than simulation codes. In this study, we focus specifically on rendering. Our strategy uses a performance model, where nodes predicted to have a small amount of work are allocated less power, and nodes predicted to have a large amount of work are allocated more power. This approach increases the likelihood of all nodes finishing at the same time, which is optimal for power efficiency. At best, our adaptive strategy achieves up to 33% speedup over the traditional strategy, while using the same total power.
  • 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
    A Task-Based Parallel Rendering Component For Large-Scale Visualization Applications
    (The Eurographics Association, 2017) Biedert, Tim; Werner, Kilian; Hentschel, Bernd; Garth, Christoph; Alexandru Telea and Janine Bennett
    An increasingly heterogeneous system landscape in modern high performance computing requires the efficient and portable adaption of performant algorithms to diverse architectures. However, classic hybrid shared-memory/distributed systems are designed and tuned towards specific platforms, thus impeding development, usage and optimization of these approaches with respect to portability. We demonstrate a flexible parallel rendering framework built upon a task-based dynamic runtime environment enabling adaptable performance-oriented deployment on various platform configurations. Our task definition represents an effective and easy-to-control trade-off between sort-first and sort-last image compositing, enabling good scalability in combination with inherent dynamic load balancing. We conduct comprehensive benchmarks to verify the characteristics and potential of our novel task-based system design for high-performance visualization.