Browsing by Author "Doyle, Michael J."

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    A Flexible Data Streaming Design for Interactive Visualization of Large-Scale Volume Data
    (The Eurographics Association, 2022) Wu, Qi; Doyle, Michael J.; Ma, Kwan-Liu; Bujack, Roxana; Tierny, Julien; Sadlo, Filip
    Modern simulations and experiments can produce massive amounts of high-fidelity data that are challenging to transport and visualize interactively. We have designed a data streaming system to support interactive visualization of large volume data. Our streaming system design is unique in its flexibility to support diverse data organizations and its coupling with a highly efficient CPU-based ray-tracing renderer. In this paper, we present our streaming and rendering design and demonstrate the efficacy of our system with progressive rendering of streaming tree-based AMR (TAMR) volume data and radial basis function (RBF) particle volume data. With our system, interactive visualization can be achieved using only a mid-range workstation with a single CPU and a modest quantity of RAM.
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    Stochastic Subsets for BVH Construction
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Tessari, Lorenzo; Dittebrandt, Addis; Doyle, Michael J.; Benthin, Carsten; Myszkowski, Karol; Niessner, Matthias
    BVH construction is a critical component of real-time and interactive ray-tracing systems. However, BVH construction can be both compute and bandwidth intensive, especially when a large degree of dynamic geometry is present. Different build algorithms vary substantially in the traversal performance that they produce, making high quality construction algorithms desirable. However, high quality algorithms, such as top-down construction, are typically more expensive, limiting their benefit in real-time and interactive contexts. One particular challenge of high quality top-down construction algorithms is that the large working set at the top of the tree can make constructing these levels bandwidth-intensive, due to O(nlog(n)) complexity, limited cache locality, and less dense compute at these levels. To address this limitation, we propose a novel stochastic approach to GPU BVH construction that selects a representative subset to build the upper levels of the tree. As a second pass, the remaining primitives are clustered around the BVH leaves and further processed into a complete BVH. We show that our novel approach significantly reduces the construction time of top-down GPU BVH builders by a factor up to 1.8x, while achieving competitive rendering performance in most cases, and exceeding the performance in others.