EGPGV21: Eurographics Symposium on Parallel Graphics and Visualization

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https://diglib.eg.org/handle/10.2312/2633052

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Browsing EGPGV21: Eurographics Symposium on Parallel Graphics and Visualization by Subject "Visualization techniques"

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    Faster RTX-Accelerated Empty Space Skipping using Triangulated Active Region Boundary Geometry
    (The Eurographics Association, 2021) Wald, Ingo; Zellmann, Stefan; Morrical, Nate; Larsen, Matthew and Sadlo, Filip
    We describe a technique for GPU and RTX accelerated space skipping of structured volumes that improves on prior work by replacing clustered proxy boxes with a GPU-extracted triangle mesh that bounds the active regions. Unlike prior methods, our technique avoids costly clustering operations, significantly reduces data structure construction cost, and incurs less overhead when traversing active regions.
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    HyLiPoD: Parallel Particle Advection Via a Hybrid of Lifeline Scheduling and Parallelization-Over-Data
    (The Eurographics Association, 2021) Binyahib, Roba; Pugmire, David; Childs, Hank; Larsen, Matthew and Sadlo, Filip
    Performance characteristics of parallel particle advection algorithms can vary greatly based on workload.With this short paper, we build a new algorithm based on results from a previous bake-off study which evaluated the performance of four algorithms on a variety of workloads. Our algorithm, called HyLiPoD, is a ''meta-algorithm,'' i.e., it considers the desired workload to choose from existing algorithms to maximize performance. To demonstrate HyliPoD's benefit, we analyze results from 162 tests including concurrencies of up to 8192 cores, meshes as large as 34 billion cells, and particle counts as large as 300 million. Our findings demonstrate that HyLiPoD's adaptive approach allows it to match the best performance of existing algorithms across diverse workloads.
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    Machine Learning-Based Autotuning for Parallel Particle Advection
    (The Eurographics Association, 2021) Schwartz, Samuel D.; Childs, Hank; Pugmire, David; Larsen, Matthew and Sadlo, Filip
    Data-parallel particle advection algorithms contain multiple controls that affect their execution characteristics and performance, in particular how often to communicate and how much work to perform between communications. Unfortunately, the optimal settings for these controls vary based on workload, and, further, it is not easy to devise straight-forward heuristics that automate calculation of these settings. To solve this problem, we investigate a machine learning-based autotuning approach for optimizing data-parallel particle advection. During a pre-processing step, we train multiple machine learning techniques using a corpus of performance data that includes results across a variety of workloads and control settings. The best performing of these techniques is then used to form an oracle, i.e., a module that can determine good algorithm control settings for a given workload immediately before execution begins. To evaluate this approach, we assessed the ability of seven machine learning models to capture particle advection performance behavior and then ran experiments for 108 particle advection workloads on 64 GPUs of a supercomputer. Our findings show that our machine learning-based oracle achieves good speedups relative to the available gains.