Browsing by Author "Binyahib, Roba"

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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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    State-of-the-Art Report on Optimizing Particle Advection Performance
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Yenpure, Abhishek; Sane, Sudhanshu; Binyahib, Roba; Pugmire, David; Garth, Christoph; Childs, Hank; Bruckner, Stefan; Raidou, Renata G.; Turkay, Cagatay
    The computational work to perform particle advection-based flow visualization techniques varies based on many factors, including number of particles, duration, and mesh type. In many cases, the total work is significant, and total execution time (''performance'') is a critical issue. This state-of-the-art report considers existing optimizations for particle advection, using two high-level categories: algorithmic optimizations and hardware efficiency. The sub-categories for algorithmic optimizations include solvers, cell locators, I/O efficiency, and precomputation, while the sub-categories for hardware efficiency all involve parallelism: shared-memory, distributed-memory, and hybrid. Finally, this STAR concludes by identifying current gaps in our understanding of particle advection performance and its optimizations.