Browsing by Author "Yenpure, Abhishek"

Now showing 1 - 3 of 3
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    Efficient Point Merging Using Data Parallel Techniques
    (The Eurographics Association, 2019) Yenpure, Abhishek; Childs, Hank; Moreland, Kenneth; Childs, Hank and Frey, Steffen
    We study the problem of merging three-dimensional points that are nearby or coincident. We introduce a fast, efficient approach that uses data parallel techniques for execution in various shared-memory environments. Our technique incorporates a heuristic for efficiently clustering spatially close points together, which is one reason our method performs well against other methods. We then compare our approach against methods of a widely-used scientific visualization library accompanied by a performance study that shows our approach works well with different kinds of parallel hardware (many-core CPUs and NVIDIA GPUs) and data sets of various sizes.
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    Scalable In Situ Computation of Lagrangian Representations via Local Flow Maps
    (The Eurographics Association, 2021) Sane, Sudhanshu; Yenpure, Abhishek; Bujack, Roxana; Larsen, Matthew; Moreland, Kenneth; Garth, Christoph; Johnson, Chris R.; Childs, Hank; Larsen, Matthew and Sadlo, Filip
    In situ computation of Lagrangian flow maps to enable post hoc time-varying vector field analysis has recently become an active area of research. However, the current literature is largely limited to theoretical settings and lacks a solution to address scalability of the technique in distributed memory. To improve scalability, we propose and evaluate the benefits and limitations of a simple, yet novel, performance optimization. Our proposed optimization is a communication-free model resulting in local Lagrangian flow maps, requiring no message passing or synchronization between processes, intrinsically improving scalability, and thereby reducing overall execution time and alleviating the encumbrance placed on simulation codes from communication overheads. To evaluate our approach, we computed Lagrangian flow maps for four time-varying simulation vector fields and investigated how execution time and reconstruction accuracy are impacted by the number of GPUs per compute node, the total number of compute nodes, particles per rank, and storage intervals. Our study consisted of experiments computing Lagrangian flow maps with up to 67M particle trajectories over 500 cycles and used as many as 2048 GPUs across 512 compute nodes. In all, our study contributes an evaluation of a communication-free model as well as a scalability study of computing distributed Lagrangian flow maps at scale using in situ infrastructure on a modern supercomputer.
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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.