Spatio-Temporal Visual Analysis of Turbulent Superstructures in Unsteady Flow

Ghaffari, Behdad; Gatti, Davide; Westermann, Rüdiger

doi:10.1109/tvcg.2022.3232367

Cited by 1 publication

(1 citation statement)

References 38 publications

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“…These algorithms also generate copious amounts of data output, making it crucial to carefully consider their implementation on GPUs due to memory limitations. Ghaffari et al [GGW22] developed an interactive visualization system for analyzing turbulent flow superstructures, utilizing data compression for efficient GPU streaming and on‐the‐fly decompression for particle advection and feature detection tasks. Günther et al [GKT16] created a Monte Carlo‐based method for visualizing Lagrangian fields in fluid flows, demonstrating GPUs speedups of an order of magnitude better than its multi‐threaded CPU counterpart, and later introduced an enhanced version [RGG20] using GPU shaders achieving 8X their initial research speed.…”

Section: Using Hardware Efficientlymentioning

confidence: 99%

State‐of‐the‐Art Report on Optimizing Particle Advection Performance

Yenpure

Sane²,

Binyahib

et al. 2023

Computer Graphics Forum

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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.

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Section: Using Hardware Efficientlymentioning

confidence: 99%