High-Performance Host-Device Scheduling and Data-Transfer Minimization Techniques for Visualization of 3D Agent-Based Wound Healing Applications.

N Seekhao, G Yu, S Yuen, J JaJa, L Mongeau, N Y K Li-Jessen
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Abstract

High-fidelity numerical simulations produce massive amounts of data. Analyzing these numerical data sets as they are being generated provides useful insights into the processes underlying the modeled phenomenon. However, developing real-time in-situ visualization techniques to process large amounts of data can be challenging since the data does not fit on the GPU, thus requiring expensive CPU-GPU data copies. In this work, we present a scheduling scheme that achieve real-time simulation and interactivity through GPU hyper-tasking. Furthermore, the CPU-GPU communications were minimized using an activity-aware technique to reduce redundant copies. Our simulation platform is capable of visualizing 1.7 billion protein data points in situ, with an average frame rate of 42.8 fps. This performance allows users to explore large data sets on remote server with real-time interactivity as they are performing their simulations.

Abstract Image

Abstract Image

Abstract Image

基于三维代理的伤口愈合应用可视化的高性能主机-设备调度和数据传输最小化技术。
高保真数值模拟产生大量数据。在生成这些数值数据集时对其进行分析,可以对模拟现象背后的过程提供有用的见解。然而,开发实时现场可视化技术来处理大量数据可能具有挑战性,因为数据不适合GPU,因此需要昂贵的CPU-GPU数据副本。在这项工作中,我们提出了一种通过GPU超任务实现实时仿真和交互性的调度方案。此外,使用活动感知技术最小化CPU-GPU通信以减少冗余副本。我们的仿真平台能够实时显示17亿个蛋白质数据点,平均帧率为42.8 fps。这种性能允许用户在执行模拟时通过实时交互性探索远程服务器上的大型数据集。
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