Seekhao N, Yu G, Yuen S, JaJa J, Mongeau L, Li-Jessen N Y K
Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA.
School of Communication Sciences and Disorders, McGill University, Montreal, Québec, Canada.
PDPTA 19 (2019). 2019 Jul-Aug;2019:69-76.
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.
高保真数值模拟会产生大量数据。在生成这些数值数据集时对其进行分析,能为建模现象背后的过程提供有用的见解。然而,开发实时原位可视化技术来处理大量数据可能具有挑战性,因为数据无法适配到GPU上,因此需要进行昂贵的CPU - GPU数据拷贝。在这项工作中,我们提出了一种调度方案,通过GPU超任务实现实时模拟和交互性。此外,使用活动感知技术将CPU - GPU通信降至最低,以减少冗余拷贝。我们的模拟平台能够原位可视化17亿个蛋白质数据点,平均帧率为42.8帧/秒。这种性能使用户在远程服务器上进行模拟时能够实时交互地探索大型数据集。