Nonlinear spatio-temporal wave computing for real-time applications on GPU

M. Tukel, R. Yeniceri, M. Yalçin
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引用次数: 1

Abstract

In this work, active wave simulation on Cellular Nonlinear Network was computed for path planning on the GPU of a NVIDIA GTX275 video card. In software part, QtOpenCL, which is a wrapper library of OpenCL, was used to make code portable for systems with different GPUs. We achieved promising results comparing to results achieved by both CPU and FPGA. We have implemented different hardware and software solutions to path planning problem for 2-D media in real-time. They were almost at limit of real-time requirements because of some bottlenecks such as low communication bandwidth and low resolution of network. In this work, by utilizing GPUs, we performed 60000 iterations per second for simulation of 128×128 node network while we achieved at most 35 iterations per second with software on an Intel Core 2 Duo P8700 processor. We also achieved 36 iterations per second for 3-D active wave simulation of a 256 × 256 × 256 network on GPU.
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非线性时空波计算在GPU上的实时应用
本文在NVIDIA GTX275显卡的GPU上进行了蜂窝非线性网络有源波仿真,并进行了路径规划。在软件部分,利用OpenCL的封装库QtOpenCL实现了代码在不同gpu系统上的可移植性。与CPU和FPGA的结果相比,我们取得了很好的结果。针对二维介质的实时路径规划问题,我们实现了不同的硬件和软件解决方案。由于通信带宽低、网络分辨率低等瓶颈,它们的实时性几乎达到了极限。在这项工作中,通过使用gpu,我们每秒执行60000次迭代来模拟128×128节点网络,而我们在Intel Core 2 Duo P8700处理器上的软件每秒最多实现35次迭代。我们还在GPU上实现了256 × 256 × 256网络的三维有源波模拟每秒36次迭代。
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