{"title":"Nonlinear spatio-temporal wave computing for real-time applications on GPU","authors":"M. Tukel, R. Yeniceri, M. Yalçin","doi":"10.1109/CNNA.2012.6331419","DOIUrl":null,"url":null,"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.","PeriodicalId":387536,"journal":{"name":"2012 13th International Workshop on Cellular Nanoscale Networks and their Applications","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 13th International Workshop on Cellular Nanoscale Networks and their Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CNNA.2012.6331419","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.