本文利用现代图形加速卡中GPU(Graphics Process Unit)的可编程管线,实现了图形电磁计算(GRECO)方法.与原有的方法相比,在利用物理光学和物理绕射理论的基础上,计算速度提高了20倍左右.并且利用GPU实现了射线追踪算法,用于目标上多次散...本文利用现代图形加速卡中GPU(Graphics Process Unit)的可编程管线,实现了图形电磁计算(GRECO)方法.与原有的方法相比,在利用物理光学和物理绕射理论的基础上,计算速度提高了20倍左右.并且利用GPU实现了射线追踪算法,用于目标上多次散射的计算,使得GRECO方法可以快速计算具有凹腔结构目标的电磁散射.本方法对于目标识别和逆合成孔径成像等方面的研究具有重要的应用价值.展开更多
Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search space.EAs have good running and mining perform...Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search space.EAs have good running and mining performance,but they still require huge computational resource and may miss many HUIs.Due to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded processors.Experiments show that the mining performance of PHUI-GA outperforms the existing EAs.When mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.展开更多
图形处理器(Graphics Process Unit,GPU)作为整个手机系统及芯片(System On Chip,SoC)的重要组成部分,除了具有提供高性能的图形图像处理能力之外,减少功耗也是移动平台图形处理器设计者们需要考虑的关键问题之一。在经典图形处理器芯...图形处理器(Graphics Process Unit,GPU)作为整个手机系统及芯片(System On Chip,SoC)的重要组成部分,除了具有提供高性能的图形图像处理能力之外,减少功耗也是移动平台图形处理器设计者们需要考虑的关键问题之一。在经典图形处理器芯片的基础上,一种新的低功耗高性能的图形绘制解决方案着重从架构层面解决功耗的问题。通过显卡驱动端的配合,将很多通用手机应用程序的三维绘制任务转换成低功耗绘制命令,绕开复杂的指令执行单元高效的完成绘制任务,同时通过将整个指令执行单元划分在独立的电源域,进行电源门控,将功耗大大降低。实验表明,对某些典型应用场景,新的解决方案最多可以节省50%的功耗。展开更多
文摘本文利用现代图形加速卡中GPU(Graphics Process Unit)的可编程管线,实现了图形电磁计算(GRECO)方法.与原有的方法相比,在利用物理光学和物理绕射理论的基础上,计算速度提高了20倍左右.并且利用GPU实现了射线追踪算法,用于目标上多次散射的计算,使得GRECO方法可以快速计算具有凹腔结构目标的电磁散射.本方法对于目标识别和逆合成孔径成像等方面的研究具有重要的应用价值.
基金This work was supported by the National Natural Science Foundation of China(62073155,62002137,62106088,62206113)the High-End Foreign Expert Recruitment Plan(G2023144007L)the Fundamental Research Funds for the Central Universities(JUSRP221028).
文摘Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search space.EAs have good running and mining performance,but they still require huge computational resource and may miss many HUIs.Due to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded processors.Experiments show that the mining performance of PHUI-GA outperforms the existing EAs.When mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.
文摘图形处理器(Graphics Process Unit,GPU)作为整个手机系统及芯片(System On Chip,SoC)的重要组成部分,除了具有提供高性能的图形图像处理能力之外,减少功耗也是移动平台图形处理器设计者们需要考虑的关键问题之一。在经典图形处理器芯片的基础上,一种新的低功耗高性能的图形绘制解决方案着重从架构层面解决功耗的问题。通过显卡驱动端的配合,将很多通用手机应用程序的三维绘制任务转换成低功耗绘制命令,绕开复杂的指令执行单元高效的完成绘制任务,同时通过将整个指令执行单元划分在独立的电源域,进行电源门控,将功耗大大降低。实验表明,对某些典型应用场景,新的解决方案最多可以节省50%的功耗。
