【摘 要】
：
提出3种策略挖掘三维Kirchhoff积分法体偏移在众核GPU(图形处理器)上的并行性。首先，使用数据传输线程和GPU计算线程构造流水线并行框架，基于此框架直接实现异步输入输出(I/O)以减少GPU和网络存储之间数据传输所需的时间；其次，使用GPU的线程满载策略以使指令吞吐量最大化；最后，应用纹理缓存和常量缓存来减少片外存储器访问，并使用固定功能单元计算超越函数。实验结果表明：相比于Intel X
【机 构】
：
中国石油化工股份有限公司石油勘探开发研究院，北京100083;中国石油化工集团公司多波地震技术重点实验室，北京100083
论文部分内容阅读
提出3种策略挖掘三维Kirchhoff积分法体偏移在众核GPU(图形处理器)上的并行性。首先，使用数据传输线程和GPU计算线程构造流水线并行框架，基于此框架直接实现异步输入输出(I/O)以减少GPU和网络存储之间数据传输所需的时间；其次，使用GPU的线程满载策略以使指令吞吐量最大化；最后，应用纹理缓存和常量缓存来减少片外存储器访问，并使用固定功能单元计算超越函数。实验结果表明：相比于Intel Xeon E5430 CPU上的算法串行版本，在nVidia Tesla C1060 GPU上的优化算法实现了约20倍的加速比。比较了算法在3种不同GPU架构上的性能，并给出了CPU与GPU结果在0.5×10-4误差限下仅0.3×10-5的浮点数绝对误差。
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