【摘要】 转换波Kirchhoff叠前时间偏移可以实现全空间三维转换波资料的准确成像。但转换波叠前偏移数据量巨大,而且需偏移迭代多次来寻找匹配的偏移速度模型,导致偏移处理周期长、效率低,限制了转换波偏移技术在生产上的应用规模。目前解决海量运算问题的方法主要是应用CPU集群来提高计算效率,但集群存在成本高、功耗大、占用空间大、维护成本高等缺点。给出了一种基于MPI(Message Passing Interface)和CUDA的转换波Kirchhoff叠前时间偏移并行算法,将细粒度线程级的GPU(Graphic Processing Unit)并行计算融入粗粒度进程级MPI并行编程模型。利用实际转换波数据分别在CPU(单核)、GPU(单卡)、MPI和GPU(2个节点)测试平台上对算法性能进行了测试,结果表明,MPI和GPU(2个节点)的计算速度是CPU(单核)的近400倍,可以大幅度提高转换波Kirchhoff叠前时间偏移的计算效率,降低计算成本。更多还原

【Abstract】 Converted-wave Kirchhoff prestack time migration can image accurately in 3-D space.However,the prestack time migration generates huge computation amount and need migration for several times to discover matched migration velocity model,which leads to long processing period and low efficiency,and restricts the application scale in production.Nowadays,CPU-cluster computation is applied widely to solve the problem,but the CPU-cluster has some disadvantages such as high cost,large power consumption,large space occupation and high maintenance cost.In order to improve the time-consuming and save the computing cost,we proposed a parallel algorithm of converted-wave Kirchhoff prestack time migration based on MPI+CUDA,which merged the GPU parallel computation of fine-grained thread-level into the MPI parallel programming model of coarse-grained process-level.We tested the performance of the parallel algorithm on CPU(single core),GPU(single card),MPI+GPU(2 nodes).The testing results indicate that the computation speed of the MPI+GPU(2 nodes) platform is nearly 400 times of the CPU’s(single core),largely improves the computation efficiency of converted-wave Kirchhoff prestack time migration and decreases the computation cost.更多还原