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.
论文详情