地球物理反演中的混合并行计算方法研究- - 以MT Occam并行反演为例

针对MT Occam反演计算速度不佳的问题，研究基于多计算机、CPU多核、GPU众核的混合并行计算方法。建立一个异构、多层次、自顶向下逐步细化的并行编程模型，顶层采用消息传递方式，通过网络通信实现节点间并行，中、下层（CPU多核和GPU流处理单元）采用OpenMP和CUDA技术通过共享内存方式实现节点内并行。研究异构模式下层次间的通信方式、任务映射方式、地址转换方法和计算粒度与通信效率的关联性，分析、对比不同的混合并行计算模式下的编程复杂度及计算效率，寻找最优方案。全面挖掘MT Occam反演的内在并行分量，研究反演问题的分解方式，通过计算任务多次分解，形成不同粒度结合的优化任务集，实现任务到不同层次部件的合理分配及计算负载的良好均衡。通过对节点性能的动态监控，实现系统对不同硬件配置、不同反演规模（频点、测点及μ扫描点数）的自动适应。

Aim at solving the problem of low calculation speed of the MT Occam inversion, this project research on hybrid parallel computation methods based on multi-computers, multi-CPU cores and mass GPU SP units, realizing a top-down hierarchical and gradually refined parallel programming model. With the massage passing, the top level of the model will realize the inter-node parallel computation through network communication, while the middle and bottom levels (multi-CPU cores and GPU SP units) realize the intra-node parallel computation through memory sharing. The project will study heterogeneous communication patterns between levels, task mapping methods, address translating methods and the relevance between task granularity and communication efficiency. By analyzing and comparing the programming complexity and computing efficiency for different hybrid parallel computation pattern, the best alternatives will be determined. The MT Occam inversion parallel components lie in the computation will be excavated, and, on the base of this, the inversion problem decomposition methods will be studied. By task secondary decomposition, the project will generate optimized task sets of different granularity and realize the task mapping to different levels units, ensuring satisfied load balancing of the calculation. By dynamic monitoring the node performance, the project will realize the system automatic adaptation to different hardware configuration and different inversion scale (different number of frequency points, survey points and μ scanning points).