MPI和OpenMP混合并行模型下的遥感编目信息检索

目的 空间位置检索是遥感影像检索中的关键步骤,为进一步提高海量遥感影像编目数据定位检索效率,降低误检率,提出一种基于MPI和OpenMP混合编程模型对射线法进行多层次并行化实现。方法 首先完善传统射线法处理点在多边形边上以及射线与边的端点相交的情况;其次采用MPI实现基于程序层面多机并行,OpenMP实现算法层面单机多线程并行,通过开启多个线程同时处理多边形的各个点,判断它们是否在另一个多边形的内部。结果 当系统中所有节点开启线程数之和等于主节点的最佳线程数时,全局计算速度达到最佳。混合并行算法相比串行算法检索时间减少50%以上,效率更高。结论 MPI+OpenMP混合并行比普通的串行执行、单纯MPI并行或单纯OpenMP并行执行空间定位检索算法效率显著提高,这种并行方案普遍适用于集群环境下的并行程序,并且可以进一步拓展到其他图像处理算法领域。

Remote sensing resources parallel retrieval based on MPI and OpenMP

Objective Spatial location retrieval is one of important pretreatment steps in remote sensing data searching. In order to improve the original Ray-algorithm performance and reduce the false alarm rate in retrieving catalogue data from massive remote sensing images, a new hybrid parallel strategy has been proposed based on MPI and OpenMP to implement the Ray-algorithm. Method First, two improved processing methods have been added to the classical Ray-algorithm to deal with two special cases between the point on the edge of the polygon and the ray intersecting on the endpoint. And then, we use multi-process based on MPI to do parallel computing on a PC cluster in program procedure and multi-threads based on OpenMP to do parallel algorithm implementation in which many threads are started synchronously to deal with each point of the polygon which is in another point or not. Result The experimental results show that the whole system will reach better speedup when the sum of started threads in all nodes is equal to the optimal threads number of the main node.Compared with serial algorithm, the retrieval time of hybrid parallel algorithm reduced by more than 50% and this algorithm is more efficient.Conclusion The hybrid parallel strategy based on MPI and OpenMP is better than common serial execution and parallel execution based on MPI and OpenMP respectively. The hybrid parallel mode is generally applicable to the cluster environment parallel programs and it can used to deal with other images processing algorithms with parallel features.