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Parallel Programming for OSEM Reconstruction with MPI, OpenMP, and Hybrid MPI-OpenMP

机译:使用MPI,OpenMP和Hybrid MPI-OpenMP并行编程OSEM重建

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To improve the parallel efficiency (PE) of the Ordered-Subsets Expectation-Maximization (OSEM) algorithm for 3D PET image reconstruction, we implemented the algorithm with 1) an OpenMP and 2) a hybrid Message Passing Interface (MPI)-OpenMP model on the basis of a standard MPI implementation. The motivation was to reduce the inter-processor data exchange time which was the dominant PE limiting factor of the MPI model when large number of processors was used. The OpenMP model used a fine-grained approach and showed significant speedup only up to 2-3 processors for both the true shared memory and the single system image (SSI) distributed shared memory architectures. The hybrid MPI-OpenMP model achieved a consistent improvement of ~10% in terms of speedup factor on a large number of parallel processors compared to the pure MPI approach. As clusters of larger symmetric multiprocessor (SMP) machines continue to become more cost effective, we expect this hybrid MPI-OpenMP approach to be increasingly valuable to accelerate 3D PET reconstructions, and other applications with similar computational characteristics.
机译:为了提高订购子集的并行效率(PE)预期 - 最大化(OSEM)算法3D PET图像重建,我们实现了1)openMP和2)的算法,2)混合消息传递接口(MPI)-Openmp模型标准MPI实施的基础。动机是减少当使用大量处理器时是MPI模型的主导PE限制因子的处理器间数据交换时间。 OpenMP模型使用细粒度的方法,并且仅显示了True共享内存和单个系统图像(SSI)分布式共享内存架构的2-3个处理器。与纯MPI方法相比,混合MPI-OPENMP模型在大量并行处理器上的加速因子方面实现了〜10%的一致性提高。由于较大对称多处理器(SMP)机器的集群继续变得更具成本效益,我们预计这种混合MPI-OpenMP方法越来越有价值,以加速3D宠物重建,以及具有类似计算特征的其他应用。

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