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Parallel programming for OSEM reconstruction with MPI, OpenMP, and hybrid MPI-OpenMP

机译:使用MPI,OpenMP和混合MPI-OpenMP进行OSEM重建的并行编程

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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:为了提高用于3D PET图像重建的有序子集期望最大化(OSEM)算法的并行效率(PE),我们在1)OpenMP和2)混合消息传递接口(MPI)-OpenMP模型上实现了该算法。标准MPI实施的基础。这样做的动机是减少处理器间的数据交换时间,这是使用大量处理器时MPI模型的主要PE限制因素。 OpenMP模型使用了一种细粒度的方法,并且对于真正的共享内存和单系统映像(SSI)分布式共享内存体系结构,仅显示多达2-3个处理器的显着加速。与纯MPI方法相比,在大量并行处理器上,混合MPI-OpenMP模型在加速因子方面实现了约10%的一致改进。随着大型对称多处理器(SMP)机器的集群持续变得更具成本效益,我们希望这种混合MPI-OpenMP方法对于加速3D PET重建以及具有类似计算特征的其他应用的价值越来越高

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