Debasis Mitra, Hui Pan, Fares Alhassen, Youngho Seo
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引用次数: 5
摘要
在这项工作中，我们并行化了最大似然期望最大化(MLEM)和有序子集期望最大化(OSEM)算法，以提高多针孔SPECT和锥bean CT数据的重建效率。我们在通用图形处理单元(GPGPU)上实现了算法的并行化版本:NVIDIA Tesla M2070 GPU的448核，每线程计算6GB RAM。我们将它们的运行时间与相应CPU实现的运行时间进行了比较，这些CPU实现运行在AMD Opteron 6128的8核CPU上，具有32 GB RAM。我们进一步展示了线程平衡的优化如何加快GPU实现的速度。
Parallelization of Iterative Reconstruction Algorithms in Multiple Modalities.
In this work we have parallelized the Maximum Likelihood Expectation-Maximization (MLEM) and Ordered Subset Expectation Maximization (OSEM) algorithms for improving efficiency of reconstructions of multiple pinholes SPECT, and cone-bean CT data. We implemented the parallelized versions of the algorithms on a General Purpose Graphic Processing Unit (GPGPU): 448 cores of a NVIDIA Tesla M2070 GPU with 6GB RAM per thread of computing. We compared their run times against those from the corresponding CPU implementations running on 8 cores CPU of an AMD Opteron 6128 with 32 GB RAM. We have further shown how an optimization of thread balancing can accelerate the speed of the GPU implementation.
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