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High-Performance Blob-Based Iterative Reconstruction of Electron Tomography on Multi-GPUs

机译:基于高性能Blob的多GPU电子层析成像的迭代重建。

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Three-dimensional (3D) reconstruction of electron tomography (ET) has emerged as a leading technique to elucidate the molecular structures of complex biological specimens. Blob-based iterative methods are advantageous reconstruction methods for 3D reconstruction of ET, but demand huge computational costs. Multiple Graphic processing units (multi-GPUs) offer an affordable platform to meet these demands, nevertheless, are not efficiently used owing to a synchronous communication scheme and the limited available memory of GPUs. We propose a multilevel parallel scheme combined with an asynchronous communication scheme and a blob-ELLR data structure. The asynchronous communication scheme is used to minimize the idle GPU time. The blob-ELLR data structure only needs nearly 1/16 of the storage space in comparison with ELLPACK-R (ELLR) data structure and yields significant acceleration. Experimental results indicate that the multilevel parallel scheme allows efficient implementations of 3D reconstruction of ET on multi-GPUs, without loss any resolution.
机译:电子断层扫描(ET)的三维(3D)重建已成为阐明复杂生物标本分子结构的一种领先技术。基于斑点的迭代方法是用于ET的3D重建的有利重建方法,但需要巨大的计算成本。多个图形处理单元(multi-GPU)提供了一个负担得起的平台来满足这些需求,但是由于同步通信方案和GPU的可用内存有限,因此无法有效使用。我们提出了一种多级并行方案,该方案结合了异步通信方案和Blob-ELLR数据结构。异步通信方案用于最小化空闲GPU时间。与ELLPACK-R(ELLR)数据结构相比,blob-ELLR数据结构仅需要近16%的存储空间,并产生显着的加速。实验结果表明,多级并行方案允许在多GPU上高效实现ET的3D重建,而不会损失任何分辨率。

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