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A distributed ASTRA toolbox.
Advanced Structural and Chemical Imaging Pub Date : 2016-12-07 , DOI: 10.1186/s40679-016-0032-z
Willem Jan Palenstijn 1 , Jeroen Bédorf 2 , Jan Sijbers 3 , K Joost Batenburg 4
Affiliation  

While iterative reconstruction algorithms for tomography have several advantages compared to standard backprojection methods, the adoption of such algorithms in large-scale imaging facilities is still limited, one of the key obstacles being their high computational load. Although GPU-enabled computing clusters are, in principle, powerful enough to carry out iterative reconstructions on large datasets in reasonable time, creating efficient distributed algorithms has so far remained a complex task, requiring low-level programming to deal with memory management and network communication. The ASTRA toolbox is a software toolbox that enables rapid development of GPU accelerated tomography algorithms. It contains GPU implementations of forward and backprojection operations for many scanning geometries, as well as a set of algorithms for iterative reconstruction. These algorithms are currently limited to using GPUs in a single workstation. In this paper, we present an extension of the ASTRA toolbox and its Python interface with implementations of forward projection, backprojection and the SIRT algorithm that can be distributed over multiple GPUs and multiple workstations, as well as the tools to write distributed versions of custom reconstruction algorithms, to make processing larger datasets with ASTRA feasible. As a result, algorithms that are implemented in a high-level conceptual script can run seamlessly on GPU-enabled computing clusters, up to 32 GPUs or more. Our approach is not limited to slice-based reconstruction, facilitating a direct portability of algorithms coded for parallel-beam synchrotron tomography to cone-beam laboratory tomography setups without making changes to the reconstruction algorithm.

中文翻译:

分布式ASTRA工具箱。

尽管与标准反投影方法相比,层析成像的迭代重建算法具有多个优点,但是在大型成像设备中采用此类算法仍然受到限制,主要障碍之一是它们的高计算量。尽管原则上启用GPU的计算集群功能强大,足以在合理的时间内对大型数据集进行迭代重建,但是到目前为止,创建有效的分布式算法仍然是一项复杂的任务,需要低级编程来处理内存管理和网络通信。ASTRA工具箱是一种软件工具箱,可用于快速开发GPU加速层析成像算法。它包含针对许多扫描几何体的正向和反向投影操作的GPU实现,以及用于迭代重建的一组算法。这些算法当前仅限于在单个工作站中使用GPU。在本文中,我们介绍了ASTRA工具箱及其Python界面的扩展,其中包括可分布在多个GPU和多个工作站上的正向投影,反向投影和SIRT算法的实现,以及用于编写分布式版本的自定义重构的工具算法,使使用ASTRA处理更大的数据集变得可行。结果,在高级概念脚本中实现的算法可以在支持GPU的计算集群(最多32个GPU或更多)上无缝运行。我们的方法不仅限于基于切片的重建,
更新日期:2016-12-07
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