Analytical Performance Prediction for Iterative Reconstruction Techniques in Electron Tomography of Biological Structures
Analytical Performance Prediction for Iterative Reconstruction Techniques in Electron Tomography of Biological Structures
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DOI：
10.1177/1094342010370575
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发表时间：
2010-11-01
影响因子：
3.1
通讯作者：
Luque, Emilio
中科院分区：
文献类型：
--
作者：
Cecilia Fritzsche, Paula;Fernandez, Jose-Jesus;Luque, Emilio
根据投影数据对结构进行三维重建的问题广泛存在于各个领域。代数重建技术 (ART) 是从投影图像中恢复三维物体结构的迭代过程。由于对计算资源的高需求，ART 在七十年代被驳回。在接下来的几年里，快速收敛的图像重建计算机算法被设计、实现、评估并最终优化。然而，报告的实验数据超出了单个 CPU 的计算潜力以及 I/O 能力。最近有趣的研究旨在获取并行化策略的经验，并证明生物环境中大规模并行处理方法的有效性。这使得高性能并行计算的性能预测研究变得至关重要。迭代重建技术（IRT）的性能预测模型将提供并行计算机算法的额外知识，并预测其在特定条件或硬件配置下的行为。此外，性能分析的目标是找到产生最佳整体性能的参数值集。不仅这些系统的最终用户对预测性能感兴趣，计算机设计人员和专业工程师也对预测性能感兴趣。本文讨论了 IRT（分量平均的块迭代版本，BICAV）并行化的分析性能预测模型的推导和评估，并强调了并行计算机在图像重建中的重要作用。通过具体步骤分析技术的行为，以创建问题的分析表述。 BPTomo 是一个使用 IRT 进行断层扫描重建的并行分布式应用程序。通过将代表性数据集的估计时间与在两个 PC 集群上测量的 BPTomo 计算时间进行比较来验证分析性能预测模型。分析模型被证明是相当准确的。估计时间和测量时间之间的百分比偏差小于 12%。
The problem of the three-dimensional reconstruction of structures from projection data occurs in a wide range of areas. Algebraic reconstruction techniques (ART) are iterative procedures for recovering the structure of three-dimensional objects from projection images. ART was dismissed during the seventies due to the high demands on computing resources. In the following years, computer algorithms for image reconstruction with fast convergence were designed, implemented, evaluated, and, finally, optimized. Nevertheless, the reported experimental data exceed computing potential for a single CPU as well as I/O capability. Interesting recent research aims at acquiring experience with parallelization strategies and at demonstrating the effectiveness of the massively parallel processing approach in biological environments. This makes the investigation of performance prediction for high-performance parallel computing of paramount importance. A performance prediction model for iterative reconstruction techniques (IRT) would provide additional knowledge of the parallel computer algorithm and predict its behavior under specific conditions or hardware configurations. Also, a goal of performance analysis is to find the set of parameter values that produces the best overall performance. Not only the end-users of these systems have a vested interest in predicting performance but also the computer designers and the professional engineers. This article addresses the derivation and evaluation of an analytical performance prediction model for a parallelization of IRT (block iterative version of the component averaging, BICAV) and emphasizes the essential role of parallel computers in image reconstruction. The techniques' behavior is analyzed through specifics steps to create an analytical formulation of the problem. BPTomo is a parallel distributed application for tomographic reconstruction that uses IRT. The analytical performance prediction model is validated by comparison of the estimated times for representative datasets against BPTomo computation times measured on two PC clusters. The analytical model is shown to be quite accurate. The percentage deviation between estimated and measured times is less than 12%.