An interior-point implementation developed and tuned for radiation therapy treatment planning
An interior-point implementation developed and tuned for radiation therapy treatment planning
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DOI：
10.1007/s10589-017-9919-4
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发表时间：
2017-11-01
影响因子：
2.2
通讯作者：
Heijmen, Ben
中科院分区：
文献类型：
--
作者：
Breedveld, Sebastiaan;van den Berg, Bas;Heijmen, Ben
虽然内点方法共享相同的基本原理，但实现决定了实际性能。为了获得最高的效率，不同的应用程序可能需要不同的调优实现。在这篇文章中，我们描述了一个专门为优化放射治疗而设计的实施方案。这些问题是大规模的非线性(有时是非凸)约束优化问题，由稀疏和稠密的数据组成。利用了几个特定于应用程序的属性来提高效率。置换、平铺和混合精度算法允许算法以最佳方式处理混合的密集和稀疏数据矩阵(使此步骤快2.2倍，总体运行时间减少)和可伸缩性(与单线程性能相比，16个线程的加速系数为9.8，而较少优化的实现的加速系数为7.7)。预定义的成本函数是硬编码的，并且计算代价高昂的二阶导数以规范形式编写，如果为相同的临床结构定义了多个成本函数，则将其组合。然后使用比例矩阵-矩阵乘积计算导数。一种基于背景知识的廉价初始化策略减少了迭代次数。我们还提出了一种新的Mehrotra-Gondzio组合方法。该算法在一个由120名患者组成的数据集上进行了广泛的测试，这些患者分布在6个肿瘤部位/途径上。该测试数据集是公开可用的。
While interior-point methods share the same fundamentals, the implementation determines the actual performance. In order to attain the highest efficiency, different applications may require differently tuned implementations. In this paper we describe an implementation specifically designed for optimisation in radiation therapy. These problems are large-scale nonlinear (and sometimes nonconvex) constrained optimisation problems, consisting of both sparse and dense data. Several application-specific properties are exploited to enhance efficiency. Permuting, tiling and mixed precision arithmetic allow the algorithm to optimally process the mixed dense and sparse data matrices (making this step 2.2 times faster, and overall runtime reduction of ) and scalability (16 threads resulted in a speed-up factor of 9.8 compared to singlethreaded performance, against a speed-up factor of 7.7 for the less optimised implementation). Predefined cost-functions are hard-coded and the computationally expensive second derivatives are written in canonical form, and combined if multiple cost-functions are defined for the same clinical structure. The derivatives are then computed using a scaled matrix-matrix product. A cheap initialisation strategy based on the background knowledge reduces the number of iterations by . We also propose a novel combined Mehrotra-Gondzio approach. The algorithm is extensively tested on a dataset consisting of 120 patients, distributed over 6 tumour sites/approaches. This test dataset is made publicly available.