Reumann Matthias, Fitch Blake G, Rayshubskiy Aleksandr, Keller David U J, Seemann Gunnar, Dossel Olaf, Pitman Michael C, Rice John J
Computational Biology Center, IBM TJ Watson Research Center, Yorktown Heights, 1101 Kitchawan Road, Route 134, NY 10598, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2799-802. doi: 10.1109/IEMBS.2009.5333803.
Orthogonal recursive bisection (ORB) algorithm can be used as data decomposition strategy to distribute a large data set of a cardiac model to a distributed memory supercomputer. It has been shown previously that good scaling results can be achieved using the ORB algorithm for data decomposition. However, the ORB algorithm depends on the distribution of computational load of each element in the data set. In this work we investigated the dependence of data decomposition and load balancing on different rotations of the anatomical data set to achieve optimization in load balancing. The anatomical data set was given by both ventricles of the Visible Female data set in a 0.2 mm resolution. Fiber orientation was included. The data set was rotated by 90 degrees around x, y and z axis, respectively. By either translating or by simply taking the magnitude of the resulting negative coordinates we were able to create 14 data set of the same anatomy with different orientation and position in the overall volume. Computation load ratios for non - tissue vs. tissue elements used in the data decomposition were 1:1, 1:2, 1:5, 1:10, 1:25, 1:38.85, 1:50 and 1:100 to investigate the effect of different load ratios on the data decomposition. The ten Tusscher et al. (2004) electrophysiological cell model was used in monodomain simulations of 1 ms simulation time to compare performance using the different data sets and orientations. The simulations were carried out for load ratio 1:10, 1:25 and 1:38.85 on a 512 processor partition of the IBM Blue Gene/L supercomputer. Th results show that the data decomposition does depend on the orientation and position of the anatomy in the global volume. The difference in total run time between the data sets is 10 s for a simulation time of 1 ms. This yields a difference of about 28 h for a simulation of 10 s simulation time. However, given larger processor partitions, the difference in run time decreases and becomes less significant. Depending on the processor partition size, future work will have to consider the orientation of the anatomy in the global volume for longer simulation runs.
正交递归二分法（ORB）算法可作为一种数据分解策略，用于将心脏模型的大数据集分配到分布式内存超级计算机上。此前已有研究表明，使用ORB算法进行数据分解可获得良好的扩展性结果。然而，ORB算法依赖于数据集中每个元素的计算负载分布。在本研究中，我们调查了数据分解和负载平衡对解剖数据集不同旋转的依赖性，以实现负载平衡的优化。解剖数据集由可见女性数据集的两个心室提供，分辨率为0.2毫米，包含纤维方向。数据集分别绕x、y和z轴旋转90度。通过平移或简单地取所得负坐标的大小，我们能够创建14个具有相同解剖结构但在总体积中具有不同方向和位置的数据集。在数据分解中使用的非组织与组织元素的计算负载比为1:1、1:2、1:5、1:10、1:25、1:38.85、1:50和1:100，以研究不同负载比对数据分解的影响。使用Ten Tusscher等人（2004年）的电生理细胞模型进行1毫秒模拟时间的单域模拟，以比较使用不同数据集和方向时的性能。在IBM Blue Gene/L超级计算机的512处理器分区上，针对负载比1:10、1:25和1:38.85进行了模拟。结果表明，数据分解确实取决于解剖结构在全局体积中的方向和位置。对于1毫秒的模拟时间，数据集之间的总运行时间差异为10秒。对于10秒的模拟时间，这会产生约28小时的差异。然而，在更大的处理器分区下，运行时间的差异会减小且变得不那么显著。根据处理器分区大小不同，未来的工作必须考虑在更长模拟运行中解剖结构在全局体积中的方向。
