基于曲线投影模型的电子断层三维重构并行算法

大尺度高精度的电子断层三维重构可以获得在更大视角下的生物大分子三维结构的细节信息。但研究尺度的增大给获取高精度重构结果和缩短数据处理时间带来了巨大的挑战。TxBR提出的曲线模型显著提高了重构的精度,但其计算比直线模型更复杂耗时,且在曲线模型下,之前的并行策略不再可行。针对这一问题,提出了一种在GPU平台上实现的针对曲线模型的分块迭代并行算法。通过对曲线模型的研究发现,曲线模型具有一定的空间局域性,利用这种性质提出了一种纵向的分块方式。在算法的实现阶段,提出一个基于页的数据传输策略,从而能够去除冗余的数据传输,减少数据传输带来的时间消耗。实验结果显示,本算法可接近40倍的加速比。

A Parallel Reconstruction Algorithm Based on Curvilinear Projection Model in Tomography

Large-field high-resolution electron tomography enables visualizing detailed mechanisms under globalstructure. As field enlarges, the distortions of reconstruction and processing time become more critical. TxBR has proposeda curvilinear projection model, which can dramatically improve the quality of reconstruction. But its computation is morecomplex and time-consuming. Furthermore, previous parallel strategies are not suitable for curvilinear projection model.In this work, a block iterative parallel algorithm using curvilinear projection model on GPU platform was proposed. Bystudying the locality of curvilinear projection model, we proposed a vertical data decomposition method. We also adopta page-based data transfer scheme to reduce the processing time. Experimental results show that our method can yieldspeedups of approximate 40 times.