Three-dimensional reconstruction of electron tomography using graphic processing units (GPUs)

Three-dimensional (3D) reconstruction of electron tomography (ET) has emerged as a leading technique to elucidate the molecular structures of complex biological specimens. Iterative methods using blob basis functions are advantageous reconstruction methods due to their good performance especially under noisy and limited-angle conditions. However, iterative reconstruction algorithms for ET pose tremendous computational challenges. Graphic processing units (GPUs) offer an affordable platform to meet these demands. Nevertheless, due to the limited available memory of GPUs, the weighted matrix involved by iterative methods cannot be located into GPUs especially for the large images. To meet high computational demands, we propose a multilevel parallel scheme to perform iterative algorithm reconstruction using blob on GPUs. In order to address the large memory requirements of the weighted matrix, we also present a matrix storage technique, called blobELL-R, suitable for GPUs. In the storage technique, several geometric related symmetry relationships have been exploited to significantly reduce the storage space. Experimental results indicate that the multilevel parallel reconstruction scheme on GPUs can achieve high and stable speedups. The blobELL-R data structure only needs nearly 1/16 of the storage space in comparison with ELLPACK-R (ELL-R) storage structure and yields significant acceleration compared to the standard and matrix with CRS implementations on CPU.