Three-Level Parallelism for FDK Algorithm Using Multi-GPU Based Cluster System

Parallel computing is applied in computed tomography to shorten the imaging time and achieves outstanding effects. But image reconstruction algorithm remains to be significantly time-consuming when faced with the large data sets which decreases the efficiency of imaging process. Single graphic card cannot solve this problem because of the limitation on video memory. This paper presents a parallel method for FDK algorithm using multi-GPU based cluster system. In the method, the cluster system is divided into three levels: nodes, GPUs and single GPU. Corresponding parallel strategies are designed for different levels according to the algorithm characteristics and hardware structures. The experiments show that the multi-GPU based cluster system is able to achieve the same precision with the single node meanwhile it will gain higher speedup ratio with the increasing number of nodes or GPUs. The computing time of whole reconstruction reduces to almost half of the origin by doubling the computing devices.