Fast dose calculation of convolution/superposition in radiotherapy based on multi GPU heterogeneous model

Abstract

BackgroundThe accuracy of Convolution/Superposition (CS) algorithm is considered to be next to Monte Carlo algorithm (MC) for radiotherapy dose calculation algorithm. Although the calculating speed of this algorithm is much faster than that of MC, its calculating speed can not fully meet the clinical requirements. With the aid of a single graphics processing unit GPU (Tesla C1060), the CS algorithm can be accelerated to 60 times faster than the traditional CPU serial calculation. The calculating time for single field is about 1 min which can be used in some simple three dimensional conformal radiotherapy planning (3DCRT), but this calculating speed does not satisfy the speed need for intensity modulated radiation therapy (IMRT) planning.PurposeThis study aims to explore a faster calculating speed solution of CS algorithm applied to IMRT with multi GPU.MethodsThe acceleration scheme of CPU + multi GPU heterogeneous model was analyzed by using different number of GPUs. High-end GPU, i.e., Tesla C2015, was used for experimental test of CS algorithm executing under the compute unified device architecture (CUDA) platform. Speeds of different number GPUs combined with CPU were compared to find the suitable solution.ResultsThe experimental results show that the speedup of CS algorithm is not completely linear with the number of GPUs. With reasonable number of GPUs and optimized program codes, the computing time of CS algorithm for single field radiotherapy dose calculation can be reduced to 9 seconds by using 7 high-end GPUs (Tesla C2015), 207 times faster than that of a single CPU.ConclusionsWith the implementation of multi GPU heterogeneous model and code optimization, the CS algorithm can be applied to clinical IMRT treatment planning.

Keywords

• convolution/superposition algorithm
• multi gpu
• dose calculation