Background:Dose calculation plays a key role in treatment planning for radiotherapy

its performance and accuracy are crucial to the quality of treatment plans.Differential convolution/superposition algorithm is considered as an accurate algorithm for photon dose calculation;however

improvement on its computational efficiency is still desirable for such purpose as real time treatment planning.Purpose:The goal of this work is to boost the performance of differential convolution/superposition algorithm by devising a graphics processing unit (GPU) implementation so as to make the method practical for daily usage.Methods:In this work

we implemented a GPU-based version of the differential convolution/superposition algorithm

by which the most time-consuming parts are implemented on GPU.In order to fully utilize the GPU computing power

the algorithm is modified to match the GPU hardware architecture.Results:Compared with the algorithm completely running on CPU

the GPU-based algorithm can speed up 30-60 times on a Tesla C1060 with higher values corresponding to larger field size.Finally

we use y index to analyze the accuracy of calculation results

no matter one field or multi-field

homogeneous phantom or inhomogeneous phantom

the GPU implementation has the same accuracy as the CPU implementation. Conclusions:GPU is a useful solution for satisfying the increasing demands on computation speed and accuracy of dose calculation.The GPU-based differential convolution/superposition can be feasible and cost-efficient for satisfying the increasing demands for either computation speed or accuracy by advanced radiation therapy technologies.