Abstract: Photon mapping is a global illumination algorithm which is composed of two steps: photon tracing and photon searching. During photon searching step, each shading point needs to search the photon-tree to find k-neighbouring photons for reflected radiance estimation. As the number of shading points and the size of photon-tree are dramatically large, the photon searching step is time consuming. We proposed a parallel photon searching algorithm by using radiance estimation approach for coherent shading points on the Intel Many^® Integrated Core (MIC) Architecture. In order to efficiently use single instruction multiple data (SIMD) units, shading points are clustered by similarity first (every cluster contains 16 shading-points), and an initial neighbouring scope is searched from the photon-tree for each cluster. Then we use 16-wide SIMD units by performing K-NN searching from the initial neighbouring scope for those 16 shading-points in a cluster in parallel. We use the method to simulate some global illumination scenes on Intel^® Xeon^® Processors and Intel^® Xeon^® Phi^TM Coprocessors. The comparison results with existing photon mapping techniques indicate that our method gives significant improvement in speed with the same accuracy.