Accelerating 3D Digital Differential Analyzer Ray Tracing Algorithm on the GPU Using CUDA

We present a novel optimized 3DDDA ray tracing algorithm on GPUs with CUDA architecture. The grid data is built on the host and then copied to the device. The whole traversing procedure is done on the GPU device. So it just needs the data transformation between the host and the device once. The results show that the CUDA implementation parallel code using different block size on GPU achieves 2.9x to 5.9x faster speed compared with the CPU implementation. And the performance is significantly influenced by the size of the CUDA block. 3DDDA algorithm on CUDA architecture can really get the performance accelerated with a reasonable block size, but it is difficult to play high-end GPU performance. The main reason is that 3DDDA algorithm for memory access is discontinuous, so it is difficult to play high-frequency performance of RAM DDR5.