GPU enhanced simulation of angiogenesis

Abstract

In the paper we present the use of graphic processor units to accelerate the most time-consuming stages of a simulation of angiogenesis and tumor growth. By the use of advanced CUDA mechanisms such as shared memory, textures and atomic operations, we managed to speed up the CUDA kernels by a factor of 57x. However, in our simulation we used the GPU as a co-processor and data from CPU was copied back and forth in each phase. It decreased the speedup of rewritten stages by 40%. We showed that the performance of the entire simulation can be improved by a factor of 10 up to 20.