Accelerate numerical diffusion solver of 2D multi- scale and multi-resolution agent-based brain cancer model by employing graphics processing unit technology

Diffusion model is increasingly employed to simulate diffusion of biological compounds including nutrient, oxygen and chemoattractants in the agent-based model (ABM). However, it takes long compute time to employ conventional numerical methods such as alternating direction implicit (ADI) method to approximate the exact solution of the diffusion processed by sequential computing algorithm. To overcome this limitation, our study employs cutting-edge graphics processing unit (GPU) technology to speed up the conventional sequential numerical solver for diffusion and incorporates our proposed parallel computing algorithms into our well developed 2D multi-scale and multi-resolution agent-based brain cancer model to break through the bottleneck of the ABM that it is hard to simulate the large system restricted to the limited compute resource and memory. Our simulation outputs demonstrate that ABM model can be used to simulate real-time actual cancer progression with relative fine grids by using GPU based parallel computing algorithm.