Multi-GPU Acceleration of Optical Flow Computation in Visual Functional Simulation

Numerical simulation for visual processing of the human brain is one of time-consuming applications. This paper shows acceleration techniques for a simulation program of the visual processing. We parallelize convolution calculations, which are core operations, which the simulation program requests, on a GPU-accelerated PC cluster. Our implementation includes three improvement points. Firstly, we consider efficient data mapping onto global and shared memories1 of the GPU. Secondly, multiple convolutions for the same input data are computed by each node´s GPU, referred to as package execution. Finally, an input 2-dimensional image is divided into regions and convolutions for these regions are executed in parallel utilizing MPI (Message Passing Interface). Our experimental results show a linear speedup up to 12 nodes in the PC cluster for the convolution program. We also show the effects of the package execution and reduced communication on NVIDIA tesla C1060 and C2070, respectively.