Performance Optimization of Light-Field Applications on GPU

Light-field image processing has been widely employed in many areas, from mobile devices to manufacturing applications. The fundamental process to extract the usable information requires significant computation with high-resolution raw image data. A graphics processing unit (GPU) is used to exploit the data parallelism as in general image processing applications. However, the sparse memory access pattern of the applications reduced the performance of GPU devices for both systematic and algorithmic reasons. Thus, we propose an optimization technique which redesigns the memory access pattern of the applications to alleviate the memory bottleneck of rendering application and to increase the data reusability for depth extraction application. We evaluated our optimized implementations with the state-of-the-art algorithm implementations on several GPUs where all implementations were optimally configured for each specific device. Our proposed optimization increased the performance of rendering application on GTX-780 GPU by 30% and depth extraction application on GTX-780 and GTX-980 GPUs by 82% and 18%, respectively, compared with the original implementations.