An Efficient Approach for Image Border Handling on GPUs via Iteration Space Partitioning

Border handling is a crucial step in many image processing applications. For stencil kernels such as the Gaussian filter where a window of pixels is required to compute an output pixel, the border of the image needs to be handled differently than the body of the image. To prevent out-of-bounds accesses, conditional statements need to be inserted into the pixel address calculation. This introduces significant overhead, especially on hardware accelerators such as GPUs. Existing research efforts mostly focus on image body computations, while neglecting the importance of border handling or treating it as a corner case. In this paper, we propose an efficient border handling approach for GPUs. Our approach is based on iteration space partitioning, which is a technique similar to index-set splitting, a well-known general-purpose compiler optimization. We present a detailed systematic analysis including an analytic model that quantitatively evaluates the benefits as well as the costs of the transformation. In addition, manually implementing the border handling technique is a tedious task and not portable at all. We integrate our approach into an image processing DSL and a source-to-source compiler called Hipacc to relieve the burden and increase programmers’ productivity. We evaluate over five commonly used image processing applications on two Nvidia GPUs. Results show our proposed approach achieves a geometric mean speedup of up to 87% over a naive implementation.