Adaptable Two-Dimension Sliding Windows on NVIDIA GPUs with Runtime Compilation

Abstract

For some classes of problems, NVIDIA CUDA abstraction and hardware properties combine with problem characteristics to limit the specific problem instances that can be effectively accelerated. As a real-world example, a two-dimensional correlation-based template-matching MATLAB application is considered. While this problem has a well known solution for the common case of linear image filtering -- small fixed templates of a known size applied to a much larger image -- the application considered here uses large arbitrarily-sized templates, up to 156-by-116 pixels, with small search spaces containing no more than 703 window positions per template. Our CUDA implementation approach employs template tiling and problem-specific kernel compilation to achieve speedups of up to 15 when compared to an optimized multi-threaded implementation running on a 3.33 GHz four core Intel Nehalem processor. Tiling the template enables exploiting the parallelism within the computation and shared memory usage. At the same time, problem-specific kernel compilation allows greater levels of adaptability than would otherwise be possible.