AForge is a popular .Net library for image processing and computer vision. It features a lot of image filters, noise generators, or other image-related algorithms. Since it’s written in C#, and given the very parallel nature of most of image processing algorithms, we tried to hybridize some of them. AForge code make heavy use of […]
Tags: CUDA, Image Processing, Intrinsics
A wide variety of image processing algorithms are typically parallel. However, depending on filter-size or neighborhood search pattern, memory access is critical for performances. We’ll show how loop reordering and memory locality fine-tuning help achieve best performance. Using Hybridizer to automate Java byte-code transformation to CUDA source code, and using new CUDA feature Run Time […]
Tags: CUDA, Image Processing, Java