An Analysis of OpenCL for Portable Imaging

摘要

In the development of commercial imaging based software applications there is the challenge of trying to provide high performance imaging algorithms that are utilized by multiple applications running on a range of hardware platforms. Many times the imaging algorithms will need to be run on workstations, smartphones, tablets, or other devices that may have different CPU and possibly GPU/DSP hardware. Implementing software on the cloud infrastructure can place limitations on the hardware capabilities imaging software can take advantage of. In the face of these challenges, OpenCL provides a promising framework to write imaging algorithms in. It promises that algorithms can be written once and then deployed on many different hardware configurations; GPU, DSP, CPU, etc... and take advantage of the computing features of particular hardware. In this paper we look at how well OpenCL delivers on this multi target promise for different image processing algorithms. Both GPU (Nvidia and AMD) and CPU (AMD and Intel) platforms are explored to see how OpenCL does in using the same code on different hardware. We also compare OpenCL with optimized CPU and GPU (CUDA) versions of the same imaging algorithms. Our findings are presented and we share some interesting observations in using OpenCL. The imaging algorithms include a basic CMYK to RGB color transformation, 25 x 25 floating point convolution, and visual attention saliency map calculation. The saliency map algorithm is complex and includes many different imaging calculations; difference of Gaussian, color features, image statistics, FFT filtering, and assorted other algorithms. Looking at such a complex set of algorithms gives a good real world test for comparing the different platforms with.