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The inherent massive parallelism of cellular neural networks makes them an ideal computational platform for kernel-based algorithms and image processing. General-purpose GPUs provide similar massive parallelism, but it can be difficult to design algorithms to make optimal use of the hardware. The presented research includes a GPU abstraction based on cellular neural networks. The abstraction offers a simplified view of massively parallel computation which remains reasonably efficient. An image processing library with visualization software has been developed to showcase the flexibility and power of cellular computation on GPUs. Benchmarks of the library indicate that commodity GPUs can be used to significantly accelerate CNN research and offer a viable alternative to CPU-based image processing algorithms.