Voting algorithms, such as histogram and Hough transforms, are frequently used algorithms in various domains, such as statistics and image processing. Algorithms in these domains may be accelerated using GPUs. Implementing voting algorithms efficiently on a GPU however is far from trivial due to irregularities and unpredictable memory accesses. Existing GPU implementations therefore target only specific voting algorithms while we propose in this work a methodology which targets voting algorithms in general. This methodology is used in GPU-VOTE, a framework to accelerate current and future voting algorithms on a GPU without significant programming effort. We classify voting algorithms into four categories. We describe a transformation to merge categories which enables GPU-VOTE to have a single implementation for all voting algorithms. Despite the generality of GPU-VOTE, being able to handle various voting algorithms, its performance is not compromised. Compared to recently published GPU implementations of the Hough transform and the histogram algorithms, GPU-VOTE yields a 11% and 38% lower execution time respectively. Additionally, we give an accurate and intuitive performance prediction model for the generalized GPU voting algorithm. Our model can predict the execution time of GPU-VOTE within an average absolute error of 5%.
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