A CUDA-enabled Parallel Implementation of Collaborative Filtering

Collaborative filtering (CF) is one of the essential algorithms in recommendation system. Based on the performance analysis, two computational kernels are identified. In order to accelerate CF on large-scale data, a CUDA-enabled parallel CF approach is proposed where an efficient data partition scheme is proposed as well. Various optimization techniques are also applied to maximize the performance of the GPU. The experimental results demonstrate up to 48× speedup on a single Tesla C2070 graphics card.