high performance computing on graphics processing units: hgpu.org

In a recommendation system, customers’ preferences are encoded into vectors, and finding the nearest vectors to each vector is an essential part. We define this part of problem as a $k$-nearest vector problem and give an effective algorithm to solve it on multiple graphics processor units (GPUs). By an experiment, we show that when the size of the problem is large, an implementation of the algorithm on two GPUs runs more than 260 times faster than a single core implementation on a latest CPU. We also show that our algorithm scales well with respect to the number of GPUs.