A KNN Optimization Based on GPU Parallel Computing Method

KNN (K-nearest neighbor) is a simple and practical classification algorithm, but it is less efficient when dealing with massive high-dimensional data. Parallel computing is an effective way to accelerate big data calculations. In order to improve the efficiency of KNN, an optimization method based on GPU parallel computing is proposed in this paper. In the distance calculation stage, the parallelism is increased to the numerical value, and the independent components are calculated in parallel before the thread collaborative calculation. In the distance sorting stage, a method of judging order is proposed. This method based on shared memory gives Odd-even sorting the parallel ability to determine whether the sequence is ordered. The experimental results obtained in this research show that the proposed method can obviously improve the execution efficiency of KNN algorithm in high-dimensional samples and partial ordered distribution.