Heterogeneous Parallel and Distributed Optimization of K-Means Algorithm on Sunway Supercomputer

Clustering plays an essential role in large-volume data analysis areas such as bioinformatics, statistic, pattern recognition and so on. K-means is one of most effective clustering algorithms, which is relatively easy to implement. Most real world applications usually involve a huge amount of data. Thus, how to improve applications' efficiency while maintaining accuracy becomes a significant and considerable issue. In this paper, a K-means clustering algorithm, which uses heterogeneous parallel computing technology on Computing processing elements and distributed computing technology, is proposed. This algorithm is applied in unique Sunway architecture based on "Sunway TaihuLight" Supercomputer---the world's fastest supercomputer with peak performance over 100PFLOPS. The testing results suggest that this improved algorithm is stable, fast and efficient. Conclusively, it has a great improvement in computation performance, especially with large volumes of data.