A vectorized K-means algorithm for Intel many integrated core architecture

Abstract

The K-Means algorithms is one of the most popular and effective clustering algorithms for many practical applications. However, direct K-Means methods, taking objects as processing unit, is computationally expensive especially in Objects-Assignment phase on Single-Instruction Single-Data (SISD) processors, typically as CPUs. In this paper, we propose a vectorized K-Means algorithm for Intel Many Integrated Core (MIC) coprocessor, a newly released product from Intel for highly parallel workloads. This new algorithm is able to achieve fine-grained Single-Instruction Multiple-Data (SIMD) parallelism by taking each dimension of all objects as a long vector. This vectorized algorithm is suitable for any-dimensional objects, which is little taken into consideration in preceding works. We also parallelize the vectorized K-Means algorithm on MIC coprocessor to achieve coarse-grained thread-level parallelism. Finally, we implement and evaluate the vectorized method on the first generation of Intel MIC product. Measurements show that this algorithm based on MIC coprocessor gets desired speedup to sequential algorithm on CPU and demonstrate that MIC coprocessor owns highly parallel computational power as well as scalability. © 2013 Springer-Verlag.