Evaluation of K-Means Data Clustering Algorithm on Intel Xeon Phi

Sunwoo Lee, Wei-keng Liao, Ankit Agrawal, Nikos Hardavellas, Alok Choudhary

BigData, 2019

This paper studies how to optimize K-means for Intel Xeon Phi’s MIC architecture using compiler-intrinsic-based memory layouts, data padding for VPU-width alignment, and parallel reduction for better cache and thread/data-level parallelism, achieving up to 68.65%/56.14% speedups over auto-vectorization on aligned/unaligned datasets and up to 53.49% on large-scale parallel runs with high-dimensional data.

BibTeX

@inproceedings{lee2016evaluation,
  title={Evaluation of K-means data clustering algorithm on Intel Xeon Phi},
  author={Lee, Sunwoo and Liao, Wei-keng and Agrawal, Ankit and Hardavellas, Nikos and Choudhary, Alok},
  booktitle={2016 IEEE International Conference on Big Data (Big Data)},
  pages={2251--2260},
  year={2016},
  organization={IEEE}
}