Towards highly scalable X10 based spectral clustering

Large graph analysis has become a widely studied area in recent years. Clustering is one of the most important types of analysis that has versatile applications such as community detection in social networks, image segmentation, graph partitioning, etc. However, existing clustering algorithms do not intend for large scale graphs. To solve this problem, we implemented spectral clustering in X10, that is a programming language aimed for developing highly scalable applications on Post- Petascale supercomputers. Our spectral clustering is based on the algorithm proposed by Shi and Malik. After evaluating scalability and precision, we found that our implementations are scalable in terms of execution time and precise for analyzing real data. © 2012 IEEE.