Understanding Parallel K-Means Clustering
Dive into the world of parallel K-Means clustering, a method that partitions data points into clusters based on their similarities. Learn about strategies for parallelizing the computation, tackling issues like false sharing, and optimizing the clustering process.
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Presentation Transcript
Problem description Clustering is the task of assigning a set of objects into groups (clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters. k-means clustering is a method of clustering which aims to partition n data points into k clusters (n >> k) in which each observation belongs to the cluster with the nearest mean. The nearness is evaluated by a distance function
Example Iterative: Stopping criterion: clusters stable, max iter reached
K-means parallelization: Na ve for large dimensions (d), we can try simple parallelization of the distance function computation = split DIMENSIONS between threads fine just for shared memory systems a risk of false sharing
BTW: false sharing? false sharing is a performance issue on SMP systems, where each processor has a local cache. It occurs when threads on different processors modify variables that reside on the same cache line each thread is not actually sharing access to the same variable but it s cached copy cache line is invalidated, forcing a memory update to maintain cache coherency
K-means parallelization: Better split DATA (points) into threads assign points to clusters in threads update centroids after points clustered
