Parallel and accurate k-means algorithm on CPU-GPU architectures for spectral clustering

k-Means is a standard algorithm for clustering data. It constitutes generally the final step in a more complex chain of high-quality spectral clustering. However, this chain suffers from lack of scalability when addressing large datasets. This can be overcome by applying also the k-means algorithm as a preprocessing task to reduce the input data instances. We propose parallel optimization techniques for the k-means algorithm on CPU and GPU. Particularly we use a two-step summation method with package processing to handle the effect of rounding errors that may occur during the phase of updating cluster centroids. Our experiments on synthetic and real-world datasets containing millions of instances exhibit a speedup up to 7 for the k-means iteration time on GPU versus 20/40 CPU threads using AVX units, and achieve double-precision accuracy with single-precision computations.