In-place transposition of rectangular matrices on accelerators
Proceedings of the 19th ACM SIGPLAN symposium on Principles and practice of parallel programming
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We present an implementation of parallel $(K)$-means clustering, called $(K_{ps})$-means, that achieves high performance with near-full occupancy compute kernels without imposing limits on the number of dimensions and data points permitted as input, thus combining flexibility with high degrees of parallelism and efficiency. As a key element to performance improvement, we introduce parallel sorting as data preprocessing and updating steps. Our final implementation for Nvidia GPUs achieves speedups of up to 200-fold over CPU reference code and of up to three orders of magnitude when compared with popular numerical software packages.