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As the data size in Machine Learning fields grows exponentially, it isinevitable to accelerate the computation by utilizing the ever-growing largenumber of available cores provided by high-performance computing hardware.However, existing parallel methods for clustering or regression often sufferfrom problems of low accuracy, slow convergence, and complexhyperparameter-tuning. Furthermore, the parallel efficiency is usuallydifficult to improve while striking a balance between preserving modelproperties and partitioning computing workloads on distributed systems. In thispaper, we propose a novel and simple data structure capturing the mostimportant information among data samples. It has several advantageousproperties supporting a hierarchical clustering strategy that is irrelevant tothe hardware parallelism, well-defined metrics for determining optimalclustering, balanced partition for maintaining the compactness property, andefficient parallelization for accelerating computation phases. Then we combinethe clustering with regression techniques as a parallel library and utilize ahybrid structure of data and model parallelism to make predictions. Experimentsillustrate that our library obtains remarkable performance on convergence,accuracy, and scalability.

An Accurate and Efficient Large-scale Regression Method through Best Friend Clustering