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arXiv · 1805.00569

Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems

Yang You·James Demmel·Cho-Jui Hsieh·Richard Vuduc
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Abstract

We propose two new methods to address the weak scaling problems of KRR: the Balanced KRR (BKRR) and K-means KRR (KKRR). These methods consider alternative ways to partition the input dataset into p different parts, generating p different models, and then selecting the best model among them. Compared to a conventional implementation, KKRR2 (optimized version of KKRR) improves the weak scaling efficiency from 0.32% to 38% and achieves a 591times speedup for getting the same accuracy by using the same data and the same hardware (1536 processors). BKRR2 (optimized version of BKRR) achieves a higher accuracy than the current fastest method using less training time for a variety of datasets. For the applications requiring only approximate solutions, BKRR2 improves the weak scaling efficiency to 92% and achieves 3505 times speedup (theoretical speedup: 4096 times).

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Yang You, James Demmel, Cho-Jui Hsieh, Richard Vuduc. 2018-05-01. Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems. https://doi.org/10.1145/3205289.3205290

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