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

05/01/2018

∙

by   Yang You, et al.

∙

University of California-Davis

∙

Georgia Institute of Technology

∙

berkeley college

∙

0

∙

share

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 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 speedup: 4096 times).