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

arXiv: https://arxiv.org/abs/1805.00569 · HTML: https://mlsanity.com/paper/1805.00569/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2018.

## Built on by (most cited first, 3 of 3)

- [Scalable Dual Coordinate Descent for Kernel Methods](https://mlsanity.com/paper/2406.18001/) (2024)
- [Training very large scale nonlinear SVMs using Alternating Direction Method of Multipliers coupled with the Hierarchically Semi-Separable kernel approximations](https://mlsanity.com/paper/2108.04167/) (2021)
- [An Accurate and Efficient Large-scale Regression Method through Best Friend Clustering](https://mlsanity.com/paper/2104.10819/) (2021)

## Abstract (arXiv)

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).