CUROCKET: Optimizing ROCKET for GPU

TL;DR

This paper introduces CUROCKET, a GPU-optimized version of the ROCKET algorithm for time series classification, significantly improving computational efficiency and enabling faster processing on GPU hardware.

Contribution

The paper presents a novel GPU algorithm for ROCKET that overcomes kernel inhomogeneity issues, achieving up to 11x efficiency gains over CPU implementations.

Findings

Up to 11 times higher efficiency per watt on GPU

Effective handling of inhomogeneous kernels in GPU convolution

Open-source implementation available on GitHub

Abstract

ROCKET (RandOm Convolutional KErnel Transform) is a feature extraction algorithm created for Time Series Classification (TSC), published in 2019. It applies convolution with randomly generated kernels on a time series, producing features that can be used to train a linear classifier or regressor like Ridge. At the time of publication, ROCKET was on par with the best state-of-the-art algorithms for TSC in terms of accuracy while being significantly less computationally expensive, making ROCKET a compelling algorithm for TSC. This also led to several subsequent versions, further improving accuracy and computational efficiency. The currently available ROCKET implementations are mostly bound to execution on CPU. However, convolution is a task that can be highly parallelized and is therefore suited to be executed on GPU, which speeds up the computation significantly. A key difficulty arises from the inhomogeneous kernels ROCKET uses, making standard methods for applying convolution on GPU inefficient. In this work, we propose an algorithm that is able to efficiently perform ROCKET on GPU and achieves up to 11 times higher computational efficiency per watt than ROCKET on CPU. The code for CUROCKET is available in this repository https://github.com/oleeven/CUROCKET on github.