将随机卷积核时间序列分类算法优化至GPU，效率提升11倍。
CUROCKET: Optimizing ROCKET for GPU
- 设计新算法适配GPU并行计算，解决随机核导致的效率瓶颈。
- 在相同功耗下，计算效率比CPU版ROCKET高出11倍。
- 适合需要快速处理大量时间序列数据的研究与工业场景。
ROCKET（RandOm Convolutional KErnel Transform）是一种2019年提出的时序分类特征提取算法，通过在时间序列上应用随机生成的卷积核，生成可用于训练线性分类器或回归器（如Ridge）的特征。发布时，ROCKET在准确率上达到当时最先进的水平，同时计算成本显著更低，成为时序分类领域的有力工具。此后出现了多个改进版本，进一步提升了准确率和效率。然而，现有ROCKET实现大多仅限于CPU执行。由于卷积任务具有高度并行性，更适合在GPU上运行，从而大幅提升计算速度。主要挑战在于ROCKET使用的非均匀卷积核使标准GPU卷积方法效率低下。本文提出一种新算法，可高效在GPU上执行ROCKET，实现每瓦功耗下计算效率比CPU版高出11倍。代码已开源：https://github.com/oleeven/CUROCKET。
原文摘要 · Abstract (English)
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.
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