基于GPU的Winograd 卷积算法并行化

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中图分类号：TP183 文献标志码：A 文章编号：1001-3695（2025）08-026-2446-06

doi：10.19734/j. issn.1001-3695.2024.11.0502

GPU-based parallelization of Winograd convolution algorithm

Wang Xin†，Zhen Xueru （KeyLboratodedrotroghstr（tcto）gUsitui）

Abstract：This paper proposedaninovativeWinogradparalelconvolutionalgorithmbasedonGPU toaddress theproblemof excessivecomputationalloadinmodernconvolutionalneuralnetworks.Thealgorithmusedload-balanced task mapping，optimized thedataloadingstrategyto hidelatency，andcombined thedynamic padding methodtofullexplore thesynergybetwen theWinogradconvolution algorithmandtheGPUarchitecture.Experimentalresultsshowthatonmultipleconvolutionallayers of the classic convolutional l network model ResNet，the proposed algorithm outperforms the standard Winograd convolutionalgorithmintheNVIDIAcuDNN8.3.Olibrary.Itachievesaspeed-upratioofupto2.46ontheTuringarchitecture RTX 2080Ti GPUand maintainshigh computational accuracy.Compared with the standard Winograd convolutionalgorithm based on GPU，the algorithm significantly improves the efficiency of convolutional computation.

Key Words：Winograd algorithm；parallel computing；CUDA；convolutional neural network

0 引言

卷积神经网络（convolutionalneuralnetwork，CNN）作为深度学习（deeplearning，DL）中的核心技术，已经在图像分类[1]和目标分割[2]等多个领域得到了广泛应用。（剩余14861字）