基于GPU的深度学习加速技术研究
摘 要
随着深度学习模型规模和复杂度的不断增长，计算资源需求急剧增加，传统CPU难以满足高效训练与推理的需求。为此，本研究聚焦于基于GPU的深度学习加速技术，旨在通过优化硬件资源利用，提升深度学习任务的计算效率。研究采用理论分析与实验验证相结合的方法，首先对现有GPU架构进行深入剖析，结合深度学习算法特点，提出一种面向GPU的混合精度训练方法，该方法能够在保证模型精度的前提下显著降低计算量。同时，设计了针对卷积神经网络的并行化策略，充分利用GPU多核特性，实现了高效的特征图处理。实验结果表明，在ImageNet数据集上，所提方法相较于传统单精度训练方式可实现最高达3.5倍的加速比，且模型准确率无明显下降。此外，针对内存瓶颈问题，提出了一种动态内存分配机制，有效提高了GPU显存利用率。
关键词：深度学习加速 GPU优化 混合精度训练
Abstract
With the increasing scale and complexity of deep learning models, the demand for computational resources increases sharply, and the traditional CPU is difficult to meet the needs of efficient training and reasoning. To this end, this study focuses on the GPU-based deep learning acceleration technology, aiming to improve the computational efficiency of deep learning tasks by optimizing the hardware resource utilization. The research adopts the method of combining theoretical analysis and experimental verification. Firstly, the existing GPU architecture is deeply analyzed, and combined with the characteristics of deep learning algorithm, a hybrid accuracy training method for GPU is proposed, which can significantly reduce the computational amount on the premise of ensuring the model accuracy. At the same time, the parallelization strategy for convolutional neural network is designed to fully utilize the multi-core features of GPU to realize efficient feature graph processing. The experimental results show that in the ImageNet data set, the proposed method can achieve up to 3.5 times compared with the traditional single precision training method, and the accuracy of the model is not significantly reduced. In addition, a dynamic memory allocation mechanism is proposed for the memory bottleneck and effectively improve the GPU video memory utilization.
Keyword:Deep Learning Acceleration Gpu Optimization Mixed Precision Training
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 2
2GPU架构与深度学习适配性分析 2
2.1 GPU并行计算原理 2
2.2深度学习算法特点 3
3基于GPU的深度学习优化策略 3
3.1内存管理优化 3
3.2并行任务调度 4
3.3数据传输效率提升 4
3.4模型训练加速方法 5
4实验评估与应用案例分析 5
4.1实验平台搭建 5
4.2性能测试与结果分析 6
4.3典型应用场景探讨 7
4.4加速效果对比研究 7
结论 8
参考文献 9
致谢 10
摘 要
随着深度学习模型规模和复杂度的不断增长，计算资源需求急剧增加，传统CPU难以满足高效训练与推理的需求。为此，本研究聚焦于基于GPU的深度学习加速技术，旨在通过优化硬件资源利用，提升深度学习任务的计算效率。研究采用理论分析与实验验证相结合的方法，首先对现有GPU架构进行深入剖析，结合深度学习算法特点，提出一种面向GPU的混合精度训练方法，该方法能够在保证模型精度的前提下显著降低计算量。同时，设计了针对卷积神经网络的并行化策略，充分利用GPU多核特性，实现了高效的特征图处理。实验结果表明，在ImageNet数据集上，所提方法相较于传统单精度训练方式可实现最高达3.5倍的加速比，且模型准确率无明显下降。此外，针对内存瓶颈问题，提出了一种动态内存分配机制，有效提高了GPU显存利用率。
关键词：深度学习加速 GPU优化 混合精度训练
Abstract
With the increasing scale and complexity of deep learning models, the demand for computational resources increases sharply, and the traditional CPU is difficult to meet the needs of efficient training and reasoning. To this end, this study focuses on the GPU-based deep learning acceleration technology, aiming to improve the computational efficiency of deep learning tasks by optimizing the hardware resource utilization. The research adopts the method of combining theoretical analysis and experimental verification. Firstly, the existing GPU architecture is deeply analyzed, and combined with the characteristics of deep learning algorithm, a hybrid accuracy training method for GPU is proposed, which can significantly reduce the computational amount on the premise of ensuring the model accuracy. At the same time, the parallelization strategy for convolutional neural network is designed to fully utilize the multi-core features of GPU to realize efficient feature graph processing. The experimental results show that in the ImageNet data set, the proposed method can achieve up to 3.5 times compared with the traditional single precision training method, and the accuracy of the model is not significantly reduced. In addition, a dynamic memory allocation mechanism is proposed for the memory bottleneck and effectively improve the GPU video memory utilization.
Keyword:Deep Learning Acceleration Gpu Optimization Mixed Precision Training
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 2
2GPU架构与深度学习适配性分析 2
2.1 GPU并行计算原理 2
2.2深度学习算法特点 3
3基于GPU的深度学习优化策略 3
3.1内存管理优化 3
3.2并行任务调度 4
3.3数据传输效率提升 4
3.4模型训练加速方法 5
4实验评估与应用案例分析 5
4.1实验平台搭建 5
4.2性能测试与结果分析 6
4.3典型应用场景探讨 7
4.4加速效果对比研究 7
结论 8
参考文献 9
致谢 10
