基于CUDA的GMM模型快速训练方法及应用（.doc

基于CUDA的GMM模型快速训练方法及应用( 吴奎，宋彦，戴礼荣 （中国科学技术大学电子工程与信息科学系，安徽合肥，230027） 摘 要 由于能够很好地近似描述任何分布，GMM在模式在识别领域得到了广泛的应用。GMM模型参数通常使用迭代的EM算法训练获得，当训练数据量非常庞大及模型混合数很大时，需要花费很长的训练时间。NVIDIA公司推出的CUDA技术通过在GPU并发执行多个线程能够实现大规模并行快速计算。由此，本文提出一种基于CUDA，适用于特大数据量的GMM模型快速训练方法，包括用于模型初始化的K-means算法的快速实现方法，以及用于模型参数估计的EM算法的快速实现方法。文中还将这种训练方法应用到语种GMM模型训练中。实验结果表明，与Intel DualCore Pentium Ⅳ 3.0GHz CPU的一个单核相比，在NVIDIA GTS250 GPU上语种GMM模型训练速度提高了26倍左右。 关键词：GMM模型；语种识别；图形处理单元；统一计算设备架构 CUDA based Fast GMM Model Training Method and its Application Wu Kui，Song Yan，Dai LiRong (Department of Electronic Engineering and Information Science，University of Science and Technology of China，Hefei，230027，China) Abstract: Due to its good property to provide an approximation to any distribution, GMM has been widely applied in the field of pattern recognition. Usually, the iterative EM algorithm is applied to estimate GMM parameters .The computational complexity at model training procedure will become very high when large amounts of training data and large mixture number are engaged. The CUDA technology provided by NVIDIA Corporation can perform fast parallel computation by running thousands of threads simultaneously on GPU. In this paper, a fast GMM model training implementation using CUDA is presented, which is especially applicable to large amounts of training data. The fast training implementation contains two parts, the K-means algorithm for model initialization and the EM algorithm for parameter estimation. Furthermore, this fast training method has been applied in language GMMs training. The experimental results show that language model training using GPU is about 26 times faster on NVIDIA GTS250 when compared to traditional implementation on one of the single core of Intel DualCore Pentium Ⅳ 3.0GHz CPU. Keywords: GMM model; Language identification; GPU; CUDA 1 引言 由于能够很好地近似描述任何分布，高斯混合模型（Gaussian Mixture Model ，GMM）在模式识别领域得到了广泛的应用。GMM模型参数通常使用迭代的EM(Expectation-Maximization)算法[1]训练获得。EM算法是一个迭代算法,需要对模型初始化，一般采用K-means算法实现EM