high performance computing on graphics processing units: hgpu.org

With the success of deep learning (DL) methods in diverse application domains, several deep learning software frameworks have been proposed to facilitate the usage of these methods. By knowing the frameworks which are employed in big data analysis, the analysis process will be more efficient in terms of time and accuracy. Thus, benchmarking DL software frameworks is in high demand. This paper presents a comparative study of deep learning frameworks, namely Caffe and TensorFlow on performance metrics: runtime performance and accuracy. This study is performed with several datasets, such as LeNet MNIST classification model, CIFAR-10 image recognition datasets and message passing interface (MPI) parallel matrix-vector multiplication. We evaluate the performance of the above frameworks when employed on machines of Intel Xeon Phi 7210. In this study, the use of vectorization, OpenMP parallel processing, and MPI are examined to improve the performance of deep learning frameworks. The experimental results show the accuracy comparison between the number of iterations of the test in the training model and the training time on the different machines before and after optimization. In addition, an experiment on two multi-nodes of Xeon Phi is performed. The experimental results also show the optimization of Xeon Phi is beneficial to the Caffe and TensorFlow frameworks.