Distributed Learning of CNNs on Heterogeneous CPU/GPU Architectures

• Autor(a) principal: Marques, José
• Data de Publicação: 2018
• Outros Autores: Falcao, Gabriel, Alexandre, Luís
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.6/8141

Convolutional Neural Networks (CNNs) have shown to be powerful classi cation tools in tasks that range from check reading to medical diagnosis, reaching close to human perception, and in some cases surpassing it. However, the problems to solve are becoming larger and more complex, which translates to larger CNNs, leading to longer training times|the computational complex part|that not even the adoption of Graphics Processing Units (GPUs) could keep up to. This problem is partially solved by using more processing units and distributed training methods that are o ered by several frameworks dedicated to neural network training, such as Ca e, Torch or TensorFlow. However, these techniques do not take full advantage of the possible parallelization o ered by CNNs and the cooperative use of heterogeneous devices with di erent processing capabilities, clock speeds, memory size, among others. This paper presents a new method for the parallel training of CNNs that can be considered as a particular instantiation of model parallelism, where only the convolutional layer is distributed. In fact, the convolutions processed during training (forward and backward propagation included) represent from 60-90% of global processing time. The paper analyzes the in uence of network size, bandwidth, batch size, number of devices, including their processing capabilities, and other parameters. Results show that this technique is capable of diminishing the training time without a ecting the classi cation performance for both CPUs and GPUs. For the CIFAR-10 dataset, using a CNN with two convolutional layers, and 500 and 1500 kernels, respectively, best speedups achieve 3:28 using four CPUs and 2:45 with three GPUs. Modern imaging datasets, larger and more complex than CIFAR-10 will certainly require more than 60-90% of processing time calculating convolutions, and speedups will tend to increase accordingly.