A Unified Architecture for Accelerating Distributed {DNN} Training in Heterogeneous GPU/CPU Clusters

Data center clusters that run DNN training jobs are inherently heterogeneous. They have GPUs and CPUs for computation and network bandwidth for distributed training. However, existing distributed DNN training architectures, all-reduce and Parameter Server (PS), cannot fully utilize such heterogeneous resources. In this paper, we present a new distributed DNN training architecture called BytePS. BytePS can leverage spare CPU and bandwidth resources in the cluster to accelerate distributed DNN training tasks running on GPUs. It provides a communication framework that is both proved optimal and unified – existing all-reduce and PS become two special cases of BytePS. To achieve the proved optimality in practice, BytePS further splits the functionalities of a parameter optimizer. It introduces a Summation Service abstraction for aggregating gradients, which is common for all the optimizers. Summation Service can be accelerated by AVX instructions and can be efficiently run on CPUs, while DNN model-related optimizer algorithms are run on GPUs for computation acceleration. BytePS can accelerate DNN training for major frameworks including TensorFlow, PyTorch and MXNet. For representative DNN training jobs with up to 256 GPUs, BytePS outperforms the state-of-the-art open source all-reduce and PS by up to 84% and 245%, respectively.