Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models

Abstract

Large deep learning models have shown great potential with state-of-the-art results in many tasks. However, running these large models is quite challenging on an accelerator (GPU or TPU) because the on-device memory is too limited for the size of these models. Intra-layer model parallelism is an approach to address the issues by partitioning individual layers or operators across multiple devices in a distributed accelerator cluster. But, the data communications generated by intra-layer model parallelism can contribute to a significant proportion of the overall execution time and severely hurt the computational efficiency. As intra-layer model parallelism is critical to enable large deep learning models, this paper proposes a novel technique to effectively reduce its data communication overheads by overlapping communication with computation. With the proposed technique, an identified original communication collective is decomposed along with the dependent computation operation into a sequence of finer-grained operations. By creating more overlapping opportunities and executing the newly created, finer-grained communication and computation operations in parallel, it effectively hides the data transfer latency and achieves a better system utilization. Evaluated on TPU v4 Pods using different types of large models that have 10 billion to 1 trillion parameters, the proposed technique improves system throughput by 1.14 - 1.38x. The achieved highest peak FLOPS utilization is 72% on 1024 TPU chips with a large language model that has 500 billion parameters.