MadPipe: Memory Aware Dynamic Programming Algorithm for Pipelined Model Parallelism

Abstract

The training phase in Deep Neural Networks (DNNs) is very computationally intensive and is nowadays often performed on parallel computing platforms, ranging from a few GPUs to several thousand GPUs. The strategy of choice for the parallelization of training is the so-called data parallel approach, based on the parallel training of the different inputs (typically images) and the aggregation of network weights with collective communications (AllReduce operation). The scalability of this approach is limited both by the memory available on each node and the networking capacities for collective operations. Recently, a parallel model approach has been proposed (PipeDream, Gpipe), in which the DNN weights are distributed and images are trained in a pipeline/stream manner over the computational nodes. In this paper, we formalize in detail the optimization problem associated with the placement of DNN layers onto computation resources when using pipelined model parallelism, and we derive a dynamic programming based heuristic, MadPipe. We show through extensive simulations based on realistic networks that MadPipe significantly improves the performance of the pipelined parallel model approach compared to PipeDream.