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 of the parallel training of the different inputs (typically images) and a the aggregation of network weights with collective communications (AllReduce). 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, in which the network weights are distributed and images are trained in a pipeline/stream manner over the computational nodes has been proposed (Pipedream, Gpipe). 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, that allows to significantly improve the performance of the parallel model approach compared to the literature
