Research Paper
HyPar-Flow: Exploiting MPI and Keras for Scalable Hybrid-Parallel DNN Training with TensorFlow
Event Type
Research Paper
AI/Machine Learning/Deep Learning
Communication Optimization
Parallel Applications
System Software & Runtime Systems
TimeMonday, June 22nd4:00pm - 4:30pm
LocationPanorama 1
DescriptionTo reduce the training time of large-scale Deep Neural Networks (DNNs), scientists have started to explore parallelization strategies like data-parallelism, model-parallelism, and hybrid-parallelism. While data-parallelism has been extensively studied and developed, several problems exist in realizing model-parallelism and hybrid-parallelism efficiently. Four major problems we focus on are: 1) defining a notion of a distributed model across processes, 2) implementing forward/back-propagation across process boundaries that requires explicit communication, 3) obtaining parallel speedup on an inherently sequential task, and 4) achieving scalability without losing out on a model's accuracy. To address these problems, we create HyPar-Flow--- a model-size/-type agnostic, scalable, practical, and user-transparent system for hybrid-parallel training by exploiting MPI, Keras, and TensorFlow. HyPar-Flow provides a single API that can be used to perform data, model, and hybrid parallel training of any Keras model at scale. We create an internal distributed representation of the user-provided Keras model, utilize TF's Eager execution features for distributed forward/back-propagation across processes, exploit pipelining to improve performance and leverage efficient MPI primitives for scalable communication. Between model partitions, we use send and recv to exchange layer-data/partial-errors while allreduce is used to accumulate/average gradients across model replicas. Beyond the design and implementation of HyPar-Flow, we also provide comprehensive correctness and performance results on three state-of-the-art HPC systems including TACC Frontera (#5 on For ResNet-1001, an ultra-deep model, HyPar-Flow provides: 1) Up-to 1.6x speedup over Horovod-based data-parallel training, 2) 110x speedup over single-node on 128 Stampede2 nodes, and 3) 481x speedup over single-node on 512 Frontera nodes.