4 papers
Near-Optimal Sparse Allreduce for Distributed Deep Learning
Shigang Li, Torsten Hoefler
Communication overhead is one of the major obstacles to train large deep learning models at scale. Gradient sparsification is a promising technique to reduce the communication volu…
Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines
Shigang Li, Torsten Hoefler
Training large deep learning models at scale is very challenging. This paper proposes Chimera, a novel pipeline parallelism scheme which combines bidirectional pipelines for effici…
Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging
Shigang Li, Tal Ben-Nun, Giorgi Nadiradze +4
Deep learning at scale is dominated by communication time. Distributing samples across nodes usually yields the best performance, but poses scaling challenges due to global informa…
Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations
Shigang Li, Tal Ben-Nun, Salvatore Di Girolamo +2
Load imbalance pervasively exists in distributed deep learning training systems, either caused by the inherent imbalance in learned tasks or by the system itself. Traditional synch…