29 citations · 60 across the 11 of their papers we have counts for
5 papers · 1 filter
Automap: Towards Ergonomic Automated Parallelism for ML Models
Michael Schaarschmidt, Dominik Grewe, Dimitrios Vytiniotis +8
The rapid rise in demand for training large neural network architectures has brought into focus the need for partitioning strategies, for example by using data, model, or pipeline…
Memory-efficient array redistribution through portable collective communication
Norman A. Rink, Adam Paszke, Dimitrios Vytiniotis +1
Modern large-scale deep learning workloads highlight the need for parallel execution across many devices in order to fit model data into hardware accelerator memories. In these set…
Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learning
Ningning Xie, Tamara Norman, Dominik Grewe +1
We present a novel characterization of the mapping of multiple parallelism forms (e.g. data and model parallelism) onto hierarchical accelerator systems that is hierarchy-aware and…
Gradient Forward-Propagation for Large-Scale Temporal Video Modelling
Mateusz Malinowski, Dimitrios Vytiniotis, Grzegorz Swirszcz +2
How can neural networks be trained on large-volume temporal data efficiently? To compute the gradients required to update parameters, backpropagation blocks computations until the…
Getting to the Point. Index Sets and Parallelism-Preserving Autodiff for Pointful Array Programming
Adam Paszke, Daniel Johnson, David Duvenaud +5
We present a novel programming language design that attempts to combine the clarity and safety of high-level functional languages with the efficiency and parallelism of low-level n…