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12/20/2022
Efficient and Sound Differentiable Programming in a Functional Array-Processing Language
Automatic differentiation (AD) is a technique for computing the derivati...
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10/07/2022
Automatic Discovery of Composite SPMD Partitioning Strategies in PartIR
Large neural network models are commonly trained through a combination o...
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12/06/2021
Automap: Towards Ergonomic Automated Parallelism for ML Models
The rapid rise in demand for training large neural network architectures...
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12/02/2021
Memory-efficient array redistribution through portable collective communication
Modern large-scale deep learning workloads highlight the need for parall...
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10/20/2021
Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learning
We present a novel characterization of the mapping of multiple paralleli...
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04/12/2021
Getting to the Point. Index Sets and Parallelism-Preserving Autodiff for Pointful Array Programming
We present a novel programming language design that attempts to combine ...
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06/06/2018
Efficient Differentiable Programming in a Functional Array-Processing Language
We present a system for the automatic differentiation of a higher-order ...
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05/27/2017
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
New types of machine learning hardware in development and entering the m...
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05/24/2016