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10/10/2022
The Foil: Capture-Avoiding Substitution With No Sharp Edges
Correctly manipulating program terms in a compiler is surprisingly diffi...
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04/22/2022
You Only Linearize Once: Tangents Transpose to Gradients
Automatic differentiation (AD) is conventionally understood as a family ...
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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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10/14/2021
Parallel Algebraic Effect Handlers
Algebraic effects and handlers support composable and structured control...
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05/20/2021
Decomposing reverse-mode automatic differentiation
We decompose reverse-mode automatic differentiation into (forward-mode) ...
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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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02/26/2021
Tensors Fitting Perfectly
Multidimensional arrays (NDArrays) are a central abstraction in modern s...
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06/28/2020
PyTorch Distributed: Experiences on Accelerating Data Parallel Training
This paper presents the design, implementation, and evaluation of the Py...
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03/31/2020
VC density of set systems defnable in tree-like graphs
We study set systems definable in graphs using variants of logic with di...
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12/03/2019
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Deep learning frameworks have often focused on either usability or speed...
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06/07/2016
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
The ability to perform pixel-wise semantic segmentation in real-time is ...
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05/24/2016
