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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 parallelism.
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Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 1909
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Pipemare: Asynchronous pipeline parallel DNN training
Bowen Yang, Jian Zhang, Jonathan Li, Christopher Ré, Christopher R. Aberger, and Christopher De Sa · 1910
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
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URL https://www.tensorflow.org/xla
XLA: Optimizing compiler for machine learning, 2017 · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
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Declarative abstractions for tensor program partitioning
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