Towards compositional and generative tensor optimizations-Reference-Cited by-同舟云学术

Towards compositional and generative tensor optimizations

Published:2017-10-23 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 16th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences
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Author:

Susungi Adilla¹,Rink Norman A.²,Castrillón Jerónimo²,Huismann Immo²,Cohen Albert³,Tadonki Claude¹,Stiller Jörg²,Fröhlich Jochen²

Affiliation:

1. MINES ParisTech, France

2. TU Dresden, Germany

3. Inria, France / ENS, France

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3136040.3136050

Reference23 articles.

1. 2017. NumPy package for scientific computing with Python. http: //www.numpy.org/ . (2017). 2017. NumPy package for scientific computing with Python. http: //www.numpy.org/ . (2017).

2. 2017. XLA: Accelerated Linear Algebra. https://www.tensorflow.org/ performance/xla/ . (2017). 2017. XLA: Accelerated Linear Algebra. https://www.tensorflow.org/ performance/xla/ . (2017).

3. 2017. Xtensor Multi-dimensional arrays with broadcasting and lazy computing. https://github.com/QuantStack/xtensor . (2017). 2017. Xtensor Multi-dimensional arrays with broadcasting and lazy computing. https://github.com/QuantStack/xtensor . (2017).

4. Martín Abadi and Ashish Agarwal et al. 2015. TensorFlow: LargeScale Machine Learning on Heterogeneous Distributed Systems. http://download.tensorflow.org/paper/whitepaper2015.pdf. (2015). Martín Abadi and Ashish Agarwal et al. 2015. TensorFlow: LargeScale Machine Learning on Heterogeneous Distributed Systems. http://download.tensorflow.org/paper/whitepaper2015.pdf. (2015).

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