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Tensorflow XLA详解.pdf
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更新于2023-03-03
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XLA(加速线性代数)是用于优化TensorFlow计算的线性代数的域特定编译器。 XLA 利用 JIT 编译技术分析用户在运行时创建的 TensorFlow 图表,根据实际运行时维度和类型将其专门化,将多个运算融合在一起并为它们生成高效的本机代码——适用于 CPU、GPU 之类的设备和自定义加速器(例如,Google 的 TPU)。
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TensorFlow w/XLA:
TensorFlow, Compiled!
Expressiveness with performance
Jeff Dean
Google Brain team
g.co/brain
presenting work done by the XLA team and Google Brain team
Pre-release Documentation (or search GitHub repository for ‘XLA’):
https://www.tensorflow.org/versions/master/resources/xla_prerelease.html

It takes a village to raise a
compiler.
- Ancient proverb

Why Did We Build TensorFlow?
Wanted system that was flexible, scalable, and production-ready
DistBelief, our first system, was good on two of these, but lacked flexibility
Most existing open-source packages were also good on 2 of 3 but not all 3

TensorFlow Goals
Establish common platform for expressing machine learning ideas and systems
Make this platform the best in the world for both research and production use
Open source it so that it becomes a platform for everyone, not just Google

Facts and Figures
Launched on Nov. 9, 2015
Reasonably fully-featured:
auto differentiation, queues, control flow, fairly comprehensive set of ops, ...
Tutorials made system accessible
Out-of-the-box support for CPUs, GPUs, multiple devices, multiple platforms
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