Andy Davis
Authored Publications
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MLIR: Scaling Compiler Infrastructure for Domain Specific Computation
Chris Lattner
Mehdi Amini
Uday Bondhugula
River Riddle
Tatiana Shpeisman
Nicolas Vasilache
Oleksandr Zinenko
CGO 2021
Preview abstract
This work presents the MLIR compiler infrastructure, which is a novel approach to building reusable compiler infrastructure. MLIR aims to address software fragmentation, improve compilation for heterogeneous hardware, significantly reduces the cost of building domain specific compilers, and aid
in connecting existing compilers together. MLIR facilitates the design and implementation of code
generators, translators and optimizer at different levels of abstraction and also across application domains, hardware targets and execution environments. The scientific perspective on these challenges is twofold: 1) evaluating MLIR as an infrastructure that enables new research and educational approaches on programming languages, compilers, code generators, execution environments, hardware acceleration and codesign; and 2) discussing MLIR as a research artifact built for extension and evolution, raising its own design, semantics, algorithmic, system, engineering, and multi-disciplinary challenges. The paper presents the rationale for MLIR, its
original design principles, structures and semantics, and validates these by surveying some applications of it.
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Dynamic Control Flow in Large-Scale Machine Learning
Yuan Yu
Eugene Brevdo
Mike Burrows
Tim Harley
Peter Hawkins
Manjunath Kudlur
Rajat Monga
Xiaoqiang Zheng
Proceedings of EuroSys 2018
Preview abstract
Many recent machine learning models rely on fine-grained dynamic control flow for training and inference. In particular, models based on recurrent neural networks and on reinforcement learning depend on recurrence relations, data-dependent conditional execution, and other features that call for dynamic control flow. These applications benefit from the ability to make rapid control-flow decisions across a set of computing devices in a distributed system. For performance, scalability, and expressiveness, a machine learning system must support dynamic control flow in distributed and heterogeneous environments.
This paper presents a programming model for distributed machine learning that supports dynamic control flow. We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system. Our approach extends the use of dataflow graphs to represent machine learning models, offering several distinctive features. First, the branches of conditionals and bodies of loops can be partitioned across many machines to run on a set of heterogeneous devices, including CPUs, GPUs, and custom ASICs. Second, programs written in our model support automatic differentiation and distributed gradient computations, which are necessary for training machine learning models that use control flow. Third, our choice of non-strict semantics enables multiple loop iterations to execute in parallel across machines, and to overlap compute and I/O operations.
We have done our work in the context of TensorFlow, and it has been used extensively in research and production. We evaluate it using several real-world applications, and demonstrate its performance and scalability.
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Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Noam Shazeer
Azalia Mirhoseini
Krzysztof Maziarz
Geoffrey Hinton
ICLR (2017)
Preview abstract
The capacity of a neural network to absorb information is limited by its number of parameters. Conditional computation, where parts of the network are active on a per-example basis, has been proposed in theory as a way of dramatically increasing model capacity without a proportional increase in computation. In practice, however, there are significant algorithmic and performance challenges. In this work, we address these challenges and finally realize the promise of conditional computation, achieving greater than 1000x improvements in model capacity with only minor losses in computational efficiency on modern GPU clusters. We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward sub-networks. A trainable gating network determines a sparse combination of these experts to use for each example. We apply the MoE to the tasks of language modeling and machine translation, where model capacity is critical for absorbing the vast quantities of knowledge available in the training corpora. We present model architectures in which a MoE with up to 137 billion parameters is applied convolutionally between stacked LSTM layers. On large language modeling and machine translation benchmarks, these models achieve significantly better results than state-of-the-art at lower computational cost.
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TensorFlow: A system for large-scale machine learning
Jianmin Chen
Matthieu Devin
Geoffrey Irving
Manjunath Kudlur
Rajat Monga
Benoit Steiner
Paul Tucker
Vijay Vasudevan
Pete Warden
Yuan Yu
Xiaoqiang Zheng
12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), USENIX Association (2016), pp. 265-283
Preview abstract
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that Tensor- Flow achieves for several real-world applications.
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Ashish Agarwal
Eugene Brevdo
Craig Citro
Matthieu Devin
Ian Goodfellow
Andrew Harp
Geoffrey Irving
Yangqing Jia
Rafal Jozefowicz
Lukasz Kaiser
Manjunath Kudlur
Dan Mané
Rajat Monga
Chris Olah
Mike Schuster
Jonathon Shlens
Benoit Steiner
Ilya Sutskever
Kunal Talwar
Paul Tucker
Vijay Vasudevan
Pete Warden
Yuan Yu
Xiaoqiang Zheng
tensorflow.org (2015)
Preview abstract
TensorFlow is an interface for expressing machine learning
algorithms, and an implementation for executing such algorithms.
A computation expressed using TensorFlow can be
executed with little or no change on a wide variety of heterogeneous
systems, ranging from mobile devices such as phones
and tablets up to large-scale distributed systems of hundreds
of machines and thousands of computational devices such as
GPU cards. The system is flexible and can be used to express
a wide variety of algorithms, including training and inference
algorithms for deep neural network models, and it has been
used for conducting research and for deploying machine learning
systems into production across more than a dozen areas of
computer science and other fields, including speech recognition,
computer vision, robotics, information retrieval, natural
language processing, geographic information extraction, and
computational drug discovery. This paper describes the TensorFlow
interface and an implementation of that interface that
we have built at Google. The TensorFlow API and a reference
implementation were released as an open-source package under
the Apache 2.0 license in November, 2015 and are available at
www.tensorflow.org.
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