Article

TVM: an automated end-to-end optimizing compiler for deep learning

Authors:

Thierry Moreau,

Carlos Guestrin,

Arvind KrishnamurthyAuthors Info & Claims

OSDI'18: Proceedings of the 13th USENIX conference on Operating Systems Design and Implementation

Pages 579 - 594

Published: 08 October 2018 Publication History

Abstract

There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narrow range of server-class GPUs. Deploying workloads to new platforms - such as mobile phones, embedded devices, and accelerators (e.g., FPGAs, ASICs) - requires significant manual effort. We propose TVM, a compiler that exposes graph-level and operator-level optimizations to provide performance portability to deep learning workloads across diverse hardware back-ends. TVM solves optimization challenges specific to deep learning, such as high-level operator fusion, mapping to arbitrary hardware primitives, and memory latency hiding. It also automates optimization of low-level programs to hardware characteristics by employing a novel, learning-based cost modeling method for rapid exploration of code optimizations. Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs. We also demonstrate TVM's ability to target new accelerator back-ends, such as the FPGA-based generic deep learning accelerator. The system is open sourced and in production use inside several major companies.

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Zhang CDong RWang HZhong RChen JZhai JBagchi SZhang Y(2024)MAGPYProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3692034(683-698)Online publication date: 10-Jul-2024
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cover image ACM Other conferences

OSDI'18: Proceedings of the 13th USENIX conference on Operating Systems Design and Implementation

October 2018

815 pages

ISBN:9781931971478

Program Chairs:
Andrea Arpaci-Dusseau
University of Wisconsin-Madison
,
Geoff Voelker
University of California, San Diego

Sponsors

NetApp
Google Inc.
NSF
Microsoft: Microsoft
Facebook: Facebook

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SIGOPS: ACM Special Interest Group on Operating Systems

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USENIX Association

United States

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Published: 08 October 2018

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