A Learned Performance Model for Tensor Processing Units

• URL: http://arxiv.org/abs/2008.01040v2
• Date: Thu, 18 Mar 2021 04:49:15 GMT
• Title: A Learned Performance Model for Tensor Processing Units
• Authors: Samuel J. Kaufman, Phitchaya Mangpo Phothilimthana, Yanqi Zhou, Charith Mendis, Sudip Roy, Amit Sabne, and Mike Burrows
• Abstract summary: We demonstrate a method of learning performance models from a corpus of graph programs for Processing Unit (TPU) instances. We show that our learned model outperforms a heavily-optimized analytical performance model on two tasks. It helps an autotuner discover faster programs in a setting where access to TPUs is limited or expensive.
• Score: 5.733911161090224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate hardware performance models are critical to efficient code generation. They can be used by compilers to make heuristic decisions, by superoptimizers as a minimization objective, or by autotuners to find an optimal configuration for a specific program. However, they are difficult to develop because contemporary processors are complex, and the recent proliferation of deep learning accelerators has increased the development burden. We demonstrate a method of learning performance models from a corpus of tensor computation graph programs for Tensor Processing Unit (TPU) instances. We show that our learned model outperforms a heavily-optimized analytical performance model on two tasks -- tile-size selection and operator fusion -- and that it helps an autotuner discover faster programs in a setting where access to TPUs is limited or expensive.

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