Oral

CATWILD: Compiler Autotuning for TPU workloads in the Wild

Ignacio Cano ⋅ Yu Wang ⋅ Phitchaya Phothilimthana ⋅ Mike Burrows ⋅ ⋅ Matheus Camargo ⋅ Alexander Wertheim ⋅ Chao Wang ⋅ David Liu ⋅ Tengyu Sun ⋅ Arissa Wongpanich ⋅ Christof Angermueller ⋅ ⋅ ⋅ Vineetha Govindaraj ⋅ Amit Sabne ⋅ ⋅ ⋅ Berkin Ilbeyi ⋅ Ryan Lefever ⋅ Mehrdad Khani ⋅ Subhankar Shah ⋅ Ankit Sinha ⋅ ⋅ ⋅ ⋅ Nikhil Sarda ⋅ ⋅ ⋅ ⋅ Emily Donahue ⋅ Sami Abu-El-Haija ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ Naveen Kumar

Abstract

Compilers play a fundamental role at achieving peak performance for machine learning (ML) workloads. However, given the diverse nature of workloads and accelerators, compilers' heuristics and analytical cost models often result in sub-optimal performance, and thus waste precious datacenter resources. Furthermore, the multitude of tunable parameters and their complex interplay often make it impossible for human experts to manually find optimal configurations. In this paper, we present CATWILD, a system that automatically optimizes ML jobs in Google's TPU fleet using compiler autotuning techniques. We describe CATWILD’s design and implementation, and evaluate its performance using a handful of representative metrics. We further report experiences and lessons learned from its five-year development and operation. To the best of our knowledge, CATWILD represents the first ML compiler autotuning solution deployed in datacenters at scale. Its successful rollout yielded substantial benefits, optimizing over 70% of daily TPU training jobs and achieving significant chip savings.

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