Ankit Sinha
Software Engineer
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CATWILD: Compiler Autotuning For TPU workloads in the Wild
Ignacio Cano
Yu Emma Wang
Mike Burrows
Ziqiang Feng
Matheus Camargo
Chao Wang
David H. Liu
Tengyu Sun
Alex Wertheim
Arissa Wongpanich
Hyojun Kim
Wenqi Cao
Aleksey Orekhov
Emma Sevastian
Mehrdad Khani
Karthik Srinivasa Murthy
Berkin Ilbeyi
Ryan Lefever
Arjun Khare
Matthew Bierbaum
Jeremiah Wilke
Sami Abu-El-Haija
Νikhil Sarda
Vineetha Govindaraj
Shobha Vasudevan
Kirill Gugaev
Idan Nachman
Jie Sun
Jose Baiocchi Paredes
Samrat Ghosh
Domagoj Babic
Zongwei Zhou
Naveen Kumar
Mangpo Phothilimthana
2026
Preview 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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