Oral Thu, May 21, 2026 • 4:30 PM – 4:45 PM PDT

XProf: An Open, Scalable, and Extensible Profiling System for the Modern ML Stack

Robert Hundt ⋅ Naveen Kumar ⋅ Jose Baiocchi Paredes ⋅ Scott Goodson ⋅ Clive Verghese ⋅ Prasanna Rengasamy ⋅ Kelvin Le ⋅ Jiya Zhang ⋅ Charles Alaras ⋅ Yin Zhang ⋅ Kan Cai ⋅ Jiten Thakkar ⋅ Sai Ganesh Bandiatmakuri ⋅ Yogesh SY ⋅ Ani Udipi ⋅ Vikas Agarwal

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Abstract

Optimizing Large Models across thousands of accelerators requires deep system expertise. To address modern machine learning (ML) optimization needs, we present XProf, the ML profiler for the OpenXLA ecosystem. XProf delivers actionable optimization suggestions and in-depth performance analysis, empowering ML researchers and framework users to improve efficiency without specialized systems knowledge. XProf provides a unified, full-stack view of both host (CPU) and device (accelerator - TPUs/GPUs) performance, leveraging tools like the Roofline Model for comprehensive analysis. XProf’s distributed architecture is designed to monitor thousands of chips with minimal workload overhead (<1%). This architecture is made pluggable through the open-source PJRT C API extension, which has facilitated its adoption by third-party accelerator vendors. XProf has been instrumental in achieving significant efficiency gains at Google and winning MLPerf submissions. This paper presents the design and architecture of XProf, showcases its differentiating tools and capabilities, and highlights its impact within Google and across the industry as a state of the art ML profiler. XProf is available as part of the OpenXLA project at https://github.com/openxla/xprof.