As models become larger, ML accelerators are a scarce resource whose performance must be continually optimized to improve efficiency. Existing performance analysis tools are coarse grained, and fail to capture model performance at the machine-code level. In addition, these tools often do not provide specific recommendations for optimizations. We present xPU-Shark, a fine-grained methodology for analyzing ML models at the machine-code level that provides actionable optimization suggestions. Our core insight is to use a hardware-level simulator, an artifact of the hardware design process that we can re-purpose for performance analysis. xPU-Shark captures traces from production deployments running on accelerators and replays them in a modified microarchitecture simulator to gain low-level insights into the model's performance. We implement xPU-Shark for our in-house accelerator and used it to analyze the performance of several of our production LLMs, revealing several previously-unknown microarchitecture inefficiencies. Leveraging these insights, we optimize a common communication collective by up to 15% and reduce token generation latency by up to 4.1%.
View on arXiv@article{zarkadas2025_2503.14781,
title={ Fake Runs, Real Fixes -- Analyzing xPU Performance Through Simulation },
author={ Ioannis Zarkadas and Amanda Tomlinson and Asaf Cidon and Baris Kasikci and Ofir Weisse },
journal={arXiv preprint arXiv:2503.14781},
year={ 2025 }
}