MLSys Seminar Episode 80: Mangpo Phothilimthana [Mon, 10:30 am PT]
Simran Arora
Hi everyone,
The MLSys Seminar is back! This quarter, the seminar will be on Mondays at 10:30am PT.
We’re excited to start off with a talk from Mangpo Phothilimthana on ML for ML Compilers! See everyone there!
Livestream link: https://www.youtube.com/watch?v=VASg2XNgj-4
Mangpo Phothilimthana (Google)
Title: ML for ML Compilers
Abstract: Search-based
techniques have been demonstrated effective in solving complex optimization problems that arise in domain-specific compilers for machine learning (ML). Unfortunately, deploying such techniques in production compilers is impeded by several limitations. In this
talk, I will present an autotuner for production ML compilers that can tune both graph-level and subgraph-level optimizations at multiple compilation stages. We demonstrate how to incorporate machine learning techniques such as a learned cost model and various
learning-based search strategies to reduce autotuning time. Our learned cost model has high accuracy and outperforms a heavily-optimized analytical performance model. In an evaluation across 150 ML training and inference models on Tensor Processing Units (TPUs),
the autotuner offers up to 2.4x and an average 5% runtime speedup over the heavily-optimized XLA compiler. I will outline how we deploy the learning-based XLA autotuner at datacenter scale to automatically tune the most heavily-used production models in Google's
fleet everyday. The deployed tile size autotuner has been saving approximately 2% of fleetwide TPU compute time. We recently released a public dataset (https://github.com/google-research-datasets/tpu_graphs)
for the learned cost model, and host an on-going Kaggle competition on the dataset (https://www.kaggle.com/competitions/predict-ai-model-runtime)
to promote more research in ML for Systems.
Bio: Phitchaya Mangpo Phothilimthana is a Staff research scientist at Google DeepMind (previously Google Brain), where she leads Machine Learning for Machine Learning Compilers effort (one of Google Brain moonshots in 2020). Her research interests include compilers, machine learning for systems, program synthesis, and energy-aware computing. Mangpo received an undergraduate degree in Computer Science from MIT and PhD from UC Berkeley. Mangpo was a recipient of Microsoft Research PhD Fellowship and Qualcomm Innovation Fellowship.
Best,
Simran
Simran Arora
Sent: Saturday, October 7, 2023 2:26 PM
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Subject: MLSys Seminar Episode 80: Mangpo Phothilimthana [Mon, 10:30 am PT]