DOPPLER: Dual-Policy Learning for Device Assignment in Asynchronous Dataflow Graphs
Xinyu Yao, Daniel Bourgeois, Abhinav Jain, Yuxin Tang, Jiawen Yao, Zhimin Ding, Arlei Silva, Chris Jermaine
Abstract
We study the problem of assigning operations in a dataflow graph to devices to minimize execution time in a work-conserving system, with emphasis on complex machine learning workloads. Prior learning-based approaches face three limitations: (1) reliance on bulk-synchronous frameworks that under-utilize devices, (2) learning a single placement policy without modeling the system dynamics, and (3) depending solely on reinforcement learning in pre-training while ignoring optimization during deployment. We propose Doppler, a three-stage framework with two policies—SEL for selecting operations and PLC for placing them on devices. Doppler consistently outperforms baselines by reducing execution time and improving sampling efficiency through faster per-episode training. Our results show that DOPPLER achieves up to 52.7% lower execution times than the best baseline.
BibTeX
@inproceedings{
yao2026doppler,
title={{DOPPLER}: Dual-Policy Learning for Device Assignment in Asynchronous Dataflow Graphs},
author={Xinyu Yao and Daniel Bourgeois and Abhinav Jain and Yuxin Tang and Jiawen Yao and Zhimin Ding and Arlei Silva and Chris Jermaine},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=OQQK8gMC5H}
}