# Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning

arXiv: https://arxiv.org/abs/1906.08879 · HTML: https://mlsanity.com/paper/1906.08879/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2019.

## Built on by (most cited first, 26 of 26)

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- [Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learning](https://mlsanity.com/paper/2110.10548/) (2021)
- [A Structure-Aware Framework for Learning Device Placements on Computation Graphs](https://mlsanity.com/paper/2405.14185/) (2024)
- [A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules](https://mlsanity.com/paper/2112.04041/) (2021)
- [Baechi: Fast Device Placement of Machine Learning Graphs](https://mlsanity.com/paper/2301.08695/) (2023)
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- [Resource Allocation in Disaggregated Data Centre Systems with Reinforcement Learning](https://mlsanity.com/paper/2106.02412/) (2021)
- [Celeritas: Fast Optimizer for Large Dataflow Graphs](https://mlsanity.com/paper/2208.00184/) (2022)
- [Moirai: Towards Optimal Placement for Distributed Inference on Heterogeneous Devices](https://mlsanity.com/paper/2312.04025/) (2023)
- [Accelerate Model Parallel Training by Using Efficient Graph Traversal Order in Device Placement](https://mlsanity.com/paper/2201.09676/) (2022)
- [RLFlow: Optimising Neural Network Subgraph Transformation with World Models](https://mlsanity.com/paper/2205.01435/) (2022)
- [Reinforcement Learning Approach for Mapping Applications to Dataflow-Based Coarse-Grained Reconfigurable Array](https://mlsanity.com/paper/2205.13675/) (2022)
- [Network Aware Compute and Memory Allocation in Optically Composable Data Centres with Deep Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2211.02466/) (2022)
- [Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2301.13799/) (2023)
- [Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment](https://mlsanity.com/paper/2302.06126/) (2023)
- [GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing](https://mlsanity.com/paper/2305.14562/) (2023)
- [PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks](https://mlsanity.com/paper/2306.14357/) (2023)
- [DOPPLER: Dual-Policy Learning for Device Assignment in Asynchronous Dataflow Graphs](https://mlsanity.com/paper/2505.23131/) (2025)
- [From LLM to Silicon: RL-Driven ASIC Architecture Exploration for On-Device AI Inference](https://mlsanity.com/paper/2604.07526/) (2026)

## Abstract (arXiv)

We present Placeto, a reinforcement learning (RL) approach to efficiently find device placements for distributed neural network training. Unlike prior approaches that only find a device placement for a specific computation graph, Placeto can learn generalizable device placement policies that can be applied to any graph. We propose two key ideas in our approach: (1) we represent the policy as performing iterative placement improvements, rather than outputting a placement in one shot; (2) we use graph embeddings to capture relevant information about the structure of the computation graph, without relying on node labels for indexing. These ideas allow Placeto to train efficiently and generalize to unseen graphs. Our experiments show that Placeto requires up to 6.1x fewer training steps to find placements that are on par with or better than the best placements found by prior approaches. Moreover, Placeto is able to learn a generalizable placement policy for any given family of graphs, which can then be used without any retraining to predict optimized placements for unseen graphs from the same family. This eliminates the large overhead incurred by prior RL approaches whose lack of generalizability necessitates re-training from scratch every time a new graph is to be placed.