# GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing

arXiv: https://arxiv.org/abs/2305.14562 · HTML: https://mlsanity.com/paper/2305.14562/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 14 of 14)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [Learning Scheduling Algorithms for Data Processing Clusters](https://mlsanity.com/paper/1810.01963/) (2018)
- [Device Placement Optimization with Reinforcement Learning](https://mlsanity.com/paper/1706.04972/) (2017)
- [The Optimal Reward Baseline for Gradient-Based Reinforcement Learning](https://mlsanity.com/paper/1301.2315/) (2013)
- [Network-Aware Optimization of Distributed Learning for Fog Computing](https://mlsanity.com/paper/2004.08488/) (2020)
- [Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning](https://mlsanity.com/paper/1906.08879/) (2019)
- [GDP: Generalized Device Placement for Dataflow Graphs](https://mlsanity.com/paper/1910.01578/) (2019)
- [Learning to Optimize DAG Scheduling in Heterogeneous Environment](https://mlsanity.com/paper/2103.06980/) (2021)
- [A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments](https://mlsanity.com/paper/2110.12415/) (2021)
- [Accurate Cooperative Sensor Fusion by Parameterized Covariance Generation for Sensing and Localization Pipelines in CAVs](https://mlsanity.com/paper/2209.03306/) (2022)
- [GCNScheduler: Scheduling Distributed Computing Applications using Graph Convolutional Networks](https://mlsanity.com/paper/2110.11552/) (2021)

## Abstract (arXiv)

Careful placement of a computational application within a target device cluster is critical for achieving low application completion time. The problem is challenging due to its NP-hardness and combinatorial nature. In recent years, learning-based approaches have been proposed to learn a placement policy that can be applied to unseen applications, motivated by the problem of placing a neural network across cloud servers. These approaches, however, generally assume the device cluster is fixed, which is not the case in mobile or edge computing settings, where heterogeneous devices move in and out of range for a particular application. We propose a new learning approach called GiPH, which learns policies that generalize to dynamic device clusters via 1) a novel graph representation gpNet that efficiently encodes the information needed for choosing a good placement, and 2) a scalable graph neural network (GNN) that learns a summary of the gpNet information. GiPH turns the placement problem into that of finding a sequence of placement improvements, learning a policy for selecting this sequence that scales to problems of arbitrary size. We evaluate GiPH with a wide range of task graphs and device clusters and show that our learned policy rapidly find good placements for new problem instances. GiPH finds placements with up to 30.5% lower completion times, searching up to 3X faster than other search-based placement policies.