Skip to content
Log in
#graph neural networks Open access

Computational Graph Optimization with Program Synthesis Techniques

Jincheng Zhang
Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)
Graph Theory and Algorithms

Abstract

The optimization of computational graphs is a critical step in deploying machine learning models, particularly deep neural networks. Traditional manual optimization methods are labor-intensive, require deep domain expertise, and are prone to human error. This paper proposes a novel approach leveraging program synthesis techniques to automate the process of computational graph optimization. The core idea is to treat computational graph optimization as a program synthesis problem, where a synthesis engine generates efficient execution plans based on the graph's structure, target hardware specifications, and performance constraints. We introduce a framework that translates performance objectives (e.g., minimizing latency, maximizing throughput) into formal synthesis targets. The system employs a combination of constraint solving, search algorithms, and potentially machine learning to explore the space of possible execution plans and identify the optimal one. This approach promises to significantly reduce the time and effort required for optimization, improve the accuracy of the resulting plans, and facilitate the deployment of complex models on diverse hardware platforms. The key contribution lies in applying program synthesis to a traditionally manual and complex optimization task, opening new avenues for automated and adaptive model deployment.

DOI
View source

Similar papers

#graph neural networks Oct 2026

Locality-Preserving Graph Laplacian Manifold Learning Based Model Predictive Control for Three-Phase Inverters

This article presents a model predictive control (MPC) strategy for three-phase inverters based on locality preserving projections (LPPs). Unlike conventional machine learning–based MPC approaches that rely on predefined or high-dimensional input features, the proposed LPP-MPC automatically extracts compact, informative representations by preserving the data’s intrinsic geometric structure. This dimensionality reduction enables fast linear control-law evaluation with computational complexity O(1), making the controller well-suited for real-time implementation. Experimental results demonstrate that the LPP-MPC achieves lower total harmonic distortion (THD) and reduced tracking error compared to quadratic-programming MPC under both linear and nonlinear load conditions, and the proposed controller maintains consistently lower THD throughout load transients than other methods such as two-degree-of-freedom MPC. Compared to existing model-free MPC and deep learning neural network, the LPP-MPC has the lowest THD and root mean square error with the least computational time owing to its efficient linear structure and strong generalization capability.

Jianwu Zeng, Lizheng Cheng, V. Winstead et al. · 1 citation

Related blog posts