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arXiv · 2509.26581

Graphite: A GPU-Accelerated Mixed-Precision Graph Optimization Framework

Shishir Gopinath·Karthik Dantu·Steven Y. Ko
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Abstract

We present Graphite, a GPU-accelerated nonlinear least squares graph optimization framework. It provides a CUDA C++ interface to enable the sharing of code between a real-time application, such as a SLAM system, and its optimization tasks. The framework supports techniques to reduce memory usage, including in-place optimization, support for multiple floating point types and mixed-precision modes, and dynamically computed Jacobians. We evaluate Graphite on well-known bundle adjustment problems and find that it achieves similar performance to MegBA, a solver specialized for bundle adjustment, while maintaining generality and using less memory. We also apply Graphite to global visual-inertial bundle adjustment on maps generated from stereo-inertial SLAM datasets, and observe speed-ups of up to 59x compared to a CPU baseline. Our results indicate that our framework enables faster large-scale optimization on both desktop and resource-constrained devices.

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Shishir Gopinath, Karthik Dantu, Steven Y. Ko. 2025-09-30. Graphite: A GPU-Accelerated Mixed-Precision Graph Optimization Framework. https://arxiv.org/abs/2509.26581

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