Graphite: A GPU-Accelerated Mixed-Precision Graph Optimization Framework
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917339748892672 |
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| author | Gopinath, Shishir Dantu, Karthik Ko, Steven Y. |
| author_facet | Gopinath, Shishir Dantu, Karthik Ko, Steven Y. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26581 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graphite: A GPU-Accelerated Mixed-Precision Graph Optimization Framework Gopinath, Shishir Dantu, Karthik Ko, Steven Y. Robotics 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. |
| title | Graphite: A GPU-Accelerated Mixed-Precision Graph Optimization Framework |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.26581 |