{"ID":2862971,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26581","arxiv_id":"2509.26581","title":"Graphite: A GPU-Accelerated Mixed-Precision Graph Optimization Framework","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.","short_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 opt...","url_abs":"https://arxiv.org/abs/2509.26581","url_pdf":"https://arxiv.org/pdf/2509.26581v2","authors":"[\"Shishir Gopinath\",\"Karthik Dantu\",\"Steven Y. Ko\"]","published":"2025-09-30T17:39:53Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}