用符号编程在GPU上实现高效非线性优化，速度比现有工具快5至20倍。
Caspar: CUDA Accelerator for Symbolic Programming with Adaptive Reordering
- 从符号表达式自动生成优化的CUDA内核，支持李群运算
- 在BAL数据集上速度提升5-20倍，内存占用更低，精度相当
- 适合需要高速非线性优化的机器人与视觉系统开发者
我们提出Caspar，一个使现代GPU能力更易用于机器人领域的库，提供最先进的非线性GPU求解器，可应用于多种优化问题。Caspar通过自动将Python中的符号表达式转化为优化的CUDA内核，弥合了符号编程与高性能C++ GPU运行时之间的差距。基于SymForce库，用户可轻松定义并组合包含李群操作的符号表达式，生成定制化CUDA内核。作为求解器使用时，用户只需定义符号残差函数，Caspar便通过符号微分生成所需GPU内核并完成非线性优化。本文介绍Caspar核心组件，并在大型捆绑调整（BAL）数据集上展示其性能。与当前最佳方法对比，其速度提升5至20倍，内存消耗更少，精度相当。这验证了符号化GPU编程的有效性。Caspar作为SymForce的一部分开源，地址为https://github.com/symforce-org/symforce。
原文摘要 · Abstract (English)
We present Caspar, a library that makes the power of modern GPUs more accessible in robotics and provides a state-of-the-art nonlinear GPU solver that can be applied to a wide range of different optimization problems. Caspar bridges the gap between expressive symbolic programming in Python and high-performance GPU runtimes in C++ by automatically generating optimized CUDA kernels from symbolic expressions. Building on the SymForce library, users can easily define and combine symbolic expressions, including Lie group operations, to generate custom CUDA kernels. To use Caspar as a solver, users need only define the symbolic residual functions; Caspar then uses symbolic differentiation to generate the necessary GPU kernels and interfaces to perform nonlinear optimization. In this paper, we present the core components of Caspar and showcase its performance by performing bundle adjustment on the Bundle Adjustment in the Large (BAL) dataset. We benchmark Caspar against other state-of-the-art bundle adjusters and show that it is 5 to 20 times faster than the best alternative, requires less memory, and achieves similar accuracy. This illustrates the benefit of our symbolic GPU programming approach. Caspar is released as part of SymForce and is freely available at https://github.com/symforce-org/symforce
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