发表机构
Dartmouth College（达特茅斯学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对GPU机器人学缺乏可复用数值库的问题，提出GLASS库，通过架构特定放置决策和静态编译优化，在边缘设备上实现最高73倍性能提升，并开源发布。
AI 中文摘要
GPU机器人学缺乏成熟CPU技术栈所具有的可复用数值基础设施，转而依赖引入开销的编译器框架或反复重新实现数值库。为解决这一问题，我们提出GLASS（GPU线性代数简单子程序），一个仅含头文件的CUDA C++库，在单一可组合设备API下提供线程级、线程束级、线程块级以及NVIDIA支持的机器人规模线性代数与几何计算实现。GLASS将实现选择、执行范围和启动打包视为由离线测量确定并在编译时静态解析的架构特定放置决策。这一点至关重要，因为最佳与最差放置之间的差异中位数为4.9倍（最大81倍），在Jetson AGX Orin与RTX 5090之间，396个推荐放置中有145个发生变化，而与AGX Xavier相比则有162个变化。这些影响在边缘端最为显著，因为GLASS相对于PyTorch和JAX中最佳者的优势在Orin上高达73倍，而在RTX 5090上为12倍。GLASS以开源形式发布，附带独立数值预言机和源代码绑定的本地GPU测试证明。最后，将GLASS集成到已发表的机器人系统中，既暴露了一个先前存在的数值错误，又将嵌入式运行时性能提升了高达1.5倍。
英文摘要
GPU robotics lacks the reusable numerical infrastructure of mature CPU stacks, instead relying on compiler frameworks that introduce overhead or repeatedly reimplementing numerical libraries. To address this, we introduce GLASS (GPU Linear Algebra Simple Subroutines), a header-only CUDA C++ library that provides thread-, warp-, block-, and NVIDIA-backed implementations of robotics-scale linear algebra and geometric computations under one composable device API. GLASS treats implementation choice, execution scope, and launch packing as architecture-specific placement decisions determined by offline measurement and resolved statically at compile time. This is critical as the best and worst placements differ by a median of 4.9x (max 81x), with 145 of 396 recommended placements changing between a Jetson AGX Orin and an RTX 5090, and 162 of 396 versus an AGX Xavier. These stakes are highest at the edge as GLASS's advantage over the best of PyTorch and JAX is as much as 73x on the Orin versus 12x on the RTX 5090. GLASS is released open source with independent numerical oracles and source-bound local-GPU test attestation. Finally, integrating GLASS with published robotics systems both exposed a pre-existing numerical bug and improved embedded runtimes by up to 1.5x.
Comments8 pages, 7 figures, 2 tables
