GLASS: Architecture-Tuned, Composable, Device-Side Linear Algebra for Edge Robotics and Beyond

arXiv:2609.28179 · cs.RO, cs.DC, cs.MS · Submitted 2026-09-23 · Read on arXiv

cs.RO, cs.DC, cs.MS

Submitted: 2026-09-23

Updated: 2026-09-23

Comments: 8 pages, 7 figures, 2 tables

Code: https://github.com/A2R-Lab/GLASS

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 8× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• GPU Linear Algebra Simple Subroutines — 2× in this paper
• Verification — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• CPU-only Continuous Integration — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Edge Deployment — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• IEEE International Conference on Robotics and Automation — 1× in this paper
• Point Clouds — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• MPPI-Generic: A CUDA Library for Stochastic Trajectory Optimization
• Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization
• Kokkos Kernels: Performance Portable Sparse/Dense Linear Algebra and Graph Kernels
• KernelBench: Can LLMs Write Efficient GPU Kernels?
• KernelBench-Verified: Do LLM-Generated Kernels Actually Beat PyTorch?
• The Correctness Illusion in LLM-Generated GPU Kernels

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