Argus generates GPU kernels achieving 99-104% of hand-optimized throughput on key LLM kernels by enforcing compile-time data-flow invariants via a tag-based DSL and an in-context RL planner.
CUDA-L2: Surpassing cublas performance for matrix multipli- cation through reinforcement learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.DC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
ARGUS: Agentic GPU Optimization Guided by Data-Flow Invariants
Argus generates GPU kernels achieving 99-104% of hand-optimized throughput on key LLM kernels by enforcing compile-time data-flow invariants via a tag-based DSL and an in-context RL planner.