RESOLVE: Language-Agnostic Validation of GPU Kernels Through Testing, Reduction, and Proof
cs.PL, cs.DC, cs.LG
Submitted: 2026-10-05
Updated: 2026-10-05
Code: https://github.com/NVIDIA/cutlass
Sources
- FlashMoE: Fast Distributed MoE in a Single Kernel
- ProofWright: Towards Agentic Formal Verification of CUDA
- MPK: A Compiler and Runtime for Mega-Kernelizing Tensor Programs
- FastMoE: A Fast Mixture-of-Expert Training System
- Descend: A Safe GPU Systems Programming Language
- TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators
- KernelBench: Can LLMs Write Efficient GPU Kernels?
- TileLang: A Composable Tiled Programming Model for AI Systems
- CAKE: Compiler-Agent Co-Design for Frontier Kernel Evolution
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