GPU-Resident Top-K for Agentic RAG: I Built a CUDA Kernel So My Retrieval Step Would Stop Bouncing Off the GPU

DataKernelBench: Can LLMs Optimize Database Queries on GPUs?

DataKernelBench evaluates whether large language models (LLMs) can optimize database queries for GPU execution. The benchmark translates SQL into PyTorch TorchPlan programs and tests LLMs on optimizing core tensor snippets or full queries in CUDA or Triton, using execution-guided repair. On TPC‑H SF10 with an H100 GPU, the best full‑query CUDA configuration outperforms torch.compile by 2.11×, and extending TorchPlan with Dask‑cuDF enables a 2.54× speedup on TPC‑H SF100 across four H100 GPUs.

From Zero to GPU: A Guide to Building and Scaling Production-Ready CUDA Kernels

CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe

arXiv:2604. 01489v2 Announce Type: replace Abstract: High-performance GPU kernels are critical to modern machine learning systems, yet developing them remains a manual, expert-driven process.

AMDKernelVault: Large-Scale Datasets and Agentic Training for AMD GPU Kernel Optimization

AMDKernelVault is an open HIP and Triton kernel corpus and training framework designed for AMD CDNA GPUs. It includes 62,153 verified HIP kernels, 39,893 Triton kernels, and 2,377 ROCm library QA entries, and introduces agent-driven pipelines (HIPKernelGen and TritonKernelGen) that convert PyTorch references into GPU kernels, compile, validate, and profile them on AMD hardware. The corpus was used to fine‑tune Qwen3-8B, achieving the highest correctness on several benchmarks such as PyTorch-to-HIP, TritonBench‑G, and ROCmBench under fixed evaluation budgets.

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

arXiv:2604. 23466v2 Announce Type: replace Abstract: NVIDIA's CUDA Tile (CuTile) introduces a Python-based, tile-centric abstraction for GPU kernel development that aims to simplify programming while retaining Tensor Core and Tensor Memory Accelerator (TMA) efficiency on modern GPUs.

KernelBench-Verified: Do LLM-Generated Kernels Actually Beat PyTorch?

arXiv:2607. 16241v1 Announce Type: cross Abstract: Recent large language models (LLMs) can generate custom CUDA kernels that appear to outperform PyTorch on benchmarks such as KernelBench.