ATTUNE is a coding agent that automatically tunes large‑language‑model training recipes within a GPU‑hour budget, adjusting one lever at a time guided by search rules, scripts, and past tuning lessons. It includes a correctness gate that blocks any configuration known to alter the training objective. In Amazon’s production, ATTUNE raises throughput by an average of 1.7× on H100, H200, and B200 clusters and outperforms NVIDIA’s AutoModel benchmark recipes and other automated tuners, achieving up to 2.6× improvement on MiniMax‑M2 and more than four times the best alternative on GLM‑4.5.
arXiv:2607. 14541v1 Announce Type: new Abstract: Existing GPU kernel generation benchmarks draw problems from synthetic or curated sources that diverge from deployed workloads.
arXiv:2605.28213v2 Announce Type: replace Abstract: LLM-based agents are increasingly used to generate GPU kernels, but they often struggle to determine when an optimization is sound because its requ...
arXiv:2607. 20468v1 Announce Type: new Abstract: AI agents are increasingly used to automate research and development tasks, yet existing benchmarks typically evaluate them on prescribed workflows or narrow action spaces.
arXiv:2610.03226v1 Announce Type: cross Abstract: GPU kernels generated by large language model (LLM) agents can remain less efficient than expert implementations, but runtime alone does not reveal h...
arXiv:2504. 15610v4 Announce Type: replace Abstract: Fine-tuning a 7B language model for specialized advising is attractive in resource-constrained settings, but multi-epoch runs routinely exceed the wall-clock limits of the free-tier GPUs (Kaggle, Colab) such users rely on.
arXiv:2604.18616v2 Announce Type: replace-cross Abstract: LLM coding agents can generate correct GPU kernels, but their performance still trails expert libraries. Reaching peak throughput requires co...
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.
arXiv:2606. 09682v1 Announce Type: new Abstract: AutoMegaKernel (AMK) compiles a HuggingFace Llama-family model into a single persistent cooperative CUDA kernel that runs the whole forward pass in one launch, with no per-model hand-written CUDA.
Evolving Agent Teams (EAT) is a multi‑agent framework designed to overcome local optima that plague existing coding agents when optimizing GPU kernels. It employs a central supervisor to diagnose and restructure agent teams and a persistent file system that records learning documents, evolution history, and team artifacts. Across a range of benchmarks—including FlashInfer, FlashKDA, and RoboDojo—EAT consistently outperforms strong coding‑agent baselines, achieving up to 1.2× speedup on FlashInfer kernels and superior results on several other tasks.
The study investigates whether coding agents can reliably migrate PyTorch machine learning projects from GPUs to TPUs. Using Claude Code, ten projects were migrated, with eight meeting the project‑goal criterion but 76% of execution attempts failing, often due to compilation issues or silent changes in computation. The research highlights significant human effort (average 2.4 interventions) and variable costs, noting that while some TPUs can be faster and cheaper, failures and accuracy drops remain common.
Configuring distributed LLM training across hundreds of GPUs requires jointly optimizing tensor, pipeline, expert, context, and data parallelism alongside micro-batch size, recomputation, sharding, an...
arXiv:2606. 25207v1 Announce Type: new Abstract: Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding strong configurations within a limited budget.