The paper introduces AutoProfiler, a feedback-driven framework that learns a deterministic profiling tree for GPU kernel optimization. By iteratively refining the tree with REWRITE and SPLIT operations guided by an LLM and empirical evaluation, it selects the most useful profiling metrics for each kernel rather than relying on a fixed set. Experiments on KernelBench demonstrate that this adaptive profiling approach outperforms static configurations under limited profiling budgets.
Large language models have recently shown strong potential for automatically optimizing GPU kernels, yet their effectiveness depends critically on the hardware information available to the kernel opti...
KernelFoundry is a hardware‑aware evolutionary framework that optimizes GPU kernels by combining MAP‑Elites quality‑diversity search, meta‑prompt evolution, and template‑based parameter tuning. It generates SYCL and CUDA kernels, outperforming baseline methods with an average 2.3× speedup on KernelBench. The system is distributed, supports remote hardware access, and offers a flexible user interface for diverse real‑world kernel generation tasks.
arXiv:2606. 26453v1 Announce Type: new Abstract: We present KernelPro, a closed-loop multi-agent system that automatically generates, profiles, and iteratively optimizes GPU kernel code by integrating large language model (LLM) code generation with hardware profiler feedback and pluggable bottleneck detection tools.
arXiv:2605. 30359v2 Announce Type: replace-cross Abstract: Generating high-performance GPU kernels remains challenging due to the need for both correctness and hardware-aware optimization.
arXiv:2601. 15727v3 Announce Type: replace Abstract: The performance of modern AI systems is fundamentally constrained by the quality of their underlying GPU kernels, which translate high-level algorithmic semantics into low-level hardware operations.
arXiv:2607. 17979v1 Announce Type: cross Abstract: Large language models (LLMs) can assist GPU kernel generation, but their practical effectiveness depends on whether generated code can be reliably constrained, validated, profiled, and selected.
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:2607. 20806v1 Announce Type: new Abstract: Lightweight large language models (LLMs) are increasingly being deployed locally on personal computers and are expected to play a growing role in resource-constrained edge and mobile environments.
arXiv:2607. 24762v1 Announce Type: new Abstract: Machine learning models are increasingly embedded in everyday software, and most of their runtime is spent in a small set of compute kernels such as matrix multiplication, convolution, and normalization.
Large language models (LLMs) can assist GPU kernel generation, but their practical effectiveness depends on whether generated code can be reliably constrained, validated, profiled, and selected. This paper presents a harness-centered system for LLM-driven GPU kernel optimization in the MLSys 2026 FlashInfer AI Kernel Generation Contest on NVIDIA Blackwell B200 GPUs.
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.
Dooly is a profiling tool for large language model (LLM) inference that eliminates the need to re‑profile every operation for each configuration. By labeling input dimensions with taint propagation and only profiling operations missing from its latency database, Dooly builds a configuration‑agnostic, redundancy‑aware latency database that can be used as a drop‑in backend for simulators. In experiments across two GPU platforms, three attention backends, and multiple model architectures, Dooly achieves simulation accuracy within 5% MAPE for time‑to‑first‑token and 8% for total‑processing‑time while cutting profiling GPU‑hours by 56.4% compared to traditional methods.