Hugging Face Trending Papers
Less is more: learning what to profile for GPU kernel optimization
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
5d ago
Less is more: learning what to profile for GPU kernel optimization
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.
arXiv Machine Learning
Sep 4
KernelFoundry: Hardware-aware evolutionary GPU kernel optimization
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 Machine Learning
Jun 26
Optimizing CUDA like a Human: Micro-Profiling Tools as Expert Surrogates for LLM-Based GPU Kernel Optimization
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 Machine Learning
Aug 4
Kernel Foundry: A Diagnosis-driven Evolutionary Kernel Optimizer with Multi-Experts
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 Machine Learning
Jun 9
Towards Automated Kernel Generation in the Era of LLMs
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 AI
Jul 21
Harness Engineering for LLM-Driven GPU Kernel Generation
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.