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CUDA-LLM: LLMs Can Write Efficient CUDA Kernels

Wentao Chen, Jiace Zhu, Qi Fan, Yehan Ma, An Zou

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Tags: Artificial intelligence, Code generation, Computer sceince, CUDA, LLM, nVidia, nVidia GeForce GTX 1660, nVidia GeForce RTX 3090 Ti

June 15, 2025 by hgpu

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

TRUST: a thermalhydraulic software package for CFD simulations

TRUST: the HPC open-source CFD platform – from CPU to GPU

Modular: The Modular Platform (includes MAX & Mojo)

Mojo: MLIR-Based Performance-Portable HPC Science Kernels on GPUs for the Python Ecosystem

Allo: Accelerator Design Language

Dato: A Task-Based Programming Model for Dataflow Accelerators

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

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