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Tutoring LLM into a Better CUDA Optimizer

Matyáš Brabec, Jiří Klepl, Michal Töpfer, Martin Kruliš

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia A100, nVidia H100, nVidia V100, Package

October 26, 2025 by hgpu

Anonymized Network Sensing using C++26 std::execution on GPUs

Michael Mandulak, Sayan Ghosh, S M Ferdous, Mahantesh Halappanavar, George Slota

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Tags: Computer science, CUDA, Network communication, nVidia, nVidia A100

October 19, 2025 by hgpu

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Aksel Alpay, Vincent Heuveline

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Tags: AMD Radeon Pro VII, ATI, Benchmarking, Computer science, CUDA, HIP, Intel UHD 630, nVidia, nVidia RTX A5000, Package, Performance, SYCL

October 19, 2025 by hgpu

Compiler and Runtime Systems for Generative AI Models

Zihao Ye

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce RTX 3070, nVidia H100, Package, Tesla V100

October 19, 2025 by hgpu

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Yifan Zhao, Egan Johnson, Prasanth Chatarasi, Vikram Adve, Sasa Misailovic

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Tags: AMD Radeon Instinct MI300X, ATI, Computer science, CUDA, Deep learning, nVidia, nVidia A100, nVidia RTX A5000, nVidia RTX A6000, Package, Performance, ROCm

October 19, 2025 by hgpu

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Gabriel Kosmacher, Eric T. Phipps, Sivasankaran Rajamanickam

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Tags: Algorithms, Computer science, CUDA, Kokkos, Mathematical Software, nVidia, nVidia H100, OpenMP, Package

October 19, 2025 by hgpu

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

Lingcheng Kong, Jiateng Wei, Hanzhang Shen, Huan Wang

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Tags: AI, Code generation, Computer science, CUDA, Deep learning, LLM, Machine learning, nVidia, Package

October 12, 2025 by hgpu

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

Zeyu Sun, Jingjing Liang, Weiyi Wang, Chenyao Suo, Junjie Chen, Fanjiang Xu

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Tags: Computer science, Neural networks, nVidia, nVidia GeForce RTX 4090, OpenCL, Package, Software Engineering

October 12, 2025 by hgpu

High-Performance Computing: from Optimization to Automation

Bérenger Bramas

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Tags: AMD Radeon Instinct MI210, ATI, Computer science, CUDA, HIP, HPC, nVidia, nVidia A100, nVidia T600, Performance, Thesis

October 12, 2025 by hgpu

EvoEngineer: Mastering Automated CUDA Kernel Code Evolution with Large Language Models

Ping Guo, Chenyu Zhu, Siyuan Chen, Fei Liu, Xi Lin, Zhichao Lu, Qingfu Zhang

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Tags: AI, Code generation, Computer science, CUDA, Deep learning, LLM, nVidia, nVidia GeForce RTX 4090, nVidia H100, PyTorch

October 12, 2025 by hgpu

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

Shun-ichiro Hayashi, Koki Morita, Daichi Mukunoki, Tetsuya Hoshino, Takahiro Katagiri

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Tags: AI, Code generation, Computer science, CUDA, HPC, LLM, nVidia, OpenACC, OpenMP, Package, Tesla V100

October 5, 2025 by hgpu

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

Tihomir Thomas Bicanic

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Tags: Computer science, CUDA, nVidia, nVidia GeForce RTX 3090, OpenCL, Package, Performance, Rust, Security

October 5, 2025 by hgpu

CUDAnalyst (CUDA + Analyst)

Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation

CodegenBench

CodegenBench: Can LLMs Write Efficient Code Across Architectures?

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Agentic Code Optimization via Compiler-LLM Cooperation

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Tutoring LLM into a Better CUDA Optimizer

Anonymized Network Sensing using C++26 std::execution on GPUs

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

A Performance Portable Matrix Free Dense MTTKRP in GenTen

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

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

High-Performance Computing: from Optimization to Automation

EvoEngineer: Mastering Automated CUDA Kernel Code Evolution with Large Language Models

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

Recent source codes

CUDAnalyst (CUDA + Analyst)

CodegenBench

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Agentic Code Optimization via Compiler-LLM Cooperation

Device Virtual Machine (DVM)

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

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