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FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers

Peiyu Zang, Bosen Xie, Ruoxiang Xu, Yongqiang Cai

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, Package, Partial differential equations, PDEs, Physics, Triton

August 4, 2026 by hgpu

CUDABeaver: Benchmarking LLM-Based Automated CUDA Debugging

Shiyang Li, Haoyang Chen, Mattia Fazzini, Caiwen Ding

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Tags: Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia A100, nVidia H200, nVidia RTX PRO 6000

May 20, 2026 by hgpu

CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe

Tara Saba, Anne Ouyang, Xujie Si, Fan Long

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Tags: Code generation, Computer science, CUDA, Machine learning, Matrix multiplication, nVidia, nVidia GeForce RTX 4090

April 13, 2026 by hgpu

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Jaber Jaber, Osama Jaber

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia B200, nVidia H100, Package, Triton

March 26, 2026 by hgpu

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

Yuxuan Han, Meng-Hao Guo, Zhengning Liu, Wenguang Chen, Shi-Min Hu

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce RTX 4090, Package

March 15, 2026 by hgpu

SynPerf: A Hybrid Analytical-ML Framework for GPU Performance Prediction

Kaixuan Zhang, Yunfan Cui, Shuhao Zhang, Chutong Ding, Shiyou Qian, Luping Wang, Jian Cao, Guangtao Xue, Cheng Huang, Guodong Yang, Liping Zhang

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia A100, nVidia A40, nVidia H100, nVidia H20, nVidia H200, nVidia H800, nVidia L20, nVidia L40, nVidia RTX 6000 Ada, Performance, Triton

January 25, 2026 by hgpu

Equivalence Checking of ML GPU Kernels

Kshitij Dubey, Benjamin Driscoll, Anjiang Wei, Neeraj Kayal, Rahul Sharma, Alex Aiken

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Tags: Computer science, CUDA, Deep learning, LLM, Machine learning, Matrix multiplication, nVidia, PTX

January 12, 2026 by hgpu

AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization

Genghan Zhang, Shaowei Zhu, Anjiang Wei, Zhenyu Song, Allen Nie, Zhen Jia, Nandita Vijaykumar, Yida Wang, Kunle Olukotun

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Tags: AI, Benchmarking, Computer science, LLM, Machine learning, Performance

December 29, 2025 by hgpu

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

Pierre Abillama, Changwoo Lee, Juechu Dong, David Blaauw, Dennis Sylvester, Hun-Seok Kim

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Tags: AI, Computer science, CUDA, Machine learning, Memory, nVidia, nVidia A40, nVidia Jetson Orin Nano, Package

December 29, 2025 by hgpu

ML Inference Scheduling with Predictable Latency

Haidong Zhao, Nikolaos Georgantas

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Tags: Computer science, Machine learning, nVidia, nVidia L4, Task scheduling

December 21, 2025 by hgpu

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

Haonan Li, Keyu Man, Partha Kanuparthy, Hanning Chen, Wei Sun, Sreen Tallam, Chenguang Zhu, Kevin Zhu, Zhiyun Qian

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Tags: Computer science, CUDA, LLM, Machine learning, nVidia, nVidia H100, Package, PyTorch, Triton

December 15, 2025 by hgpu

Microbenchmarking NVIDIA’s Blackwell Architecture: An in-depth Architectural Analysis

Aaron Jarmusch, Sunita Chandrasekaran

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Tags: Benchmarking, Computer science, CUDA, HPC, Machine learning, nVidia, nVidia B200, nVidia H200, PTX

December 7, 2025 by hgpu

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization

NVIDIA Object Oriented Agents

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

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high performance computing on graphics processing units: hgpu.org

FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers

CUDABeaver: Benchmarking LLM-Based Automated CUDA Debugging

CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

SynPerf: A Hybrid Analytical-ML Framework for GPU Performance Prediction

Equivalence Checking of ML GPU Kernels

AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

ML Inference Scheduling with Predictable Latency

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

Microbenchmarking NVIDIA’s Blackwell Architecture: An in-depth Architectural Analysis

Recent source codes

PTXBench: an open-source environment for kernel agents with architecture-specific CUDA/PTX

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

TensorCorePTX: PTX Tensor Core GEMM Exploration

FlashPDE: Triton-fused PDE stencil kernels for physics-informed neural networks

PortLBM: Portable GPU-accelerated Lattice Boltzmann Method Simulations

Harness Engineering for LLM-Driven GPU Kernel Generation

CANNBot Skills

NVIDIA Object Oriented Agents

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

CuFuzz: An API-Knowledge-Graph Coverage-Driven Fuzzing Framework for CUDA Libraries

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