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StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

Shiyang Li, Zijian Zhang, Winson Chen, Yuebo Luo, Mingyi Hong, Caiwen Ding

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Tags: Benchmarking, Code generation, Computer science, CUBLAS, CUDA, LLM, nVidia, nVidia H200, nVidia RTX PRO 6000

March 4, 2026 by hgpu

Joint Training on AMD and NVIDIA GPUs

Jon Hu, Thomas Jia, Jing Zhu, Zhendong Yu

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Tags: AMD, AMD Radeon Instinct MI325X, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia H200, ROCm

March 1, 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

Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation

Le Chen1, Nuo Xu, Winson Chen, Bin Lei, Pei-Hung Lin, Dunzhi Zhou, Rajeev Thakur, Caiwen Ding, Ali Jannesari, Chunhua Liao

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia H200

December 21, 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

RDMA Point-to-Point Communication for LLM Systems

Nandor Licker, Kevin Hu, Vladimir Zaytsev, Lequn Chen

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Tags: Computer science, CUDA, LLM, nVidia, nVidia H200, Package, Performance, RDMA

November 9, 2025 by hgpu

Combining Performance and Productivity: Accelerating the Network Sensing Graph Challenge with GPUs and Commodity Data Science Software

Siddharth Samsi, Dan Campbell, Emanuel Scoullos, Oded Green

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Tags: Benchmarking, Computer science, CUDA, HPC, nVidia, nVidia A100, nVidia H100, nVidia H200, Sensing

September 14, 2025 by hgpu

Harnessing Batched BLAS/LAPACK Kernels on GPUs for Parallel Solutions of Block Tridiagonal Systems

David Jin, Alexis Montoison, Sungho Shin

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Tags: AMD Radeon Instinct MI300X, ATI, Benchmarking, BLAS, Computer science, CUDA, Factorization, Julia, nVidia, nVidia H200, Package, ROCm

September 7, 2025 by hgpu

Kevin: Multi-Turn RL for Generating CUDA Kernels

Carlo Baronio, Pietro Marsella, Ben Pan, Simon Guo, Silas Alberti

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Tags: Artificial intelligence, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia H100, nVidia H200, Performance

July 20, 2025 by hgpu

Serving LLMs in HPC Clusters: A Comparative Study of Qualcomm Cloud AI 100 Ultra and High-Performance GPUs

Mohammad Firas Sada, John J. Graham, Elham E Khoda, Mahidhar Tatineni, Dmitry Mishin, Rajesh K. Gupta, Rick Wagner, Larry Smarr, Thomas A. DeFanti, Frank Würthwein

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Tags: AMD Radeon Instinct MI300A, ATI, Cloud, Computer science, Deep learning, HPC, nVidia, nVidia A100, nVidia H200

July 13, 2025 by hgpu

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

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

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

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