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FACT: Compositional Kernel Synthesis with a Three-Stage Agentic Workflow

Sina Heidari, Dimitrios S. Nikolopoulos

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Tags: Computer science, CUBLAS, CUDA, Deep learning, nVidia, nVidia A100

May 3, 2026 by hgpu

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Divakar Kumar Yadav, Tian Zhao, Deepak Kumar

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Tags: Computer science, CUBLAS, CUDA, LLM, nVidia, nVidia B200, nVidia H100, nVidia RTX PRO 6000, Package, Performance, Triton

May 3, 2026 by hgpu

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

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

Songqiao Su, Xiaofei Sun, Xiaoya Li, Albert Wang, Jiwei Li, Chris Shum

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Tags: Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, nVidia A100, Package

December 21, 2025 by hgpu

High Performance Matrix Multiplication

Ethan Davis

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Tags: BLAS, Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, OpenMP, Package, Performance, Python, Tesla V100

September 14, 2025 by hgpu

A Novel Compiler Transformation for Fast Sparse Matrix Multiplication in GPUs

Hossein Albakri, Kazem Cheshmi

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Tags: Compilers, Computer science, CUBLAS, CUDA, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia A100, Programming Languages, Sparse matrix

June 22, 2025 by hgpu

Evaluation of computational and energy performance in matrix multiplication algorithms on CPU and GPU using MKL, cuBLAS and SYCL

L.A. Torres, Carlos J. Barrios H, Yves Denneulin

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia A100, Package, Performance, SYCL

June 2, 2024 by hgpu

DGEMM on Integer Matrix Multiplication Unit

Hiroyuki Ootomo, Katsuhisa Ozaki, Rio Yokota

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Tags: Computer science, CUBLAS, CUDA, Deep learning, Linear Algebra, Machine learning, Matrix multiplication, nVidia, nVidia A100, nVidia Jetson AGX Orin, nVidia RTX 6000 Ada, nVidia Titan RTX, Package

June 25, 2023 by hgpu

Performance study on GPU offloading techniques using the Gauss matrix inverse algorithm

Yannik Könneker

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 1080, OpenACC, OpenCL, Package, Performance, Tesla V100, Thesis

April 17, 2022 by hgpu

High Performance GPU Code Generation for Matrix-Matrix Multiplication using MLIR: Some Early Results

Navdeep Katel, Vivek Khandelwal, Uday Bondhugula

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Tags: Code generation, Computer science, CUBLAS, CUDA, HPC, Matrix multiplication, nVidia, nVidia GeForce RTX 3090, Package

September 5, 2021 by hgpu

CoCoNet: Co-Optimizing Computation and Communication for Distributed Machine Learning

Abhinav Jangda, Jun Huang, Guodong Liu, Amir Hossein Nodehi Sabet, Saeed Maleki, Youshan Miao, Madanlal Musuvathi, Todd Mytkowicz, Olli Sarikivi

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Tags: Code generation, Computer science, CUBLAS, CUDA, Deep learning, Machine learning, nVidia, nVidia DGX-2

May 23, 2021 by hgpu

SLATE port to AMD and Intel platforms

Ahmad Abdelfattah, Mohammed Al Farhan, Cade Brown, Mark Gates, Dalal Sukkari, Asim YarKhan, Jack Dongarra

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, OpenCL, Package, SYCL

April 18, 2021 by hgpu

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

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

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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high performance computing on graphics processing units: hgpu.org

FACT: Compositional Kernel Synthesis with a Three-Stage Agentic Workflow

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

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

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

High Performance Matrix Multiplication

A Novel Compiler Transformation for Fast Sparse Matrix Multiplication in GPUs

Evaluation of computational and energy performance in matrix multiplication algorithms on CPU and GPU using MKL, cuBLAS and SYCL

DGEMM on Integer Matrix Multiplication Unit

Performance study on GPU offloading techniques using the Gauss matrix inverse algorithm

High Performance GPU Code Generation for Matrix-Matrix Multiplication using MLIR: Some Early Results

CoCoNet: Co-Optimizing Computation and Communication for Distributed Machine Learning

SLATE port to AMD and Intel platforms

Recent source codes

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

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

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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