high performance computing on graphics processing units: hgpu.org

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KernelOPT: Dispatch-Aware Agentic Search for GPU Kernel Optimization

Aheli Poddar, Sanskar Prasad, Arindam Samanta, Subha Chakraborty, Vishal Goyal, Rohit Singh Rathaur

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Tags: Computer science, CUBLAS, CUDA, Deep learning, LLM, nVidia, nVidia H200, Triton

September 28, 2026 by hgpu

Taming Bitwise Behavior in GPU Kernels with Tensor Core: Black-Box Reconstruction, Compiler Enforcement, and Static Verification

Ziteng Yang, Nicholas J. Riasanovsky, Warren Deng, Vivek Sarkar

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Tags: Computer science, CUBLAS, CUDA, nVidia, nVidia GB300, nVidia H100, PTX, Triton

September 14, 2026 by hgpu

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

Ximing Fan, Yong Fang, Peng Jia, Yang Liu, Yijia Xu, Xi Peng, Yuhao Zhou

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Tags: Computer science, CUBLAS, CUDA, FFT, LLM, nVidia, Package, Security

July 13, 2026 by hgpu

Fearless Concurrency on the GPU

Melih Elibol, Jared Roesch, Isaac Gelado, Eric Buehler, Michael Garland

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Tags: Computer science, CUBLAS, CUDA, nVidia, nVidia B200, nVidia GeForce RTX 5090, Performance, Python, Rust

June 17, 2026 by hgpu

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

Brian2 Atlas

A Unified Intermediate Representation and Execution Architecture for Heterogeneous and Distributed Neural Simulation

AgentGarten: Code Worlds for Evolving Agents

KernelLens: Automated Triton-to-C++ Compiler for Enterprise Inference Runtimes

KernelLens: Deploying Triton Kernels to C++ Inference Engines

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

Automated Instruction Encoding Synthesis for Modern GPU ISA Compression

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high performance computing on graphics processing units: hgpu.org

KernelOPT: Dispatch-Aware Agentic Search for GPU Kernel Optimization

Taming Bitwise Behavior in GPU Kernels with Tensor Core: Black-Box Reconstruction, Compiler Enforcement, and Static Verification

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

Fearless Concurrency on the GPU

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

Recent source codes

Brian2 Atlas

AgentGarten: Code Worlds for Evolving Agents

KernelLens: Automated Triton-to-C++ Compiler for Enterprise Inference Runtimes

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

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