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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

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

Daichi Mukunoki

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Tags: Computer science, Matrix multiplication, nVidia, nVidia GeForce RTX 5060 Ti, Package, Performance

August 10, 2025 by hgpu

Libra: Synergizing CUDA and Tensor Cores for High-Performance Sparse Matrix Multiplication

Jinliang Shi, Shigang Li, Youxuan Xu, Xueying Wang, Rongtian Fu, Zhi Ma, Tong Wu

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce RTX 4090, nVidia H100, Package, Sparse matrix

July 6, 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

WgPy: GPU-accelerated NumPy-like array library for web browsers

Masatoshi Hidaka, Tatsuya Harada

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce RTX 4070, Python

March 10, 2025 by hgpu

On the Partitioning of GPU Power among Multi-Instances

Tirth Vamja, Kaustabha Ray, Felix George, UmaMaheswari C Devi

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Tags: Computer science, CUDA, Energy-efficient computing, Machine learning, Matrix multiplication, nVidia, nVidia A100, nVidia V100, Performance

February 3, 2025 by hgpu

Utilizing Tensor Cores in Futhark

Kristoffer August Kortbæk, Rune Ejnar Bang Lejbølle

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, High-level Languages, Matrix multiplication, nVidia, nVidia A100, Package, PTX

December 24, 2024 by hgpu

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

Ezhilmathi Krishnasamy, Pascal Bouvry

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Tags: BLAS, Computer science, CUDA, Differential equations, HPC, Linear Algebra, Matrix multiplication, nVidia, nVidia A100, OpenACC, Package, Partial differential equations, PDEs, Performance

December 24, 2024 by hgpu

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

Xiaoteng (Frank) Liu, Pavly Halim

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Tags: Computer science, CUDA, Energy-efficient computing, GEMM, Machine learning, Matrix multiplication, nVidia, nVidia GeForce RTX 4070, Package, Performance

December 1, 2024 by hgpu

Improving Parallel Program Performance Through DSL-Driven Code Generation with LLM Optimizers

Anjiang Wei, Allen Nie, Thiago S. F. X. Teixeira, Rohan Yadav, Wonchan Lee, Ke Wang, Alex Aiken

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Tags: Code generation, Computer science, DSL, LLM, Matrix multiplication, nVidia, Optimization, Tesla P100

November 17, 2024 by hgpu

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

Matteo Croci, Garth N. Wells

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Tags: AVX, Computer science, Finite element method, Floating point error, Intel, Matrix multiplication, Mixed precision, Package

October 27, 2024 by hgpu

Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores

Shaobo Ma, Chao Fang, Haikuo Shao, Zhongfeng Wang

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Tags: Computer science, CUDA, Data recovery, Extended precision, LLM, Matrix multiplication, nVidia, nVidia GeForce RTX 3090, Security

October 6, 2024 by hgpu

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

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

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

HPC Benchmark Survey

An HPC Benchmark Survey and Taxonomy for Characterization

HDM: Home made Diffusion Models

Home-made Diffusion Model from Scratch to Hatch

General Matrix Multiplication (GEMM)

High Performance Matrix Multiplication

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high performance computing on graphics processing units: hgpu.org

High Performance Matrix Multiplication

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

Libra: Synergizing CUDA and Tensor Cores for High-Performance Sparse Matrix Multiplication

A Novel Compiler Transformation for Fast Sparse Matrix Multiplication in GPUs

WgPy: GPU-accelerated NumPy-like array library for web browsers

On the Partitioning of GPU Power among Multi-Instances

Utilizing Tensor Cores in Futhark

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

Improving Parallel Program Performance Through DSL-Driven Code Generation with LLM Optimizers

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores

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

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