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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

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

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

Fast and Practical Strassen’s Matrix Multiplication using FPGAs

Afzal Ahmad, Linfeng Du, Wei Zhang

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Tags: BLAS, Computer science, FPGA, GEMM, Linear Algebra, Machine learning, Matrix multiplication, OpenCL, Package

June 9, 2024 by hgpu

Automatic BLAS Offloading on Unified Memory Architecture: A Study on NVIDIA Grace-Hopper

Junjie Li, Yinzhi Wang, Xiao Liang, Hang Liu

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Tags: BLAS, Chemistry, CUDA, nVidia, nVidia GH200, nVidia H100, Performance, Physics

May 5, 2024 by hgpu

Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU

Jou-An Chen, Hsin-Hsuan Sung, Nathan Tallent, Kevin Barker, Xipeng Shen, Ang Li

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Tags: BLAS, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan V, Package, Sparse

January 30, 2022 by hgpu

Parallel time integration using Batched BLAS (Basic Linear Algebra Subprograms) routines

Konstantin Herb, Pol Welter

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Tags: BLAS, Computational Physics, Computer science, CUDA, Numerical Analysis, nVidia, Package, Tesla V100

August 22, 2021 by hgpu

FBLAS: Streaming Linear Algebra Kernels on FPGA

Tiziano De Matteis, Johannes de Fine Licht, Torsten Hoefler

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Tags: BLAS, Computer science, FPGA, Linear Algebra, OpenCL, Package

December 1, 2019 by hgpu

Out-of-core singular value decomposition

Vadim Demchik, Miroslav Bačák, Stefan Bordag

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Tags: BLAS, Computer science, Mathematical Software, Numerical Analysis, Out of core, Sparse matrix

July 16, 2019 by hgpu

Cross-Platform Performance Portability Using Highly Parametrized SYCL Kernels

John Lawson, Mehdi Goli, Duncan McBain, Daniel Soutar, Louis Sugy

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Tags: AMD R9 Nano, ATI, BLAS, Computer science, Deep learning, Linear Algebra, Machine learning, Mathematical Software, OpenCL, Package, Performance, performance portability, SYCL

April 14, 2019 by hgpu

Implementing Push-Pull Efficiently in GraphBLAS

Carl Yang, Aydin Buluc, John D. Owens

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Tags: Algorithm optimization, Algorithms, BLAS, Computer science, CUDA, Graph theory, Linear Algebra, nVidia, Sparse matrix, Tesla K40

April 15, 2018 by hgpu

Investigating Half Precision Arithmetic to Accelerate Dense Linear System Solvers

Azzam Haidar, Panruo Wu, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Artificial intelligence, BLAS, Computer science, Linar Algebra, Mixed precision, Neural networks, nVidia, Tesla P100

December 10, 2017 by hgpu

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

LC Framework

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

pplx-garden: Perplexity open source garden for inference technology

RDMA Point-to-Point Communication for LLM Systems

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high performance computing on graphics processing units: hgpu.org

High Performance Matrix Multiplication

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

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

Fast and Practical Strassen’s Matrix Multiplication using FPGAs

Automatic BLAS Offloading on Unified Memory Architecture: A Study on NVIDIA Grace-Hopper

Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU

Parallel time integration using Batched BLAS (Basic Linear Algebra Subprograms) routines

FBLAS: Streaming Linear Algebra Kernels on FPGA

Out-of-core singular value decomposition

Cross-Platform Performance Portability Using Highly Parametrized SYCL Kernels

Implementing Push-Pull Efficiently in GraphBLAS

Investigating Half Precision Arithmetic to Accelerate Dense Linear System Solvers

Recent source codes

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

LC Framework

pplx-garden: Perplexity open source garden for inference technology

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