high performance computing on graphics processing units: hgpu.org

hgpu.org » Matrix multiplication

Scaleable Sparse Matrix-Vector Multiplication with Functional Memory and GPUs

Noboru Tanabe, Yuuka Ogawa, Masami Takata, Kazuki Joe

Tags: Computer science, Heterogeneous systems, Matrix multiplication, Sparse matrix

April 14, 2011 by hgpu

Speeding up K-Means Algorithm by GPUs

You Li, Kaiyong Zhao, Xiaowen Chu, Jiming Liu

Tags: Algorithms, Computer science, Matrix multiplication, Optimization

April 13, 2011 by hgpu

Dense linear algebra solvers for multicore with GPU accelerators

Stanimire Tomov, Rajib Nath, Hatem Ltaief, Jack Dongarra

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Tags: Algorithms, Computer science, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 280, Package

March 23, 2011 by hgpu

Improving accuracy for matrix multiplications on GPUs

Matthew Badin, Lubomir Bic, Michael Dillencourt, Alexandru Nicolau

Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, Performance

March 22, 2011 by hgpu

Dense Matrix Algebra on the GPU

Adam Moravanszky

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Tags: ATI, ATI Radeon 9500 Pro, ATI Radeon 9700 Pro, Computer science, DirectX, Linear Algebra, Matrix multiplication

February 24, 2011 by hgpu

Evaluating one-sided programming models for GPU cluster computations

Jeff R. Hammond and A. Eugene DePrince III

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Tags: Chemistry, Computer science, CUDA, GPU cluster, Matrix multiplication, nVidia, Tesla T10

February 17, 2011 by hgpu

Static Memory Access Pattern Analysis on a Massively Parallel GPU

Byunghyun Jang, Dana Schaa, Perhaad Mistry, and David Kaeli

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Tags: Computer science, Matrix multiplication, Memory model, nVidia, nVidia GeForce 8800 Ultra, nVidia GeForce GTX 285, OpenCL, Optimization

February 16, 2011 by hgpu

Efficient Sparse Matrix-Vector Multiplication on CUDA

Nathan Bell and Michael Garland

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 280, Sparse matrix

January 24, 2011 by hgpu

An Improved Magma Gemm For Fermi Graphics Processing Units

Rajib Nath, Stanimire Tomov, Jack Dongarra

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Tags: BLAS, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, Package, Tesla C2050

January 23, 2011 by hgpu

Automatic tuning matrix multiplication performance on graphics hardware

Changhao Jiang, M. Snir

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Tags: Brook, Computer science, DirectX, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce 6800 GT, nVidia GeForce 6800 Ultra, nVidia GeForce FX 5800 Ultra, nVidia Quadro FX 3400, OpenGL

November 30, 2010 by hgpu

Fast Conjugate Gradients with Multiple GPUs

Ali Cevahir, Akira Nukada, Satoshi Matsuoka

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Tags: Computer science, Conjugate gradient solver, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce 8800 GTS

November 27, 2010 by hgpu

A Note on Auto-tuning GEMM for GPUs

Yinan Li, Jack Dongarra, Stanimire Tomov

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 280

November 27, 2010 by hgpu

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

pplx-garden: Perplexity open source garden for inference technology

RDMA Point-to-Point Communication for LLM Systems

LC Framework

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

OpScanner

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

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high performance computing on graphics processing units: hgpu.org

Scaleable Sparse Matrix-Vector Multiplication with Functional Memory and GPUs

Speeding up K-Means Algorithm by GPUs

Dense linear algebra solvers for multicore with GPU accelerators

Improving accuracy for matrix multiplications on GPUs

Dense Matrix Algebra on the GPU

Evaluating one-sided programming models for GPU cluster computations

Static Memory Access Pattern Analysis on a Massively Parallel GPU

Efficient Sparse Matrix-Vector Multiplication on CUDA

An Improved Magma Gemm For Fermi Graphics Processing Units

Automatic tuning matrix multiplication performance on graphics hardware

Fast Conjugate Gradients with Multiple GPUs

A Note on Auto-tuning GEMM for GPUs

Recent source codes

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

pplx-garden: Perplexity open source garden for inference technology

LC Framework

OpScanner

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

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