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Lessons learned from contrasting a BLAS kernel implementations

Andres More

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Tags: BLAS, Computer science, CUDA, nVidia, nVidia Quadro FX 770 M, Performance

December 12, 2013 by hgpu

Towards a functional run-time for dense NLA domain

Mauro Blanco, Pablo Perdomo, Pablo Ezzatti, Alberto Pardo, Marcos Viera

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Tags: Algorithms, BLAS, Computer science, CUDA, Factorization, Haskell, Linear Algebra, nVidia, nVidia GeForce GTX 570

September 2, 2013 by hgpu

Optimizing CUDA Code By Kernel Fusion – Application on BLAS

Jiri Filipovic, Matus Madzin, Jan Fousek, Ludek Matyska

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Tags: BLAS, Code generation, Computer science, CUBLAS, CUDA, nVidia, nVidia GeForce GTX 480

May 7, 2013 by hgpu

Improving Numerical Accuracy for Non-Negative Matrix Multiplication on GPUs using Recursive Algorithms

Matthew Badin, Paolo D'Alberto, Lubomir Bic, Michael Dillencourt, Alexandru Nicolau

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Tags: Algorithms, BLAS, Computer science, CUDA, Matrix multiplication, nVidia, Performance, Tesla C2070

April 30, 2013 by hgpu

Batched Kronecker product for 2-D matrices and 3-D arrays on NVIDIA GPUs

Chetan Jhurani

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Tags: BLAS, CUBLAS, CUDA, Kronecker product, nVidia, Tesla K20

April 10, 2013 by chetan.jhurani

A GEMM interface and implementation on NVIDIA GPUs for multiple small matrices

Chetan Jhurani, Paul Mullowney

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Tags: BLAS, CUBLAS, CUDA, Dense linear algebra, GEMM, Linear Algebra, nVidia, Parallel programming, Tesla K20

April 9, 2013 by chetan.jhurani

Auto-tuning Dense Vector and Matrix-Vector Operations for Fermi GPUs

Hans Henrik Brandenborg Sorensen

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Tags: BLAS, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C2050

April 18, 2012 by hgpu

High-Performance Matrix-Vector Multiplication on the GPU

Hans Henrik Brandenborg Sorensen

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Tags: BLAS, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C2050

April 18, 2012 by hgpu

Auto-Tuning of Level 1 and Level 2 BLAS for GPUs

Hans Henrik Brandenborg Sorensen

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

April 16, 2012 by hgpu

phiGEMM: a CPU-GPU library for porting Quantum ESPRESSO on hybrid systems

Filippo Spiga, Ivan Girotto

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Source codes

Tags: Benchmarking, BLAS, Computer science, CUDA, FFT, Matrix multiplication, nVidia, Package, Tesla C2050

April 14, 2012 by hgpu

Autotuning GEMMs for Fermi

Jakub Kurzak, Stanimire Tomov, Jack Dongarra

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Tags: Benchmarking, BLAS, Computer science, CUDA, Linear Algebra, Mathematical Software, Matrix multiplication, nVidia, Package, Tesla S2050

November 20, 2011 by hgpu

Hierarchical QR factorization algorithms for multi-core cluster systems

Jack Dongarra, Mathieu Faverge, Thomas Herault, Julien Langou, Yves Robert

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Tags: Algorithms, BLAS, Computer science, Factorization, Linear Algebra

November 20, 2011 by hgpu

Over-synchronization in GPU Programs

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Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

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Mixed-precision finite element kernels

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

SHMT: The Simultaneous and Heterogenous Multithreading project

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high performance computing on graphics processing units: hgpu.org

Lessons learned from contrasting a BLAS kernel implementations

Towards a functional run-time for dense NLA domain

Optimizing CUDA Code By Kernel Fusion – Application on BLAS

Improving Numerical Accuracy for Non-Negative Matrix Multiplication on GPUs using Recursive Algorithms

Batched Kronecker product for 2-D matrices and 3-D arrays on NVIDIA GPUs

A GEMM interface and implementation on NVIDIA GPUs for multiple small matrices

Auto-tuning Dense Vector and Matrix-Vector Operations for Fermi GPUs

High-Performance Matrix-Vector Multiplication on the GPU

Auto-Tuning of Level 1 and Level 2 BLAS for GPUs

phiGEMM: a CPU-GPU library for porting Quantum ESPRESSO on hybrid systems

Autotuning GEMMs for Fermi

Hierarchical QR factorization algorithms for multi-core cluster systems

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Over-synchronization in GPU Programs

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