high performance computing on graphics processing units: hgpu.org

hgpu.org » Linear Algebra

Using Mixed Precision for Sparse Matrix Computations to Enhance the Performance while Achieving 64-bit Accuracy

Alfredo Buttari, Jack Dongarra, Jakub Kurzak, Piotr Luszczek, Stanimir Tomov

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Tags: Algorithms, Computer science, Linear Algebra, Mixed precision, Sparse matrix

December 12, 2010 by hgpu

BLAS Comparison on FPGA, CPU and GPU

Srinidhi Kestur, John D. Davis, Oliver Williams

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Tags: BLAS, Computer science, CUBLAS, CUDA, Energy-efficient computing, FPGA, Linear Algebra, nVidia, nVidia GeForce 9500 GT, Performance, Tesla C1060

December 6, 2010 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

LU-GPU: Efficient Algorithms for Solving Dense Linear Systems on Graphics Hardware

Nico Galoppo, Naga K. Govindaraju, Michael Henson, Dinesh Manocha

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Tags: Computer science, Fluid simulation, Linear Algebra, nVidia, nVidia GeForce 6800 GT, nVidia GeForce 6800 Ultra, nVidia GeForce 7800

November 29, 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

Numerical linear algebra on emerging architectures: The PLASMA and MAGMA projects

Emmanuel Agullo, Jim Demmel, Jack Dongarra, Bilel Hadri, Jakub Kurzak, Julien Langou, Hatem Ltaief, Piotr Luszczek, Stanimire Tomov

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Tags: Algorithms, Computer science, Linear Algebra, Matrix multiplication

November 27, 2010 by hgpu

Triangular matrix inversion on Graphics Processing Unit

Florian Ries, Tommaso De Marco, Matteo Zivieri, Roberto Guerrieri

Tags: Computer science, CUDA, Linear Algebra, Matrix inversion, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295

November 26, 2010 by hgpu

Fast matrix multiplies using graphics hardware

E. Scott Larsen, David Mcallister

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Tags: Computer science, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce 3, OpenGL

November 24, 2010 by hgpu

GPU computing with Kaczmarz’s and other iterative algorithms for linear systems

Joseph M. Elble, Nikolaos V. Sahinidis, Panagiotis Vouzis

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Tags: CUDA, Differential equations, Fluid dynamics, Linear Algebra, nVidia, Tesla C870

November 22, 2010 by hgpu

Accelerating the reduction to upper Hessenberg, tridiagonal, and bidiagonal forms through hybrid GPU-based computing

Stanimire Tomov, Rajib Nath, Jack Dongarra

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Source codes

Tags: BLAS, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280, Package

November 17, 2010 by hgpu

Parallel GPU Implementation of Iterative PCA Algorithms

M. Andrecut

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Tags: Algorithms, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280

November 13, 2010 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

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

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Using Mixed Precision for Sparse Matrix Computations to Enhance the Performance while Achieving 64-bit Accuracy

BLAS Comparison on FPGA, CPU and GPU

Automatic tuning matrix multiplication performance on graphics hardware

LU-GPU: Efficient Algorithms for Solving Dense Linear Systems on Graphics Hardware

Fast Conjugate Gradients with Multiple GPUs

A Note on Auto-tuning GEMM for GPUs

Numerical linear algebra on emerging architectures: The PLASMA and MAGMA projects

Triangular matrix inversion on Graphics Processing Unit

Fast matrix multiplies using graphics hardware

GPU computing with Kaczmarz’s and other iterative algorithms for linear systems

Accelerating the reduction to upper Hessenberg, tridiagonal, and bidiagonal forms through hybrid GPU-based computing

Parallel GPU Implementation of Iterative PCA Algorithms

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

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

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