high performance computing on graphics processing units: hgpu.org

hgpu.org » Linear Algebra

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

Modular Arithmetic for Solving Linear Equations on the GPU

J. Hladik, I. Simecek

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Tags: Algorithms, Linear Algebra, Mathematics, Modular arithmetic, nVidia, OpenCL

April 11, 2012 by hgpu

New Basic Linear Algebra Methods for Simulation on GPUs

Jian Shi, Yiwei Zhang, Blake langland, Jijun Tang, Roger Dougal

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Tesla T10

April 9, 2012 by hgpu

A Scalable Framework for Heterogeneous GPU-Based Clusters

Fengguang Song, Jack Dongarra, Stanimire Tomov

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Tags: Algorithms, Computer science, CUDA, Factorization, GPU cluster, Heterogeneous systems, Linear Algebra, MPI, nVidia, Tesla M2070

April 7, 2012 by hgpu

Novel GPU Implementation of Jacobi Algorithm for Karhunen-Loeve Transform of Dense Matrices

Mustafa U. Torun, Onur Yilmaz, Ali N. Akansu

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Tesla C2070

April 4, 2012 by hgpu

CUDA 2D Stencil Computations for the Jacobi Method

Jose Maria Cecilia, Jose Manuel Garcia, Manuel Ujaldon

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Differential equations, Linear Algebra, nVidia, Optimization, Tesla C1060, Tesla C870

March 16, 2012 by hgpu

Parallel Sparse Linear Algebra for Multi-core and Many-core Platforms: Parallel Solvers and Preconditioners

Dimitar Lukarski

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Tags: Algorithms, Computer science, CUDA, Differential equations, Factorization, FEM, Finite difference, Finite element method, Linear Algebra, nVidia, nVidia GeForce GTX 580, Partial differential equations, PDEs, Sparse matrix, Tesla M1060, Tesla S1070, Thesis

March 16, 2012 by hgpu

Multi-platform Linear Algebra

E. Ketelaer, D. Lukarski

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Tags: Computer science, CUDA, Differential equations, Distributed data structures, Linear Algebra, nVidia, Package, Partial differential equations, PDEs

March 16, 2012 by hgpu

Advancing Large Scale Many-Body QMC Simulations on GPU Accelerated Multicore Systems

Andres Tomas, Chia-Chen Chang, Richard Scalettar, Zhaojun Bai

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Tags: Computer science, CUDA, Linear Algebra, Monte Carlo simulation, nVidia, QMC, Tesla C2050

February 15, 2012 by hgpu

Automatic Performance Optimization in ViennaCL for GPUs

Karl Rupp, Josef Weinbub, Florian Rudolf

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Tags: Algorithms, ATI, ATI Radeon HD 5850, Computer science, Linear Algebra, nVidia, nVidia GeForce 8500 GT, nVidia GeForce GTX 470, OpenCL, Optimization, Package, Performance, Programming techniques

February 10, 2012 by hgpu

Parallel Semi-Implicit Time Integrators

B. Ong, A. Melfi, A. Christlieb

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Partial differential equations, PDEs, Tesla C1060

February 9, 2012 by hgpu

CUDA Expression Templates

Paul Wiemann, Stephan Wenger, Marcus Magnor

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTS 250, Package

January 30, 2012 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

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

Modular Arithmetic for Solving Linear Equations on the GPU

New Basic Linear Algebra Methods for Simulation on GPUs

A Scalable Framework for Heterogeneous GPU-Based Clusters

Novel GPU Implementation of Jacobi Algorithm for Karhunen-Loeve Transform of Dense Matrices

CUDA 2D Stencil Computations for the Jacobi Method

Parallel Sparse Linear Algebra for Multi-core and Many-core Platforms: Parallel Solvers and Preconditioners

Multi-platform Linear Algebra

Advancing Large Scale Many-Body QMC Simulations on GPU Accelerated Multicore Systems

Automatic Performance Optimization in ViennaCL for GPUs

Parallel Semi-Implicit Time Integrators

CUDA Expression Templates

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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