high performance computing on graphics processing units: hgpu.org

hgpu.org » Linear Algebra

Pretty Good Accuracy in Matrix Multiplication with GPUs

Matthew Badin, Lubomir Bic, Michael Dillencourt, Alexandru Nicolau

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 285

April 21, 2011 by hgpu

Using graphics processors to accelerate the computation of the matrix inverse

P. Ezzatti, E. Quintana-Orti, A. Remon

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Tags: Computer science, Linear Algebra, Matrix inversion

April 21, 2011 by hgpu

Automatically generating and tuning GPU code for sparse matrix-vector multiplication from a high-level representation

Dominik Grewe, Anton Lokhmotov

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Tags: ATI, ATI Radeon HD 5970, Code generation, Computer science, CUDA, High-level Languages, Linear Algebra, nVidia, OpenCL, Sparse matrix, Tesla S1070

April 21, 2011 by hgpu

Auto-Tuning CUDA Parameters for Sparse Matrix-Vector Multiplication on GPUs

Ping Guo, Liqiang Wang

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce 9500 GTX, nVidia GeForce GTX 295, Sparse matrix

April 21, 2011 by hgpu

Accelerating Linpack Performance with Mixed Precision Algorithm on CPU+GPGPU Heterogeneous Cluster

Wang Lei, Zhang Yunquan, Zhang Xianyi, Liu Fangfang

Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, Linear Algebra, Mixed precision, nVidia, nVidia GeForce GTX 295, Tesla C1060

April 20, 2011 by hgpu

Implementation of Jacobi iterative method on graphics processor unit

Tao Wang, Yuan Yao, Lin Han, Dan Zhang, Yuanyuan Zhang

Tags: Computer science, CUDA, Linear Algebra, nVidia

April 15, 2011 by hgpu

CULA: hybrid GPU accelerated linear algebra routines

John R. Humphrey, Daniel K. Price, Kyle E. Spagnoli, Aaron L. Paolini, Eric J. Kelmelis

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Tags: Computer science, CUDA, CULA, Linear Algebra, nVidia, Package, Tesla C1060

April 11, 2011 by hgpu

A Light-weight API for Portable Multicore Programming

Christopher G. Baker, Michael A. Heroux, H. Carter Edwards, Alan B. Williams

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280, OpenCL, RapidMind

April 3, 2011 by hgpu

Acceleration of Hessenberg Reduction for Nonsymmetric Eigenvalue Problems Using GPU

Jun-ichi Muramatsu, Shao-Liang Zhang, Yusaku Yamamoto

Tags: BLAS, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C1060

March 28, 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

A Fast GEMM Implementation On a Cypress GPU

Naohito Nakasato

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Tags: ATI, ATI CAL, ATI IL, ATI Radeon HD 5870, ATI Stream, Computer science, Linear Algebra

March 18, 2011 by hgpu

MSCCL++: A GPU-driven communication stack for scalable AI applications

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

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high performance computing on graphics processing units: hgpu.org

Pretty Good Accuracy in Matrix Multiplication with GPUs

Using graphics processors to accelerate the computation of the matrix inverse

Automatically generating and tuning GPU code for sparse matrix-vector multiplication from a high-level representation

Auto-Tuning CUDA Parameters for Sparse Matrix-Vector Multiplication on GPUs

Accelerating Linpack Performance with Mixed Precision Algorithm on CPU+GPGPU Heterogeneous Cluster

Implementation of Jacobi iterative method on graphics processor unit

CULA: hybrid GPU accelerated linear algebra routines

A Light-weight API for Portable Multicore Programming

Acceleration of Hessenberg Reduction for Nonsymmetric Eigenvalue Problems Using GPU

Dense linear algebra solvers for multicore with GPU accelerators

Improving accuracy for matrix multiplications on GPUs

A Fast GEMM Implementation On a Cypress GPU

Recent source codes

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

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