high performance computing on graphics processing units: hgpu.org

hgpu.org » Linear Algebra

Development of a Restricted Additive Schwarz Preconditioner for Sparse Linear Systems on NVIDIA GPU

Hui Liu, Zhangxin Chen, Song Yu, Ben Hsieh, Lei Shao

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Sparse linear iterative solvers, Sparse matrix, Tesla C2050, Tesla C2070

July 11, 2014 by hgpu

Accelerating Preconditioned Iterative Linear Solvers on GPU

Hui Liu, Zhangxin Chen, Bo Yang

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Sparse linear iterative solvers, Sparse matrix, Tesla C2050, Tesla C2070

July 11, 2014 by hgpu

Improving Performance and Energy Consumption of Runtime Schedulers for Dense Linear Algebra

Pedro Alonso, Manuel F. Dolz, Francisco D. Igual, Rafael Mayo, Enrique S. Quintana-Orti

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Task scheduling, Tesla S1070, Tesla S2050

July 10, 2014 by hgpu

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Bartosz D. Wozniak

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Tags: CUBLAS, CUDA, Fluid dynamics, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Package, Python, Tesla K40, Thesis

July 7, 2014 by hgpu

Mixed-precision Orthogonalization Scheme and Adaptive Step Size for CA-GMRES on GPUs

Ichitaro Yamazaki, Stanimire Tomov, Tingxing Dong, Jack Dongarra

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Tags: Computer science, Linear Algebra, nVidia, Performance, Tesla K20, Tesla K40, Tesla M2090

July 1, 2014 by hgpu

Evaluation of DGEMM Implementation on Intel Xeon Phi Coprocessor

Pawel Gepner, Victor Gamayunov, David L. Fraser, Eric Houdard, Ludovic Sauge, Damien Declat, Mathieu Dubois

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Tags: Algorithms, Computer science, Intel Xeon Phi, Linear Algebra, Matrix multiplication, Performance

June 28, 2014 by hgpu

Computation on GPU of Eigenvalues and Eigenvectors of a Large Number of Small Hermitian Matrices

Alain Cosnuau

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Matrix decomposition, nVidia, Tesla M2070

June 27, 2014 by hgpu

Accelerating Band Linear Algebra Operations on GPUs with Application in Model Reduction

Peter Benner, Ernesto Dufrechou, Pablo Ezzatti, Pablo Igounet, Enrique S. Quintana-Orti, Alfredo Remon

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Tags: Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse matrix, Tesla S2070

June 23, 2014 by hgpu

An Out-of-core GPU Approach for Accelerating Geostatistical Interpolation

Victor Allombert, David Michea, Fabrice Dupros, Christian Bellier, Bernard Bourgine, Hideo Aochi, Sylvain Jubertie

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Tags: Algorithms, CUDA, Earth and Space Sciences, Geoscience, Linear Algebra, nVidia, nVidia Quadro K4000

June 18, 2014 by hgpu

Unified Development for Mixed Multi-GPU and Multi-Coprocessor Environments using a Lightweight Runtime Environment

Azzam Haidar, Chongxiao Cao, Asim YarKhan, Piotr Luszczek, Stanimire Tomov, Khairul Kabir, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, Linear Algebra, nVidia, Tesla K20, Tesla M2090

June 14, 2014 by hgpu

A Fast Batched Cholesky Factorization on a GPU

Tingxing Dong, Azzam Haidar, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Package, Tesla K40

June 12, 2014 by hgpu

An implementation of a reordering approach for increasing the product of diagonal entries in a sparse matrix

Ang Li, Radu Serban, Dan Negrut

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Package, Sparse matrix

June 1, 2014 by hgpu

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

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

Development of a Restricted Additive Schwarz Preconditioner for Sparse Linear Systems on NVIDIA GPU

Accelerating Preconditioned Iterative Linear Solvers on GPU

Improving Performance and Energy Consumption of Runtime Schedulers for Dense Linear Algebra

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Mixed-precision Orthogonalization Scheme and Adaptive Step Size for CA-GMRES on GPUs

Evaluation of DGEMM Implementation on Intel Xeon Phi Coprocessor

Computation on GPU of Eigenvalues and Eigenvectors of a Large Number of Small Hermitian Matrices

Accelerating Band Linear Algebra Operations on GPUs with Application in Model Reduction

An Out-of-core GPU Approach for Accelerating Geostatistical Interpolation

Unified Development for Mixed Multi-GPU and Multi-Coprocessor Environments using a Lightweight Runtime Environment

A Fast Batched Cholesky Factorization on a GPU

An implementation of a reordering approach for increasing the product of diagonal entries in a sparse matrix

Recent source codes

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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

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