high performance computing on graphics processing units: hgpu.org

hgpu.org » Linear Algebra

Accelerating Krylov Subspace Solvers on Graphics Processing Units

Hartwig Anzt, Stanimire Tomov, Piotr Luszczek, Ichitaro Yamazaki, Jack Dongarra, William Sawyer

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla K20

August 3, 2014 by hgpu

Extending Lyapack for the Solution of Band Lyapunov Equations on Hybrid CPU-GPU Platforms

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

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Tags: CUDA, Linear Algebra, Mathematics, nVidia, Tesla K20, Tesla S2070

August 3, 2014 by hgpu

A Scalable Approach to Solving Dense Linear Algebra Problems on Hybrid CPU-GPU Systems

Fengguang Song, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, Tesla K20

August 1, 2014 by hgpu

Abstraction and Implementation of Unstructured Grid Algorithms on Massively Parallel Heterogeneous Architectures

Istvan Zoltan Reguly

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Tags: Algorithms, Code generation, Computer science, CUDA, Differential equations, FEM, Finite element method, Finite volume method, Fluid dynamics, Heterogeneous systems, Linear Algebra, nVidia, Partial differential equations, PDEs, Programming techniques, Tesla C2070, Tesla K20, Tesla K40, Thesis

July 24, 2014 by hgpu

A Study on GPU Computing and Accelerating Simulation of Sedimentary Rock Structure

Koushik Mandal

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, Tesla C2075, Thesis

July 23, 2014 by hgpu

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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Source codes

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

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

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

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

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

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Accelerating Krylov Subspace Solvers on Graphics Processing Units

Extending Lyapack for the Solution of Band Lyapunov Equations on Hybrid CPU-GPU Platforms

A Scalable Approach to Solving Dense Linear Algebra Problems on Hybrid CPU-GPU Systems

Abstraction and Implementation of Unstructured Grid Algorithms on Massively Parallel Heterogeneous Architectures

A Study on GPU Computing and Accelerating Simulation of Sedimentary Rock Structure

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

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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