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AMGCL – A C++ library for efficient solution of large sparse linear systems

Denis Demidov

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Tags: Computer science, CUDA, Mathematical Software, MPI, OpenCL, Package, Sparse, Sparse linear iterative solvers

November 8, 2020 by hgpu

Development of Krylov and AMG linear solvers for large-scale sparse matrices on GPUs

Bo Yang, Hui Liu, Zhangxin Chen

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

June 7, 2016 by hgpu

On the design of sparse hybrid linear solvers for modern parallel architectures

Stojce Nakov

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Linear Algebra, MPI, nVidia, Sparse linear iterative solvers, Sparse matrix, Tesla K40, Tesla M2070, Thesis

April 29, 2016 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

Sparse Approximate Inverse Preconditioners for Iterative Solvers on GPUs

Mykola Lukash, Karl Rupp, Siegfried Selberherr

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Tags: Computer science, nVidia, nVidia GeForce GTX 580, OpenCL, Sparse linear iterative solvers

July 22, 2012 by hgpu

Analysis and optimization of power consumption in the iterative solution of sparse linear systems on multi-core and many-core platforms

Hartwig Anzt, Vincent Heuveline, Jose I. Aliaga, Maribel Castillo, Juan C. Fernandez, Rafael Mayo, Enrique S. Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, nVidia, Optimization, Sparse linear iterative solvers, Tesla C1060

January 11, 2012 by hgpu

Linear Solvers for Stable Fluids: GPU vs CPU

Goncalo Amador, Abel Gomes

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Tags: Algorithms, CUDA, Fluid dynamics, Fluid simulation, nVidia, nVidia GeForce 8800 GT, Sparse linear iterative solvers

January 2, 2012 by hgpu

On the GPGPU parallelization issues of finite element approximate inverse preconditioning

C. K. Filelis-Papadopoulos, G. A. Gravvanis, P. I. Matskanidis, K. M. Giannoutakis

Tags: Algorithms, Computer science, CUDA, Finite element method, nVidia, Sparse linear iterative solvers

August 8, 2011 by hgpu

Potential of General Purpose Graphic Processing Unit for Energy Management System

Jean-Charles Tournier, Vaibhav Donde, Zhao Li

Tags: Computational Engineering, Computer science, Power Systems Simulation, Sparse linear iterative solvers

June 16, 2011 by hgpu

Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations (Part 2: Double Precision GPUs)

Dominik Goddeke and Robert Strzodka

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Tags: Computer science, Conjugate gradient solver, CUDA, Extended precision, Mixed precision, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Partial differential equations, PDEs, Sparse linear iterative solvers

March 2, 2011 by hgpu

Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations

Dominik Goddeke, Robert Strzodka, Stefan Turek

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Tags: Cg, Computer science, Conjugate gradient solver, Mixed precision, nVidia, nVidia GeForce 7800 GTX, nVidia Quadro FX 4500, Sparse linear iterative solvers

March 1, 2011 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

AMGCL – A C++ library for efficient solution of large sparse linear systems

Development of Krylov and AMG linear solvers for large-scale sparse matrices on GPUs

On the design of sparse hybrid linear solvers for modern parallel architectures

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

Accelerating Preconditioned Iterative Linear Solvers on GPU

Sparse Approximate Inverse Preconditioners for Iterative Solvers on GPUs

Analysis and optimization of power consumption in the iterative solution of sparse linear systems on multi-core and many-core platforms

Linear Solvers for Stable Fluids: GPU vs CPU

On the GPGPU parallelization issues of finite element approximate inverse preconditioning

Potential of General Purpose Graphic Processing Unit for Energy Management System

Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations (Part 2: Double Precision GPUs)

Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations

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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