high performance computing on graphics processing units: hgpu.org

hgpu.org » Conjugate gradient solver

Preconditioned conjugate gradient solver for structural problems

Xiangge Li

View

Download (PDF)

Tags: Computer science, Conjugate gradient solver, CUDA, nVidia, Physics, Tesla M2070

September 20, 2013 by hgpu

Efficient CUDA polynomial preconditioned Conjugate Gradient solver for Finite Element computation of elasticity problems

Jianfei Zhang, Lei Zhang

View

Download (PDF)

Tags: Algorithms, Computer science, Conjugate gradient solver, CUDA, Mixed precision, nVidia, nVidia GeForce GT 430, Sparse matrix

September 14, 2013 by hgpu

Parallel Implementation of Finite Element Codes using CUDA

Shi-Lun Huang

View

Download (PDF)

Tags: Computer science, Conjugate gradient solver, CUDA, FEM, Finite element method, nVidia, nVidia GeForce GTS 450, nVidia GeForce GTX 560

June 6, 2013 by hgpu

Matrix-free GPU implementation of a preconditioned conjugate gradient solver for anisotropic elliptic PDEs

Eike Mueller, Xu Guo, Robert Scheichl, Sinan Shi

View

Download (PDF)

Tags: Algorithms, Computer science, Conjugate gradient solver, CUDA, Differential equations, Numerical Analysis, nVidia, Partial differential equations, PDEs, Tesla M2090

March 2, 2013 by hgpu

GPU Implementation of Iterative Solvers in Numerical Weather Predicting Models

Sinan Shi

View

Download (PDF)

Tags: Computer science, Conjugate gradient solver, CUDA, nVidia, Tesla C2050, Thesis, Weather Prediction Model

February 7, 2013 by hgpu

Task-based Conjugate-Gradient for multi-GPUs platforms

Emmanuel Agullo, Luc Giraud, Abdou Guermouche, Stojce Nakov, Jean Roman

View

Download (PDF)

Tags: Algorithms, Computer science, Conjugate gradient solver, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C2070

December 25, 2012 by hgpu

Analysis and performance estimation of the conjugate gradient method on multiple GPUs

Mickeal Verschoor, Andrei C. Jalba

View

Download (PDF)

Tags: Computer science, Conjugate gradient solver, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 570, Performance, Sparse matrix

August 4, 2012 by hgpu

Performance Gains in Conjugate Gradient Computation with Linearly Connected GPU Multiprocessors

Stephen J. Tarsa, Tsung-han Lin, H.T. Kung

View

Download (PDF)

Tags: Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce GTX 570

June 14, 2012 by hgpu

GPU-Accelerated Preconditioned Iterative Linear Solvers

Ruipeng Li, Yousef Saad

View

Download (PDF)

Tags: Computer science, Conjugate gradient solver, CUDA, Factorization, Linear Algebra, nVidia, Overview, Sparse matrix, Tesla C1060

December 19, 2011 by hgpu

Sparse-Matrix-CG-Solver in CUDA

Dominik Michels

View

Download (PDF)

Tags: Algorithms, Computer science, Conjugate gradient solver, CUDA, nVidia, nVidia GeForce GTX 480, Sparse matrix

December 7, 2011 by hgpu

Efficient Two-Level Preconditionined Conjugate Gradient Method on the GPU

Rohit Gupta, Martin B. van Gijzen, Kees Vuik

View

Download (PDF)

Tags: Algorithms, Conjugate gradient solver, CUDA, Mathematics, nVidia, Tesla C2070

December 7, 2011 by hgpu

Iterative Solution of Linear Systems in Electromagnetics (and not only): Experiences with CUDA

Danilo De Donno, Alessandra Esposito, Giuseppina Monti, Luciano Tarricone

View

Download (PDF)

Tags: Conjugate gradient solver, CUDA, Electrodynamics, nVidia, nVidia GeForce GTX 260

November 26, 2011 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Preconditioned conjugate gradient solver for structural problems

Efficient CUDA polynomial preconditioned Conjugate Gradient solver for Finite Element computation of elasticity problems

Parallel Implementation of Finite Element Codes using CUDA

Matrix-free GPU implementation of a preconditioned conjugate gradient solver for anisotropic elliptic PDEs

GPU Implementation of Iterative Solvers in Numerical Weather Predicting Models

Task-based Conjugate-Gradient for multi-GPUs platforms

Analysis and performance estimation of the conjugate gradient method on multiple GPUs

Performance Gains in Conjugate Gradient Computation with Linearly Connected GPU Multiprocessors

GPU-Accelerated Preconditioned Iterative Linear Solvers

Sparse-Matrix-CG-Solver in CUDA

Efficient Two-Level Preconditionined Conjugate Gradient Method on the GPU

Iterative Solution of Linear Systems in Electromagnetics (and not only): Experiences with CUDA

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Most viewed papers (last 30 days)