GPGPU-based parallel computing applied in the FEM using the conjugate gradient algorithm: a review
DOI10.1007/s12046-018-0892-0zbMath1397.65337OpenAlexW2808764408MaRDI QIDQ1789172
Arvind Y. Vyavhare, Shailesh R. Sathe, Nileshchandra K. Pikle
Publication date: 10 October 2018
Published in: Sādhanā (Search for Journal in Brave)
Full work available at URL: https://www.ias.ac.in/describe/article/sadh/043/07/0111
parallel computingcompute unified device architecture (CUDA)graphics processing units (GPUs)assembly-free FEM (AF-FEM)conjugate gradient (CG)finite-element method (FEM)sparse matrix-vector multiplication (SpMV)
Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30) Research exposition (monographs, survey articles) pertaining to numerical analysis (65-02) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60) Parallel numerical computation (65Y05)
Uses Software
Cites Work
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- Vectorized OpenCL implementation of numerical integration for higher order finite elements
- Numerical integration on GPUs for higher order finite elements
- Parallel preconditioned conjugate gradient algorithm on GPU
- Accelerating scientific computations with mixed precision algorithms
- Automated solution of differential equations by the finite element method. The FEniCS book
- Finite element mesh generation methods: A review and classification
- An element-by-element solution algorithm for problems of structural and solid mechanics
- Explicit approximate inverse preconditioning techniques
- Architecting the finite element method pipeline for the GPU
- Large-scale robust topology optimization using multi-GPU systems
- Finite element numerical integration for first order approximations on multi- and many-core architectures
- A Unified Sparse Matrix Data Format for Efficient General Sparse Matrix-Vector Multiplication on Modern Processors with Wide SIMD Units
- Generation of large finite-element matrices on multiple graphics processors
- Assembly of finite element methods on graphics processors
- Sparse Matrix-Vector Multiplication on GPGPUs
- Finite Element Integration on GPUs
- A Factored Sparse Approximate Inverse Preconditioned Conjugate Gradient Solver on Graphics Processing Units
- Element-by-element linear and nonlinear solution schemes
- Element‐by‐element vector and parallel computations
- Approximate inverse preconditionings for sparse linear systems
- Parallel Preconditioning with Sparse Approximate Inverses
- Fixed grid finite elements in elasticity problems
- A Sparse Approximate Inverse Preconditioner for the Conjugate Gradient Method
- Finite element assembly strategies on multi‐core and many‐core architectures
- Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations
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