Autotuning in high-performance computing applications
Autotuning refers to the automatic generation of a search space of possible implementations
of a computation that are evaluated through models and/or empirical measurement to …
of a computation that are evaluated through models and/or empirical measurement to …
Preconditioned krylov solvers on GPUs
In this paper, we study the effect of enhancing GPU-accelerated Krylov solvers with
preconditioners. We consider the BiCGSTAB, CGS, QMR, and IDR (s) Krylov solvers. For a …
preconditioners. We consider the BiCGSTAB, CGS, QMR, and IDR (s) Krylov solvers. For a …
The scalability-efficiency/maintainability-portability trade-off in simulation software engineering: Examples and a preliminary systematic literature review
Large-scale simulations play a central role in science and the industry. Several challenges
occur when building simulation software, because simulations require complex software …
occur when building simulation software, because simulations require complex software …
Improving pseudo-time stepping convergence for cfd simulations with neural networks
A Zandbergen, T van Noorden, A Heinlein - arXiv preprint arXiv …, 2023 - arxiv.org
Computational fluid dynamics (CFD) simulations of viscous fluids described by the Navier-
Stokes equations are considered. Depending on the Reynolds number of the flow, the …
Stokes equations are considered. Depending on the Reynolds number of the flow, the …
AutoAMG(): An Auto-tuned AMG Method Based on Deep Learning for Strong Threshold
Algebraic Multigrid (AMG) is one of the most widely used iterative algorithms for solving
large sparse linear equations $ Ax= b $. In AMG, the coarse grid is a key component that …
large sparse linear equations $ Ax= b $. In AMG, the coarse grid is a key component that …
Graph Neural Networks for Selection of Preconditioners and Krylov Solvers
Solving large sparse linear systems is ubiquitous in science and engineering, generally
requiring iterative solvers and preconditioners, as many problems cannot be solved …
requiring iterative solvers and preconditioners, as many problems cannot be solved …
A prediction framework for fast sparse triangular solves
Sparse triangular solve (SpTRSV) is an important linear algebra kernel, finding extensive
uses in numerical and scientific computing. The parallel implementation of SpTRSV is a …
uses in numerical and scientific computing. The parallel implementation of SpTRSV is a …
A Survey on Intelligent Iterative Methods for Solving Sparse Linear Algebraic Equations
Efficiently solving sparse linear algebraic equations is an important research topic of
numerical simulation. Commonly used approaches include direct methods and iterative …
numerical simulation. Commonly used approaches include direct methods and iterative …
A New Matrix Feature Selection Strategy in Machine Learning Models for Certain Krylov Solver Prediction
Numerical simulation processes in scientific and engineering applications require efficient
solutions of large sparse linear systems, and variants of Krylov subspace solvers with …
solutions of large sparse linear systems, and variants of Krylov subspace solvers with …
MaLeFICE: Machine learning support for continuous performance improvement in computational engineering
HB Sonmezer, N Muhtaroglu, I Ari… - Concurrency and …, 2022 - Wiley Online Library
Computer aided engineering (CAE) practices improved drastically within the last decade
due to ease of access to computing resources and open‐source software. However …
due to ease of access to computing resources and open‐source software. However …