s-Step Orthomin and GMRES implemented on parallel computers
January 14, 2020 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
A. T. Chronopoulos, S. K. Kim
arXiv ID
2001.04886
Category
math.NA: Numerical Analysis
Cross-listed
cs.DC
Citations
6
Venue
arXiv.org
Last Checked
1 month ago
Abstract
The Orthomin ( Omin ) and the Generalized Minimal Residual method ( GMRES ) are commonly used iterative methods for approximating the solution of non-symmetric linear systems. The s-step generalizations of these methods enhance their data locality parallel and properties by forming s simultaneous search direction vectors. Good data locality is the key in achieving near peak rates on memory hierarchical supercomputers. The theoretical derivation of the s-step Arnoldi and Omin has been published in the past. Here we derive the s-step GMRES method. We then implement s-step Omin and GMRES on a Cray-2 hierarchical memory supercomputer.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Numerical Analysis
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
R.I.P.
๐ป
Ghosted
PDE-Net: Learning PDEs from Data
R.I.P.
๐ป
Ghosted
Efficient tensor completion for color image and video recovery: Low-rank tensor train
R.I.P.
๐ป
Ghosted
Tensor Ring Decomposition
R.I.P.
๐ป
Ghosted
Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted