# Rexsss Performance Analysis: Domain Decomposition Algorithm Implementations for Resilient Numerical Partial Differential Equation Solvers

## Abstract

The future of extreme-scale computing is expected to magnify the influence of soft faults as a source of inaccuracy or failure in solutions obtained from distributed parallel computations. The development of resilient computational tools represents an essential recourse for understanding the best methods for absorbing the impacts of soft faults without sacrificing solution accuracy. The Rexsss (Resilient Extreme Scale Scientific Simulations) project pursues the development of fault resilient algorithms for solving partial differential equations (PDEs) on distributed systems. Performance analyses of current algorithm implementations assist in the identification of runtime inefficiencies.

- Authors:

- California State Univ., Turlock, CA (United States)
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)

- Publication Date:

- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)

- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)

- OSTI Identifier:
- 1171553

- Report Number(s):
- SAND2014-16842R

536698

- DOE Contract Number:
- AC04-94AL85000

- Resource Type:
- Technical Report

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 97 MATHEMATICS AND COMPUTING

### Citation Formats

```
Dahlgren, Kathryn Marie, Rizzi, Francesco, Morris, Karla Vanessa, and Debusschere, Bert.
```*Rexsss Performance Analysis: Domain Decomposition Algorithm Implementations for Resilient Numerical Partial Differential Equation Solvers*. United States: N. p., 2014.
Web. doi:10.2172/1171553.

```
Dahlgren, Kathryn Marie, Rizzi, Francesco, Morris, Karla Vanessa, & Debusschere, Bert.
```*Rexsss Performance Analysis: Domain Decomposition Algorithm Implementations for Resilient Numerical Partial Differential Equation Solvers*. United States. doi:10.2172/1171553.

```
Dahlgren, Kathryn Marie, Rizzi, Francesco, Morris, Karla Vanessa, and Debusschere, Bert. Fri .
"Rexsss Performance Analysis: Domain Decomposition Algorithm Implementations for Resilient Numerical Partial Differential Equation Solvers". United States.
doi:10.2172/1171553. https://www.osti.gov/servlets/purl/1171553.
```

```
@article{osti_1171553,
```

title = {Rexsss Performance Analysis: Domain Decomposition Algorithm Implementations for Resilient Numerical Partial Differential Equation Solvers},

author = {Dahlgren, Kathryn Marie and Rizzi, Francesco and Morris, Karla Vanessa and Debusschere, Bert},

abstractNote = {The future of extreme-scale computing is expected to magnify the influence of soft faults as a source of inaccuracy or failure in solutions obtained from distributed parallel computations. The development of resilient computational tools represents an essential recourse for understanding the best methods for absorbing the impacts of soft faults without sacrificing solution accuracy. The Rexsss (Resilient Extreme Scale Scientific Simulations) project pursues the development of fault resilient algorithms for solving partial differential equations (PDEs) on distributed systems. Performance analyses of current algorithm implementations assist in the identification of runtime inefficiencies.},

doi = {10.2172/1171553},

journal = {},

number = ,

volume = ,

place = {United States},

year = {Fri Aug 01 00:00:00 EDT 2014},

month = {Fri Aug 01 00:00:00 EDT 2014}

}

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