# Massive Asynchronous Parallelization of Sparse Matrix Factorizations

## Abstract

Solving sparse problems is at the core of many DOE computational science applications. We focus on the challenge of developing sparse algorithms that can fully exploit the parallelism in extreme-scale computing systems, in particular systems with massive numbers of cores per node. Our approach is to express a sparse matrix factorization as a large number of bilinear constraint equations, and then solving these equations via an asynchronous iterative method. The unknowns in these equations are the matrix entries of the factorization that is desired.

- Authors:

- Georgia Inst. of Technology, Atlanta, GA (United States)

- Publication Date:

- Research Org.:
- Georgia Inst. of Technology, Atlanta, GA (United States)

- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)

- OSTI Identifier:
- 1416151

- Report Number(s):
- 2017-1

- DOE Contract Number:
- SC0012538

- Resource Type:
- Technical Report

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 97 MATHEMATICS AND COMPUTING

### Citation Formats

```
Chow, Edmond.
```*Massive Asynchronous Parallelization of Sparse Matrix Factorizations*. United States: N. p., 2018.
Web. doi:10.2172/1416151.

```
Chow, Edmond.
```*Massive Asynchronous Parallelization of Sparse Matrix Factorizations*. United States. doi:10.2172/1416151.

```
Chow, Edmond. Mon .
"Massive Asynchronous Parallelization of Sparse Matrix Factorizations". United States. doi:10.2172/1416151. https://www.osti.gov/servlets/purl/1416151.
```

```
@article{osti_1416151,
```

title = {Massive Asynchronous Parallelization of Sparse Matrix Factorizations},

author = {Chow, Edmond},

abstractNote = {Solving sparse problems is at the core of many DOE computational science applications. We focus on the challenge of developing sparse algorithms that can fully exploit the parallelism in extreme-scale computing systems, in particular systems with massive numbers of cores per node. Our approach is to express a sparse matrix factorization as a large number of bilinear constraint equations, and then solving these equations via an asynchronous iterative method. The unknowns in these equations are the matrix entries of the factorization that is desired.},

doi = {10.2172/1416151},

journal = {},

number = ,

volume = ,

place = {United States},

year = {2018},

month = {1}

}

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