Difference between revisions of "LU Decomposition"
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LU Decomposition is an example of how to use Nu+ features to optimize an algorithm, using multithreaded and vectorial code. | LU Decomposition is an example of how to use Nu+ features to optimize an algorithm, using multithreaded and vectorial code. | ||
| − | Reusable code decomposition and multithreaded and vectorial methods optimization are shown; they can be applied to every other parallelizable algorithm, from image processing to machine learning ones. | + | |
| + | Reusable code decomposition and multithreaded and vectorial methods optimization are shown; they can be applied to every other parallelizable algorithm, from image processing to machine learning ones. | ||
= Algorithm Description = | = Algorithm Description = | ||
Revision as of 18:53, 1 February 2018
Summary
LU Decomposition is an example of how to use Nu+ features to optimize an algorithm, using multithreaded and vectorial code.
Reusable code decomposition and multithreaded and vectorial methods optimization are shown; they can be applied to every other parallelizable algorithm, from image processing to machine learning ones.
