Integration of Fraunhofer SAMG as an alternative to PyAMG #70
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
We integrated Fraunhofer SAMG as an alternative to PyAMG as the preconditioner used in LOBPCG.
We tested this on the swiss roll as an artificial dataset and on the data from word2vec used in previous benchmarks of megaman. In both cases using SAMG proved to be significantly faster than using PyAMG. For example in our tests using SAMG on the data from word2vec we were able to achieve a speedup of the embedding process by a factor between 2 and 6.
Note though that Fraunhofer SAMG is a commercial software and one needs a license to use it. For more information on SAMG see here and for licensing (including test or educational licenses) contact [email protected].