Key findings:
- sparsesvd is really slow and thus was not considered for the graphs
- There is almost not differnce between the implementation of randomized SVD with Gensim SVD and Scikit-Learn SVD
- MKL is faster than OpenBlas for the randomized SVD (not clearly vissible in the graphs, sorry for this)
- Randomized SVD is faster as the problem gets more difficult
Done on i3 8100, Python 3.6, Ubuntu 18.04, average of 5 runs, with the recent version of OpenBlas and MKL as of 18th June 2019.
Check out the accompanying notebook.
| OpenBlas | MKL |
|---|---|
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Based on previous work:
- https://simplyml.com/benchmarking-the-singular-value-decomposition/
- https://github.com/charanpald/tyre-hug/blob/master/tyrehug/exp/svdbenchmark.py
MIT.
This work was created as part of a project that was funded by the German Federal Ministry of Education and Research.
