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PERF: use higher level BLAS functions

See original GitHub issue

Now that we have access to BLAS functions cython bindings from scipy, we have access to level 3 functions which weren’t present in vendored CBLAS. It would be interesting to check if they could be used in some places instead of a loop of lower level functions.

For example, in cd_fast I saw that:

# XtA = np.dot(X.T, R) - beta * w
for i in range(n_features):
    XtA[i] = (_dot(n_samples, &X[0, i], 1, &R[0], 1)
              - beta * w[i])

loop of dot (level 1) which could be replaced by a gemv (level 2)

Issue Analytics

State:
Created 5 years ago
Reactions:2
Comments:10 (9 by maintainers)

Top GitHub Comments

2reactions

amuellercommented, Jun 26, 2021

Hm do we want benchmarks for each of them? then doing them one by one might be best.

2reactions

jakirkhamcommented, Sep 10, 2019

Other potential candidates include…

https://github.com/scikit-learn/scikit-learn/blob/5f0263fe7f1ce66a91a3af01a54caad7ac546443/sklearn/linear_model/cd_fast.pyx#L499

https://github.com/scikit-learn/scikit-learn/blob/5f0263fe7f1ce66a91a3af01a54caad7ac546443/sklearn/linear_model/cd_fast.pyx#L588-L589

https://github.com/scikit-learn/scikit-learn/blob/5f0263fe7f1ce66a91a3af01a54caad7ac546443/sklearn/linear_model/cd_fast.pyx#L759-L765

https://github.com/scikit-learn/scikit-learn/blob/301076e77b648ea3d715eb823ac006ec0d88e8c3/sklearn/cluster/_k_means.pyx#L68-L71

https://github.com/scikit-learn/scikit-learn/blob/301076e77b648ea3d715eb823ac006ec0d88e8c3/sklearn/cluster/_k_means.pyx#L130-L133

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