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Not sure how to do this without a bit of research, but we should try to have Miniconda numpy/scipy use openBLAS. The Anaconda page suggests it is using ATLAS.
Perhaps not a must-have, but if we're serious about having BCE set up for scientific computing, we should do this soon.
The text was updated successfully, but these errors were encountered:
We'll probably have to conda build numpy/scipy via the -openblas recipes at https://github.com/conda/conda-recipes. If this takes too long we might
need to do this outside of bootstrap and host the builds at binstar.
Ryan
On Wed, Sep 23, 2015 at 5:37 PM, Christopher Paciorek < [email protected]> wrote:
Not sure how to do this without a bit of research, but we should try to
have Miniconda numpy/scipy use openBLAS. The Anaconda page suggests it is
using ATLAS.
Perhaps not a must-have, but if we're serious about having BCE set up for
scientific computing, we should do this soon.
—
Reply to this email directly or view it on GitHub #67.
I did a brief amount of testing and the current dot (matrix multiply) in Python in BCE-summer-2015 is actually quite fast (surprisingly so considering other comparisons of different BLAS's that I've done in the past). Not threaded though, so still worth linking against openBLAS.
I'll note that dot in Python in BCE-spring-2015 is very slow.
@ryanlovett: looks like numpy's _dotblas.so (which does dot product/matrix multiply - not sure what else) is now linking against openblas in BCE-2015-fall but not a bunch of numpy's linear algebra. We should chat further about this.
Not sure how to do this without a bit of research, but we should try to have Miniconda numpy/scipy use openBLAS. The Anaconda page suggests it is using ATLAS.
Perhaps not a must-have, but if we're serious about having BCE set up for scientific computing, we should do this soon.
The text was updated successfully, but these errors were encountered: