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You sound like a good person to ask. I use numpy and I haven't looked into the BLAS/LAPACK details but I understand there are different ways of setting up numpy
by aidos 11y ago
You sound like a good person to ask. I use numpy and I haven't looked into the BLAS/LAPACK details but I understand there are different ways of setting up numpy for optimal performance. Any advice on the best way to approach this?
At the moment I more or less:
apt-get install liblapack-dev libopenblas-dev
pip install numpy
- dagw 11y agoHonestly it is very dependent on exactly what you are doing. For general purpose computing on a general purpose machine you're not going to do much better on average than an up to date openblas. In some cases, especially in the parallel case, Intel's MKL BLAS is slightly faster (but in some cases it is also slower). There is also scikit.cuda which wraps Nvidia's cuBLAS and which can be very fast in certain cases, but isn't in any way a drop in replacement for openblas. Then there's NumbaPro (a commercial product) from Continuum Analytics which is an LLVM backed JIT that attempts to automatically speed up your numpy coda and can automatically make your code use cuBLAS where it makes sense to do so.
- aidos 11y agoOk. Think I'll leave well enough alone then!