Why does multiprocessing use only a single core after I import numpy?
I am not sure whether this counts more as an OS issue, but I thought I would ask here in case anyone has some insight from the Python end of things.
blas
Compiling numpy with OpenBLAS integration
I am trying to install numpy with OpenBLAS , however I am at loss as to how the site.cfg file needs to be written.
Link ATLAS/MKL to an installed Numpy
TL;DR how to link ATLAS/MKL to existing Numpy without rebuilding.
How to check BLAS/LAPACK linkage in NumPy and SciPy?
I am builing my numpy/scipy environment based on blas and lapack more or less based on this walk through.
multithreaded blas in python/numpy
I am trying to implement a large number of matrix-matrix multiplications in Python. Initially, I assumed that NumPy would use automatically my threaded BLAS libraries since I built it against those libraries. However, when I look at top or something else it seems like the code does not use threading at all.
calling dot products and linear algebra operations in Cython?
I’m trying to use dot products, matrix inversion and other basic linear algebra operations that are available in numpy from Cython. Functions like numpy.linalg.inv (inversion), numpy.dot (dot product), X.t (transpose of matrix/array). There’s a large overhead to calling numpy.* from Cython functions and the rest of the function is written in Cython, so I’d like to avoid this.
Benchmarking (python vs. c++ using BLAS) and (numpy)
I would like to write a program that makes extensive use of BLAS and LAPACK linear algebra functionalities. Since performance is an issue I did some benchmarking and would like know, if the approach I took is legitimate.
Numpy Pure Functions for performance, caching
I’m writing some moderately performance critical code in numpy.
This code will be in the inner most loop, of a computation that’s run time is measured in hours.
A quick calculation suggest that this code will be executed up something like 10^12 times, in some variations of the calculation.