Installation
nonrad may be installed through pip from PyPI,
pip install nonrad
or directly through github,
pip install git+https://github.com/mturiansky/nonrad
Going faster
This code utilizes numba to speed up calculations, and there are various ways to improve the performance of numba.
SVML
On Intel processors, the short vector math library (SVML) can be enabled to speed up certain operations. The runtime libraries from Intel are required for this. On a conda installation, they should already be installed in the package icc_rt. The icc_rt package is also available through pip
pip install icc_rt
However, you will likely need to add your virtual environment to the library path:
export LD_LIBRARY_PATH=/path/to/.virtualenvs/env_name/lib/:$LD_LIBRARY_PATH
This can be added to your activate script in your virtual environment (/path/to/.virtualenvs/env_name/bin/activate
) to make the change persistent.
To check if the installation worked, run numba -s
; the output should include
...
__SVML Information__
SVML State, config.USING_SVML : True
SVML Library Loaded : True
llvmlite Using SVML Patched LLVM : True
SVML Operational : True
...
Numba Enviornment Variables
There are several environment variables for numba that can be enabled and may improve performance. If your machine has AVX instructions, we recommend enabling it with:
export NUMBA_ENABLE_AVX=1
The full list of numba environment variables is available here.