BE-Seminar-demo

Live demonstration of Wakis for the CERN-BE seminar: https://indico.cern.ch/event/1608797/

Involving our in-house python-packages of the impedance assessment workflow:

• Wakis for the 3D electromagnetic wakefield simulations,
• IDDEFIX for partially decayed wake extrapolation,
• BIHC for impedance-induced beam power loss, and
• neffint for impedance to wake function conversion for beam dynamics simulations.

Getting started

You can install wakis from GitHub to have the latest changes into your conda environment (Python>3.9 and <=3.12):

pip install wakis['notebook']
pip install bihc neffint iddefix # optional satellite packages

GPU setup

wakis uses cupy to access GPU acceleration with the supported NVIDIA GPUs (more info in Cupy's website). Provided a compatible NVIDIA CUDA GPU or AMD ROCm GPUs with the adequate drivers & Toolkit:

conda install -c conda-forge cupy cuda-version=11.8

MPI setup

To run multi-CPU parallelized simulations, Wakis needs the following packages:

• OpenMPI installed in your operating system:
• Python package mpi4py
• Python package ipyparallel to start a MPI kernel inside notebooks

The preferred install method is through conda-forge:

conda install -c conda-forge mpi4py openmpi
pip install ipyparallel

Multithreading setup

To achieve multithreading for Wakis sparse matrix x vector operations, the Intel-MKL backend has been implemented as an alternative to single-threaded scipy.sparse.dot. To install it in your conda environment simply do:

conda create --name wakis-env python=3.12 numpy scipy mkl mkl-service
pip install sparse_dot_mkl

Wakis will detect that the package is installed and use it as default backend. To control the number of threads and memory pinning, one can add the following lines to your python script before the imports:

# [!] Set before importing numpy/scipy/mkl modules
import os
os.environ["OMP_NUM_THREADS"] = str(os.cpu_count())       # Number of OpenMP threads
os.environ["KMP_AFFINITY"] = "balanced,granularity=fine"  # Thread pinning