blond 2.1.11

CERN code for simulating longitudinal beam dynamics in synchrotrons.

Beam Longitudinal Dynamics Code (BLonD)

Copyright Notice

Copyright 2019 CERN. This software is distributed under the terms of the GNU General Public Licence version 3 (GPL Version 3), copied verbatim in the file LICENCE.txt. In applying this licence, CERN does not waive the privileges and immunities granted to it by virtue of its status as an Intergovernmental Organization or submit itself to any jurisdiction.

Description

CERN code for the simulation of longitudinal beam dynamics in synchrotrons.

Useful Links

Repository: https://gitlab.cern.ch/blond/BLonD

Documentation: https://blond-code.docs.cern.ch/

Project website: http://blond.web.cern.ch

Installation

Dependencies

1. Python 3.8 or above (Anaconda is recommended).
2. (Optional) For better performance, a C++ (e.g. gcc, icc, clang, etc) compiler with C++11 support.

(Optional) C++ compiler installation instructions

Windows

1. Download the latest mingw-w64 using this link: https://winlibs.com/#download-release
2. Extract the downloaded zip/7-zip under e.g. C:\. You should now see a directory C:\mingw64.
3. Add C:\mingw64\bin in the PATH Environment Variable. Here you can see how to do this in Windows XP/Vista/7/8/10/11.
4. To validate the correct setup of gcc, open a command prompt and type: gcc --version. The first output line should contain the gcc version you just installed.

Linux

Use your distribution's package manager to install the compiler of your choice. BLonD has been tested with: gcc (recommended), icc, and clang.

Installation Steps

Installing BLonD from PyPI.

• Use the pip package manager and simply run:

  pip install blond
  

Installing BLonD manually (advanced users/ developers).

1. Clone the repository (with git) or download and extract it.

2. (Optional) From within the BLonD directory run:

   python blond/compile.py
   

   See the complete list of optional command line arguments with:

   python blond/compile.py --help
   

3. Then install BLonD in edit mode with:

   pip install -e .
   

Confirm proper installation

• A quick way to confirm the successfull installation is to run:

  python -c "from blond import test; test()"
  

• If you installed BLonD manually, you can in addition run the unittests with pytest. The pytest package has to be installed first with pip. :

  pip install pytest
  pytest -v unittests
  

  Note that running all the unit-tests might take more than 20 minutes, depending on your system.

• You may also run some of the example main files found in the __EXAMPLES directory:

  python __EXAMPLES/main_files/EX_01_Acceleration.py
  python __EXAMPLES/main_files/EX_02_Main_long_ps_booster.py
  etc..
  

Performance Optimizations

BLonD contains three computational backends, sorted in order of better performance:

1. C++ backend (Supports multi-threading and vectorization)
2. Numba backend (Supports multi-threading and vectorization)
3. Python-only backend (No multi-threading or vectorization)

The performance order also defines the order in which the backends will be used. If the C++ blond libary has been compiled, then the C++ backend will be used. Otherwise, if the numba package is installed, the numba backend will be used. Finally, if neither condition is met, the python-only backend will be used.

To use the Numba backend, you simply need to install the numba package with pip:

pip install numba

To use the C++ backend, follow the instructions provided in the section Installing BLonD manually.

In addition you may want to:

• Use the multi-threaded blond C++ backend:

  python blond/compile.py --parallel
  

• Enable processor specific compiler optimizations:

  python blond/compile.py --parallel --optimize
  

• If you are test-case is calling the synchrotron radiation tracking method, you can accelerate it by using the Boost library. To do so you have to:

  1. Download Boost: https://www.boost.org/. Let's say the version you downloaded is boost_1_70.
  2. Extract it, let's say in /user/path/to/boost_1_70.
  3. Pass the boost installation path when compiling BLonD:

     python blond/compile.py --boost=/user/path/to/boost_1_7_70
     

• Check the following section about the FFTW3 library.

• All the above can be combined, i.e.:

  python blond/compile.py --parallel --optimize --boost=...
  

Changing the floating point number datatype

By default BLonD uses double precision calculations (float64). To change to single precision for faster calculations, in the beginning of your mainfile you will have to add the following code lines:

from blond.utils import bmath as bm
bm.use_precision('single') 

Use the FFTW3 library for the FFTs

So far only the rfft(), irfft() and fftfreq() routines are supported. fft_convolve() to be added soon.

• Windows:

  1. Download and unzip the pre-compiled FFTW3 library. Link: ftp://ftp.fftw.org/pub/fftw/fftw-3.3.5-dll64.zip

  2. Copy the libfftw3-3.dll under your python's distribution directory.

  3. Run the blond/compile.py with the flag --with-fftw.

  4. If the FFTW3 library is not installed in one of the default directories, use the --with-fftw-lib and --with-fftw-header to point to the directories where the shared library and header files are stored.

  5. To use the supported routines, you need to call the function use_fftw() from bmath.py:

     from blond.utils import bmath as bm
     bm.use_fftw()
     ...
     bm.rfft(...)
     bm.irfft(...)
     bm.rfftfreq(...)
     

• Linux:

  1. Download and compile the FFTW3 library. Link: http://www.fftw.org/fftw-3.3.8.tar.gz

  2. Run the blond/compile.py with the flag: --with-fftw.

  3. If the FFTW3 library is not installed in one of the default directories, use the --with-fftw-lib and --with-fftw-header to point to the directories where the shared library and header files are stored.

  4. Optionally, you can enable one of the flags --with-fftw-omp or --with-fftw-threads to use the multithreaded FFTs.

  5. To use the supported routines, you need to call the function use_fftw() from bmath.py:

     from blond.utils import bmath as bm
     bm.use_fftw()
     ...
     bm.rfft(...)
     bm.irfft(...)
     bm.rfftfreq(...)
     

Using the multi-node (MPI) implementation

Set-up Instructions

• Add the following lines in your ~/.bashrc, then source it:

  # Environment variables definitions
  export LD_LIBRARY_PATH="$HOME/install/lib"
  export INSTALL_DIR="$HOME/install"
  export BLONDHOME="$HOME/git/BLonD"
  
  # User aliases
  alias mysqueue="squeue -u $USER"
  alias myscancel="scancel -u $USER"
  alias mywatch="watch -n 30 'squeue -u $USER'"
  
  # Module loads
  module load compiler/gcc7
  module load mpi/mvapich2/2.3
  

• Download and install anaconda3:

  cd ~
  mkdir -p ~/downloads
  cd downloads
  wget https://repo.continuum.io/archive/Anaconda3-2018.12-Linux-x86_64.sh
  bash Anaconda3-2018.12-Linux-x86_64.sh -b -p $HOME/install/anaconda3
  

• Download and install fftw3 (with the appropriate flags):

  cd ~
  mkdir -p ~/downloads
  cd downloads
  wget http://www.fftw.org/fftw-3.3.10.tar.gz
  tar -xzvf fftw-3.3.10.tar.gz
  cd fftw-3.3.10
  ./configure --prefix=$HOME/install/ --enable-openmp --enable-single --enable-avx --enable-avx2 --enable-fma --with-our-malloc --disable-fortran --enable-shared
  make -j4
  make install
  ./configure --prefix=$HOME/install/ --enable-openmp --enable-avx --enable-avx2 --enable-fma --with-our-malloc --disable-fortran --enable-shared
  make -j4
  make install
  

• install mpi4py with pip:

  pip install mpi4py
  

• clone this repo, compile the library and link with fftw3_omp

  cd ~
  mkdir -p git
  cd git
  git clone --branch=master https://github.com/blond-admin/BLonD.git
  cd BLonD
  python blond/compile.py -p --with-fftw --with-fftw-threads --with-fftw-lib=$INSTALL_DIR/lib --with-fftw-header=$INSTALL_DIR/include
  

• adjust your main file as needed (described bellow).

• example scripts to setup and run a parameter scan in the HPC Slurm cluster: https://cernbox.cern.ch/index.php/s/shqtotwyn4rm8ws

Changes required in the main file for MPI

1. This statements in the beginning of the script:

   from blond.utils import bmath as bm
   from blond.utils.mpi_config import WORKER, mpiprint
   bm.use_mpi()  
   

2. After having initialized the beam and preferably just before the start of the main loop:

   # This line splits the beam coordinates equally among the workers.
   beam.split()
   

3. If there is code block that you want it to be executed by a single worker only, you need to surround it with this if condition:

   if WORKER.is_master:
       foo()
       ...
   

4. If you need to re-assemble the whole beam back to the master worker you need to run:

   beam.gather()
   

5. Finally, in the end of the simulation main loop, you can terminate all workers except from the master with:

   WORKER.finalize()
   

6. To run your script, you need to pass it to mpirun or mpiexec. To spawn P MPI processes run:

   mpirun -n P python main_file.py
   

7. For more examples have a look at the __EXAMPLES/mpi_main_files/ directory.

Using the GPU backend

Setup Instructions

1. Install CUDA: https://developer.nvidia.com/cuda-downloads

2. Install the CuPy library: https://docs.cupy.dev/en/stable/install.html

3. To verify your installation open a python terminal and execute the following script

   import cupy as cp 
   import numpy as np 
   a = cp.array(np.zeros(1000,np.float64)) 
   

   To compile the CUDA files execute blond/compile.py and add the flag --gpu. The Compute Capability of your GPU will be automatically detected:

   python blond/compile.py --gpu 
   

Changes required in the main file for GPU

1. Right before your main loop you need to add:

   from blond.utils import bmath as bm
   # change some of the basic functions to their GPU equivalent
   bm.use_gpu()
   

2. Also for every object you are using in your main loop that is in the following list:

   GPU objects
   Beam
   Profile
   RingAndRFTracker
   TotalInducedVoltage
   InducedVoltageTime
   InducedVoltageFreq
   InductiveImpedance
   InducedVoltageResonator
   RFStation
   BeamFeedback

   you need to call their to_gpu() method. The following is a typical example from the __EXAMPLES/gpu_main_files/EX_01_Acceleration.py mainfile.

   # Define Objects
   beam = Beam(ring, N_p, N_b)
   profile = Profile(beam, CutOptions(n_slices=100), 
               FitOptions(fit_option='gaussian'))
   # Initialize gpu
   beam.to_gpu()
   profile.to_gpu()
   

   If an object of this list has a reference inside a different one you don't need to use the to_gpu() for the referenced object. In the previous example, we don't need to call beam.to_gpu() since beam is referenced inside the profile. The same would apply in a TotalInducedVoltage object and the objects in its induced_voltage_list.

Developers

• Simon Albright (simon.albright (at) cern.ch)
• Theodoros Argyropoulos (theodoros.argyropoulos (at) cern.ch)
• Konstantinos Iliakis (konstantinos.iliakis (at) cern.ch)
• Ivan Karpov (ivan.karpov (at) cern.ch)
• Alexandre Lasheen (alexandre.lasheen (at) cern.ch)
• Juan Esteban Muller (JuanF.EstebanMuller (at) ess.eu)
• Danilo Quartullo (danilo.quartullo (at) cern.ch)
• Joel Repond (joel (at) repond.ch)
• Markus Schwarz (markus.schwarz (at) kit.edu)
• Helga Timko (Helga.Timko (at) cern.ch)

Structure

• the folder __EXAMPLES contains several main files which show how to use the principal features of the code;
• the __doc folder contains the source files for the documentation on-line;
• the various packages which constitute the code are under the blond directory;
• blond/compile.py is needed to compile all the C++ files present in the project; this file should be run once before launching any simulation. The compiler C++ GCC (at least version 4.8) is necessary.
• WARNINGS.txt contains useful information related to code usage.