Contents

Textbooks and practicalities

Textbooks and practicalities

Textbooks

Recommended textbooks: Lecture Notes by Morten Hjorth-Jensen and Bernd A. Berg, Markov Chain Monte Carlo Simulations and their Statistical Analysis, World Scientific, 2004, chapters 1, 2

Practicalities

Course participants are expected to have their own laptops/PCs. We use Git as version control software and the usage of providers like GitHub, GitLab or similar are strongly recommended.

We will make extensive use of Python and C++ as programming languages.

If you have Python installed and you feel pretty familiar with installing different packages, we recommend that you install the following Python packages via pip as

  • pip install numpy scipy matplotlib ipython scikit-learn mglearn sympy pandas pillow

For OSX users we recommend, after having installed Xcode, to install brew. Brew allows for a seamless installation of additional software via for example

  • brew install python3

For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution, you can use pip as well and simply install Python as

  • sudo apt-get install python3

Python installers

If you don’t want to perform these operations separately and venture into the hassle of exploring how to set up dependencies and paths, we recommend two widely used distrubutions which set up all relevant dependencies for Python, namely

which is an open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.

is a Python distribution for scientific and analytic computing distribution and analysis environment, available for free and under a commercial license.

Furthermore, Google’s Colab:https://colab.research.google.com/notebooks/welcome.ipynb is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. Try it out!

Useful Python libraries

Here we list several useful Python libraries we strongly recommend (if you use anaconda many of these are already there)

  • NumPy:https://www.numpy.org/ is a highly popular library for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays

  • The pandas:https://pandas.pydata.org/ library provides high-performance, easy-to-use data structures and data analysis tools

  • Xarray:http://xarray.pydata.org/en/stable/ is a Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun!

  • Scipy:https://www.scipy.org/ (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.

  • Matplotlib:https://matplotlib.org/ is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.

  • Autograd:https://github.com/HIPS/autograd can automatically differentiate native Python and Numpy code. It can handle a large subset of Python’s features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives

  • SymPy:https://www.sympy.org/en/index.html is a Python library for symbolic mathematics.

  • scikit-learn:https://scikit-learn.org/stable/ has simple and efficient tools for machine learning, data mining and data analysis

  • TensorFlow:https://www.tensorflow.org/ is a Python library for fast numerical computing created and released by Google

  • Keras:https://keras.io/ is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano

  • And many more such as pytorch:https://pytorch.org/, Theano:https://pypi.org/project/Theano/ etc

Useful C++ libraries

How do I run MPI/OpneMP on a PC/Laptop? MPI

To install MPI is rather easy on hardware running unix/linux as operating systems, follow simply the instructions from the “OpenMPI website”:”https://www.open-mpi.org/”. See also subsequent slides. When you have made sure you have installed MPI on your PC/laptop,

  • Compile with mpicxx/mpic++ or mpif90 Compile and link mpic++ -O3 -o nameofprog.x nameofprog.cpp run code with for example 8 processes using mpirun/mpiexec mpiexec -n 8 ./nameofprog.x

If you wish to install MPI and OpenMP on your laptop/PC, we recommend the following:

  • For OpenMP, the compile option -fopenmp is included automatically in recent versions of the C++ compiler and Fortran compilers. For users of different Linux distributions, simply use the available C++ or Fortran compilers and add the above compiler instructions, see also code examples below.

  • For OS X users however, install libomp brew install libomp and compile and link as c++ -o -lomp

For linux/ubuntu users, you need to install two packages (alternatively use the synaptic package manager) sudo apt-get install libopenmpi-dev sudo apt-get install openmpi-bin

For OS X users, install brew (after having installed xcode and gcc, needed for the gfortran compiler of openmpi) and then install with brew

brew install openmpi

When running an executable (code.x), run as

mpirun -n 10 ./code.x

where we indicate that we want the number of processes to be 10.

previous

Instructor information

next

1. Many-body Hamiltonians, basic linear algebra and Second Quantization