Exercise & Notebook
Chris Myers, Andrew Dolgert (original author)
Cornell Center for Advanced Computing
Revisions: 6/2023, 5/2020, 8/2018, 6/2015, 5/2011 (original)
This section contains a Python exercise for you to run, either on Frontera at TACC or locally on your own system, which uses the mpi4py package to run a parallel program. Also included is a Jupyter notebook containing slides from a webinar presentation summarizing some of the material in this CVW roadmap.
Objectives
After completing this topic, you should be able to:
- Run an exercise using the mpi4py package to estimate the numerical value of pi using a Monte Carlo simulation
- Discuss the convergence of the Monte Carlo simulation
Prerequisites
As this topic focuses on accelerating Python programs for scientific computing, it implicitly assumes the reader has some prior experience programming in Python, as well as working knowledge of general programming concepts. The target audience is scientists and engineers who are already programming in Python, and are interested in using Python tools and packages to improve the run time performance of the programs they are developing. If additional introductory material about Python is needed, readers can consult Introduction to Python Programming as well as the documentation on the python.org website. Being able to run the code examples described in this roadmap will require either being able to install Python and related packages on your local machine, or having access to a managed system that has the relevant packages installed. If you wish to run python on the Frontera system at TACC, you will need an allocation to run there.