Monte Carlo Simulation Exemplar

Text by Devin Bjelland and Libby Shoop, Macalester College

Accompanying instructor videos by Dave Valentine, Slippery Rock University

Monte Carlo simulations are a class of algorithms that are quite easy to convert from their original sequential solutions to corresponding parallel or distributed solutions that run much faster. This module introduces these type of algorithms, providing some examples with C++ code for both the original sequential version and the parallelized OpenMP version.

Hardware and Software Needed

• You will need access to a multicore computer with a C/C++ compiler that enables compilation with OpenMP.
• If you want to try some of the other examples for Message Passing using MPI you will need access to a cluster of computers with an MPI library installed.
• If you want to try some of the other examples for CUDA on GPUs, you will need acces to a computer with a CUDA-capable nVIDIA GPU and you will need the nVIDIA CUDA Toolkit installed.

This document contains several sections with example C++ code to explain Monte Carlo methods and how to parallelize them using the OpenMP library. The last three sections contain exercises that you can try and explain a more advanced topic for ensuring greater accuracy.

• What are Monte Carlo Methods?
• About Randomness
• Testing out random number generators: Flip a coin many times
• Parallel Code with Threads
• Coin-flipping in Parallel
• Roulette Simulation
• Drawing Four Cards of the Same Suit
• Plinko from the Price is Right
• Exercises
• Advanced Topic: Seeds For Different Threads