WEP 088 (2013): Monte Carlo Methods and High Performance Computing

What are Monte Carlo Methods?

Monte Carlo methods are numerical methods that use random numbers to compute quantities of interest.  This is normally done by creating a random variable whose expected value is the desired quantity.  One then simulates and tabulates the random variable and uses its sample mean and variance to construct probabilistic estimates.

Purpose of the Course:

This course is an introduction to Monte Carlo methods in the context of modern high-performance computing.  We learn about the early history of Monte Carlo methods and their relationship to the advanced digital computing of the day.  This relationship continues to this day, and we explore the reasons for this.

By combining the theoretical background, with the computational approach firmly anchored in applications, the Monte Carlo method can be used to solve many problems that traditional deterministic methods cannot.  In addition, there are a wide number of areas where both Monte Carlo and deterministic methods can be used, where the Monte Carlo approach offers a very attractive alternative.  Thus, Monte Carlo methods themselves are a fruitful source of research problems, and when combined with deterministic methods have the promise to provide many improved numerical methods.

Secondary Purpose of the Course:

The course is a preview of courses that will be taught by the professor at KAUST in the next academic year when he spends his sabbatical visit here, which may include Monte Carlo methods, random number generation, and discrete mathematical techniques in theoretical Computer Science.

Meeting Time/Place:

9:30AM to 12:00 NOON: January 26, 27, and 28 in the Spine Auditorium between building 4 and 5 on the KAUST main campus.


Michael Mascagni, Ph.D.
Professor of Computer Science
Professor of Mathematics
Professor of Scientific Computing
Faculty in the Graduate Program in Molecular Biophysics
Florida State University
Tallahassee, FL  32306-4530
E-mail: mascagni@fsu.edu
Office: Dirac Science Library 498/Love 207A
Telephone: +1.850.644.3290

For the curious, here are links to a brief biography of the Professor, and the Professor's home page.

Lecture Notes:

Lecture notes for this course can be found here.  Note: there are many more notes than there is time for the lectures!