Monte Carlo Simulations: Parallelism in CS1/CS2
David Valentine, Slippery Rock University of Pennsylvania
Use Monte Carlo Simulations in CS1/CS2 to expose students to parallel programming with OpenMP.

Patternlets in Parallel Programming
Material originally created by Joel Adams, Calvin College Compiled by Libby Shoop, Macalester College
Short, simple C programming examples of basic shared memory programming patterns using OpenMP and basic distributed memory patterns using MPI.

Map-reduce Computing for Introductory Students using WebMapReduce
Professor Richard Brown, St. Olaf College Professor Libby Shoop, Macalester College
This module emphasizes data-parallel problems and solutions, the so-called 'embarrassingly parallel' problems where processing of input data can easily be split among several parallel processes. Students use a web application called WebMapReduce (WMR) to write map and reduce functions that operate on portions of a massive dataset in parallel.

Multicore Programming with OpenMP
Richard Brown, Saint Olaf College; Elizabeth Shoop, Macalester College
In this lab, we will create a program that intentionally uses multi-core parallelism, upload and run it on the MTL, and explore the issues in parallelism and concurrency that arise. This module uses OpenMP.

WMR Exemplar: LastFM million-song dataset
Elizabeth Shoop, Macalester College
This module demonstrates how hadoop and WMR can be used to analyze the lastFM million song dataset. It incorporates several advanced hadoop techniques such as job chaining and multiple input.

WMR Exemplar: UK Traffic Incidents
Elizabeth Shoop, Macalester College
Using data published by the United Kingdom department of Transportation about traffic incidents, students can explore and perform analyses using map-reduce techniques.

Visualize Numerical Integration
Elizabeth Shoop, Macalester College
This is an activity with working code supplied that enables students to see how various forms of the data decomposition pattern map processing units to computations.

Timing Operations in CUDA
Joel Adams, Calvin College, and Jeffrey Lyman, Macalester College
Through completion of vector addition, multiplication, square root, and squaring programs, students will gain an understanding of when the overhead of creating threads and copying memory is worth the speedup of GPU coding.

Concurrent Access to Data Structures in C++
Richard Brown, Saint Olaf College
This module enables students to experiment with creating a task-parallel solution to the problem of crawling the web by using C++ with Boost threads and thread-safe data structures available in the Intel Threading ...

Concurrent Access to Data Structures
Professor Libby Shoop, Macalester College
This module enables students to experiment with creating a task-parallel solution to the problem of crawling the web by using Java threads and thread-safe data structures available in the java.util.concurrent package.