Parallel Computing in the Computer Science Curriculum > Modules


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Distributed Computing Fundamentals
Elizabeth Shoop
Message Passing Interface (MPI) is a programming model widely used for parallel programming in a cluster. Using MPI, programmers can design methods to divide large data and perform the same computing task on segments of it and then and distribute those tasks to multiple processing units within the cluster. In this module, we will learn important and common MPI functions as well as techniques used in 'distributed memory' programming on clusters of networked computers.

Heterogeneous Computing
Elizabeth Shoop;
Message Passing Interface (MPI) is a programming model widely used for parallel programming in a cluster. NVIDIA®'s CUDA, a parallel computing platform and programming model, uses GPU for parallel computation problems. This module will explore ways to combine these two parallel computing platforms to make parallel computation more efficient.

Patternlets in Parallel Programming
Material originally created by Joel Adams, Calvin CollegeCompiled by Libby Shoop, Macalester College
Short, simple C programming examples of basic shared memory programming patterns using OpneMP and basic message-passing patterns using MPI.

Concept: Data Decomposition Pattern
Elizabeth Shoop
This module consists of reading material and code examples that depict the data decomposition pattern in parallel programming, using a small-sized example of vector addition (sometimes called the "Hello, World" of parallel programming.

Visualize Numerical Integration
Elizabeth Shoop
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.

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