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The Module Collection
Conceptsshowing only Data Parallelism Show all Concepts
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Parallel Computing Concepts
This concept module will introduce a core of parallel computing notions that CS majors and minors should know in preparation for the era of manycore computing, including parallelism categories, concurrency issues and solutions, and programming strategies.
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.
This module, targeted for algorithms and data structures courses, examines the theoretical PRAM model and its use when designing a parallel version of the mergesort algorithm.
Elizabeth Shoop; Yu Zhao
In this module, we will learn how to create programs that intensionally use GPU to execute. To be more specific, we will learn how to solve parallel problems more efficiently by writing programs in CUDA C Programming Language and then executes them on GPUs based on CUDA architecture.
Distributed Computing Fundamentals
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.
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.