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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.
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.
Concurrency and Map-Reduce Strategies in Various Programming Languages
Professor Richard Brown, St. Olaf College
This concept module explores how concurrency and parallelism have been established in programming languages and how one can implement map-reduce in several high-level programming languages taught in a CS curriculum, including Scheme, C++, Java, and Python.
Multicore Programming with OpenMP
Richard Brown; Elizabeth Shoop
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.
Multi-core programming with Intel's Manycore Testing Lab (using Threading Building Blocks)
Professor Richard Brown, St. Olaf College
Intel Corporation has set up a special remote system that allows faculty and students to work with computers with lots of cores, called the Manycore Testing Lab (MTL). 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.
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.
Concurrent Access to Data Structures in C++
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 ...
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.
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 OpenMP and basic distributed memory patterns using MPI.
Sequential and parallel versions of a Monte Carlo simulation of the spread of infectious disease are presented in detail. Students can run the code and examine performance of sequential and parallel versions.
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.
Visualize Numerical Integration
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.
WMR Exemplar: UK Traffic Incidents
Using data published by the United Kingdom department of Transportation about traffic incidents, students can explore and perform analyses using map-reduce techniques.
WMR Exemplar: Flickster network data
The exercises in this module use a network of friendships on the social movie recommendation site Flixster. Students will use it to learn how to analyze networks and chain jobs, using the WebMapReduce interface.
WMR Exemplar: LastFM million-song dataset
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.
Instructor Example: Optimizing CUDA for GPU Architecture
This module, designed for instructors to use as an example, explains how to take advantage of the CUDA GPU architecture to provide maximum speedup for your CUDA applications using a Mandelbrot set generator as an example.
Monte Carlo Simulations: Parallelism in CS1/CS2
Use Monte Carlo Simulations in CS1/CS2 to expose students to parallel programming with OpenMP.
Parallel Processes in Python
Steven Bogaerts, DePauw University
This module is designed for use in the latter half of a semester-long CS1 course. It introduces students to the concepts of forking child processes to do work in parallel and how multiple concurrent processes can coordinate using a shared data queue.