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Activities

Data Grids and Data Management
Data management evolution. Description and SRB sample applications of shared collections, digital libraries, and persistent archives. Description of federated server architecture. Overview and architecture of SRB.
Living Links: Applications of Matrix Operations to Population Studies
Many computational science models involve matrices. This module will provide a foundation for understanding matrices and some of their operations with examples from population dynamics and will provide background material for other computational science modules. The module will also have a discussion of the importance of matrices in many models that employ high performance computing. The model will have two accompanying tutorials. One will include some of the basics of MPI. The other will provide an introduction to matrices in Mathematica along with Grid Mathematica.
nanoHUB
The nanoHUB is a web-based resource for research, education, and collaboration in nanotechnology. It includes simulations, collaboration tools, learning and teaching materials. The site also allows users to become contributors of new material.
NASA Glenn Learning Technologies Project Simulations
In an effort to foster hands-on, inquiry-based learning in science and math, the NASA Glenn Research Center has developed a series of interactive computer programs for students. All of the programs are Java applets which run in your browser, on-line, over the World Wide Web. We have also developed a series of Beginner's Guides that accompany each of the software packages to explain the science and math. You can access the Beginner's Guides at the bottom of this page. And for teachers, we have developed almost 200 activities to test the student's knowledge of the material. These grade-specific activities have been developed by teachers during summer workshops and are aligned with science and math standards.
National Center for Case Study Teaching in Science
The mission of the National Center for Case Study Teaching in Science at the University at Buffalo is to promote the development and dissemination of materials and practices for case teaching in the sciences. We have found the method to be extraordinarily flexible. We have seen it used as the core of entire courses or for single experiences in otherwise traditional lecture and lab courses. Our website provides access to an award-winning collection of peer-reviewed case studies.
Parallelization: Area Under a Curve
This module teaches: 1) How to approximate the area under a curve using a Riemann sum, 2) how approximating the area under a curve is used in solutions to scientific problems, 3) how to implement parallel code for Area Under a Curve (including versions that use shared memory via OpenMP, distributed memory via the Message Passing Interface (MPI), and hybrid via a combination of MPI and OpenMP), 4) how to measure the performance and scaling of a parallel application in multicore and manycore environments, and 5) how Area Under a Curve falls into the MapReduce "dwarf" (a class of algorithms that have similar communication and computation patterns). Upon completion of this module, students should be able to: 1) Understand the importance of approximating the area under a curve in modeling scientific problems, 2) Design a parallel algorithm and implement it using MPI and/or OpenMP, 3) Measure the scalability of a parallel code over multiple or many cores, and 4) Explain the communication and computation patterns of the MapReduce dwarf. It is assumed that students will have prerequisite experience with C or Fortran 90, *nix systems, and modular arithmetic.
Parallelization: Conway's Game of Life
This module teaches: 1) Conway's Game of Life as an example of a cellular automaton, 2) how cellular automata are used in solutions to scientific problems, 3) how to implement parallel code for Conway's Game of Life (including versions that use shared memory via OpenMP, distributed memory via the Message Passing Interface (MPI), and hybrid via a combination of OpenMP and MPI), 4) how to measure the performance and scaling of a parallel application in multicore and manycore environments, and 5) how cellular automata fall into the Structured Grid "dwarf" (a class of algorithms that have similar communication and computation patterns). Upon completion of this module, students should be able to: 1) Understand the importance of cellular automata in modeling scientific problems, 2) Design a parallel algorithm and implement it using OpenMP and/or MPI, 3) Measure the scalability of a parallel code over multiple or many cores, and 4) Explain the communication and computation patterns of the Structured Grid dwarf. It is assumed that students will have prerequisite experience with C or Fortran 90, *nix systems, and modular arithmetic.
Parallelization: Infectious Disease
Epidemiology is the study of infectious disease. Infectious diseases are said to be "contagious" among people if they are transmittable from one person to another. Epidemiologists can use models to assist them in predicting the behavior of infectious diseases. This module will develop a simple agent-based infectious disease model, develop a parallel algorithm based on the model, provide a coded implementation for the algorithm, and explore the scaling of the coded implementation on high performance cluster resources.
Party Problem for LittleFe (OpenMP and MPI in C)
A parallel computing module application that faculty can use for students who have learned the basics of OpenMP and MPI and C. The module comes with lecture slides, a write-up for the instructor, and the solution code.
ScienceCourseware.org
The Virtual Courseware Project produces interactive, online simulations for inquiry-based science education, earth and environmental sciences, geology, and biology. The activities are designed to enhance an existing curriculum and include online assessments.
Time after Time: Age- and Stage-Structured Models
This page provides download links for a set of curricular materials designed to teach parallel computational modeling to undergraduate or graduate students in science and other STEM disciplines. The module begins with a description of the importance of age-structure in biological populations. Algorithms, implementation, parameter sweeping and analysis of age-structured models is then presented for both serial and parallel implementations.