Introduction to Parallel Computing: mudanças entre as edições
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** For Cuda: Programming Massively Parallel Processors: A Hands-On Approach[http://books.google.com.br/books?id=qW1mncii_6EC&printsec=frontcover&hl=pt-BR#v=onepage&q&f=false] | ** For Cuda: Programming Massively Parallel Processors: A Hands-On Approach[http://books.google.com.br/books?id=qW1mncii_6EC&printsec=frontcover&hl=pt-BR#v=onepage&q&f=false] | ||
** General textbook: A. Grama, A. Gupta, G. Karypis, V. Kumar, Introduction to Parallel Computing, Second Edition, Addison-Wesley, 2003. | ** General textbook: A. Grama, A. Gupta, G. Karypis, V. Kumar, Introduction to Parallel Computing, Second Edition, Addison-Wesley, 2003. | ||
** Algorithms-oriented textbook: Algorithms: sequential, parallel, and | ** Algorithms-oriented textbook: Algorithms: sequential, parallel, and distributed, Kenneth A. Berman, Jerome L. Paul | ||
distributed, Kenneth A. Berman, Jerome L. Paul | |||
*** For more theoretical info, see the chapter on parallel algorits in Cormen's classical algorithms book. | *** For more theoretical info, see the chapter on parallel algorits in Cormen's classical algorithms book. | ||
* Mapreduce, GFS, and Bigtable: | * Mapreduce, GFS, and Bigtable: |
Edição das 11h44min de 14 de agosto de 2012
This is the main page of a course in parallel computing being taught in 2012/2 at the Polytechnic Institute IPRJ/UERJ. It is generally useful for programmers at the advanced level in the fields of scientific and multimedia programming.
General Info
- Meeting times: Tues 12:30pm-2pm, Thursdays 2:20pm - 4pm
- Evaluation criteria: The class will consist on 1 quizz at the end of the term (60%), plus practical projects (40%).
Pre-requisites
- Linux - intermediate to advanced (will be reviewed as needed)
- C/C++ - intermediate to advanced (will be reviewed)
Approximate Content
The course focuses on software techniques for parallel computing. We are aiming at a comprehensive treatment on different types of practical parallel programming techniques
- process-oriented parallel programming
- thread programming/thread safety
- single-core vector instructions
- multi-processor and multi-core programming
- mapreduce/hadoop
- MPI
- Cuda
Homework
Project 1
- The class will be divided into interest groups
- The interest groups will each pick one of the following technologies or else propose another one of their liking.
- The project will consist in a series of presentations by the group members
individually.
- Grade will be asigned to each presentation individually
Project 2
- This project will consist on a practical programming problem from the
student's research. The student is required to describe a problem from his research and present a parallel implementation of some aspect of it.
- This will be evaluated through actual code and acompanying monograph.
Main Resources
- Textbooks
- 1st part of the course: "Is Parallel Programming Hard, and, if so, what can you do about it?" - Paul E. McKenney / IBM (editor).
- For MPI: "An Introduction to Parallel Programming" , by Peter Pacheco[2]
- For Cuda: Programming Massively Parallel Processors: A Hands-On Approach[3]
- General textbook: A. Grama, A. Gupta, G. Karypis, V. Kumar, Introduction to Parallel Computing, Second Edition, Addison-Wesley, 2003.
- Algorithms-oriented textbook: Algorithms: sequential, parallel, and distributed, Kenneth A. Berman, Jerome L. Paul
- For more theoretical info, see the chapter on parallel algorits in Cormen's classical algorithms book.
- Mapreduce, GFS, and Bigtable:
- Papers from Google
- http://code.google.com/edu/parallel/mapreduce-tutorial.html
- Wikipedia
- Hadoop documentation
- Presentations from IBM and Intel: Cilk, etc.
- Wikipedia pages
- Rice lecture notes on Parallel Computing [4]
- Other Resources
Lectures
Partial listing & Tentative Outline
- Overview of parallel computing: https://computing.llnl.gov/tutorials/parallel_comp/
- Review of Linux:
- See the book Running Linux http://wiki.nosdigitais.teia.org.br/Literatura_recomendada_pela_equipe
- Review of C/C++
- Fundamental programming techniques: processes and threads
- Read The Unix Programming Environment for some classic multi-process programming[5]
- Mapreduce/Hadoop
- MPI
- Cuda