Difference between revisions of "GPU610/DPS915"

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{{GPU610/DPS915 Index | 20123}}
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{{GPU610/DPS915 Index | 20191}}
  
 
Please help make this page resourceful for all GPU610/DPS915 students to use!
 
Please help make this page resourceful for all GPU610/DPS915 students to use!
  
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= GPU610 - Parallel Programming Fundamentals =
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= Course Material =
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== GPU610 - Parallel Programming Fundamentals ==
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<table>
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<tr valign=top>
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<td>
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*Modern GPU (Graphics Processing Unit) technology supports massively parallel computations, which complements the serial processing capabilities of CPU technology. This course teaches students how to read, write, and debug programs that use both CPU and GPU technology. Students learn to reorganize existing programs into serial code that runs on the CPU and parallel code that runs on the GPU. Students also study cases that have benefited from CPU+GPU programming.  
 
*Modern GPU (Graphics Processing Unit) technology supports massively parallel computations, which complements the serial processing capabilities of CPU technology. This course teaches students how to read, write, and debug programs that use both CPU and GPU technology. Students learn to reorganize existing programs into serial code that runs on the CPU and parallel code that runs on the GPU. Students also study cases that have benefited from CPU+GPU programming.  
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* [https://scs.senecac.on.ca/course/gpu610 Course Outline]
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* [https://ict.senecacollege.ca/course/gpu610 Course Outline]
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</td>
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<td>
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[[Image:NV_CUDA_Teaching_Center_Small.jpg]]
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</td>
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</tr>
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</table>
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== DPS915 - Introduction to Parallel Programming ==
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<table>
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<tr valign=top>
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<td>
  
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= DPS915 - Introduction to Parallel Programming =
 
 
*Modern GPU (Graphics Processing Unit) technology supports massively parallel computations, which complements the serial processing capabilities of CPU technology. This course teaches students how to read, write, and debug programs that use both CPU and GPU technology. Students learn to reorganize existing programs into serial code that runs on the CPU and parallel code that runs on the GPU. Students also study cases that have benefited from CPU+GPU programming and develop a CPU+GPU application for a client.
 
*Modern GPU (Graphics Processing Unit) technology supports massively parallel computations, which complements the serial processing capabilities of CPU technology. This course teaches students how to read, write, and debug programs that use both CPU and GPU technology. Students learn to reorganize existing programs into serial code that runs on the CPU and parallel code that runs on the GPU. Students also study cases that have benefited from CPU+GPU programming and develop a CPU+GPU application for a client.
  
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* [https://scs.senecac.on.ca/course/dps915 Course Outline]
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* [https://ict.senecacollege.ca/course/dps915 Course Outline]
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</td>
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<td>
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[[Image:NV_CUDA_Teaching_Center_Small.jpg]]
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</td>
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</tr>
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</table>
  
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= Common Material =
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== External Links ==
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=== External Links ===
 
 
* [https://scs.senecac.on.ca/~gpu610/pages/content/index.html Course Web Site – Lecture Notes]
 
* [https://scs.senecac.on.ca/~gpu610/pages/content/index.html Course Web Site – Lecture Notes]
 
* [https://cs.senecac.on.ca/~gpu610/pages/timeline.html Course Web Site – Timeline]
 
* [https://cs.senecac.on.ca/~gpu610/pages/timeline.html Course Web Site – Timeline]
 +
<!--
 
* [svn://zenit.senecac.on.ca/dpsgpu/trunk Class Samples]
 
* [svn://zenit.senecac.on.ca/dpsgpu/trunk Class Samples]
 +
-->
  
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== The Project  ==
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== Workshops ==
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* Under construction
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* The workshops provide timely opportunities to implement some of the material covered during the lectures. Each workshop is graded and all submissions are through [https://open.senecac.on.ca/cms/course/view.php?id=536 Moodle].
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<!--
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* Detail Specifications
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The course project is a three-stage, team assignment to build a game using the framework as the starting point. Each team consists of up to 5 members.  Membership is subject to instructor approval and is open to modification until the end of the week of the drop date for the course. The first stage of the assignment proposes the game design and identifies which member will work on which aspect of the game. Each member is responsible for their own aspect. Each team meets with the instructor to review the proposal and obtain approval. The second stage releases a draft of the game.  Each team meets again with the instructor to review progress and redefine goals. The third and final stage presents the completed game to the class. Details are on the Project Requirements page.
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*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w1.html Initial Profile]
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-->
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*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w2.html BLAS]
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*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w3.html Device Query and Selection]
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*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w4.html cuBLAS]
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*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w5.html Thrust]
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*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w6.html A Simple Kernel]
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*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w7.html Reduction]
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*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w8.html Thread Divergence]
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*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w9.html Coalesced Memory Access]
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*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w10.html CUDA to OpenCL]
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* Grading - The due date for each workshop is noted in MySeneca. The penalty for late submission is 20% of the workshop mark; 50% for very late submission.
 +
 
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== Assignments ==
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# [https://scs.senecac.on.ca/~gpu610/pages/assignments/a1.html Select and Assess]
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# [https://scs.senecac.on.ca/~gpu610/pages/assignments/a2.html Parallelize]
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# [https://scs.senecac.on.ca/~gpu610/pages/assignments/a3.html Optimize]
  
 
== Evaluation ==
 
== Evaluation ==
  
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* Assignment 30%
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* Assignments and Presentation 20%
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** Individual Work - 50%
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* Workshops 30%
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** Group Work - 50% inclusive
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* Option 1: Tests 50%
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** Total (Individual + Group) - 100%
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* Option 2: Tests 35% + Exam 15%
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* Workshops 20%
 
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* Test 20%
 
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* Exam 30%
 
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== Final Submission Requirements ==
 
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When ready to submit your project:
 
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# Finalize your modifications in trunk.
 
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# Create a directory in trunk called: '''"SubmissionLogs"'''
 
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# For each member of the team create a text file named as '''"YourSenecaEmailId.txt"''' in the '''"SubmissionLogs"''' directory. In this text file, in a point form, specify in detail, all the tasks you have done for the group project.
 
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# Branch (copy) the whole project including the SubmissionLogs directory and its text files into tags directory under '''"prj1.0"'''.
 
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# If final adjustments are needed after these steps, repeat everything from step one but branch the trunk into a new directory in tags as '''prj1.1, prj1.2''', etc.
 
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#:(for marking purposes, your instructor will consider your last revision as your submission)
 
  
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== Resources ==
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= Resources =
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* Software Support
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** [http://developer.nvidia.com/cuda-downloads CUDA Toolkit]
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** Get [https://inside.senecacollege.ca/its/software/index.html Visual Studio 2017] | Select Software Downloads | Go To Visual Studio 2013 Ultimate 2.82GB | Download iso | Burn, if error burn again | Finally, install
  
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<!-- * Class notes can be found here: svn://zenit.senecac.on.ca/oop344 (userid: oop344, no password) -->
 
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* [http://en.wikipedia.org/wiki/Wikipedia:How_to_edit_a_page How To edit Wiki pages]
 
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* [http://en.wikipedia.org/wiki/Wikipedia:Cheatsheet How To edit Wiki Cheatsheet]
 
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* [http://subversion.tigris.org/ Subversion (SVN)]
 
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* [http://tortoisesvn.net/downloads Download Page of TortoiseSVN]
 
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* [http://tortoisesvn.net/docs/release/TortoiseSVN_en/index.html TortoiseSVN Documentation]
 
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* [http://svnbook.red-bean.com/ SVN book at red-bean.com] or download[https://cs.senecac.on.ca/~fardad.soleimanloo/oop344/notes/svn-book.pdf the PDF from here].
 
 
<!--
 
<!--
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* [http://zenit.senecac.on.ca/wiki/index.php/OOP344_Student_Resources#The_Basics_of_IRC IRC Basics]
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** [http://developer.nvidia.com/nvidia-nsight-visual-studio-edition NSight Visual Studio Edition]
 +
** [http://developer.nvidia.com/nsight-eclipse-edition NSight Eclipse Edition]
 +
-->
 +
* Wikis
 +
** [http://en.wikipedia.org/wiki/Wikipedia:How_to_edit_a_page How To edit Wiki pages]
 +
** [http://en.wikipedia.org/wiki/Wikipedia:Cheatsheet How To edit Wiki Cheatsheet]
 +
<!--
 +
* Subversion
 +
** [http://subversion.tigris.org/ Subversion (SVN)]
 +
** [http://tortoisesvn.net/downloads Download Page of TortoiseSVN]
 +
** [http://tortoisesvn.net/docs/release/TortoiseSVN_en/index.html TortoiseSVN Documentation]
 +
** [http://svnbook.red-bean.com/ SVN book at red-bean.com] or download [https://cs.senecac.on.ca/~fardad.soleimanloo/oop344/notes/svn-book.pdf the PDF from here].
 +
** [http://ankhsvn.open.collab.net/ AnkhSVN - Free Visual Studio SVN Integration Alternative To VisualSVN]
 +
-->
  
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* [http://irchelp.org/irchelp/irctutorial.html IRC Tutorial]
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<!--
 +
= Archives =
 
-->
 
-->
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* [http://ankhsvn.open.collab.net/ AnkhSVN - Free Visual Studio SVN Integration Alternative To VisualSVN]
 
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== Examples and In-Class Notes==
 
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== Archives ==
 

Latest revision as of 17:17, 6 January 2019


GPU610/DPS915 | Student List | Group and Project Index | Student Resources | Glossary

Please help make this page resourceful for all GPU610/DPS915 students to use!

Course Material

GPU610 - Parallel Programming Fundamentals

  • Modern GPU (Graphics Processing Unit) technology supports massively parallel computations, which complements the serial processing capabilities of CPU technology. This course teaches students how to read, write, and debug programs that use both CPU and GPU technology. Students learn to reorganize existing programs into serial code that runs on the CPU and parallel code that runs on the GPU. Students also study cases that have benefited from CPU+GPU programming.
  • Course Outline

NV CUDA Teaching Center Small.jpg

DPS915 - Introduction to Parallel Programming

  • Modern GPU (Graphics Processing Unit) technology supports massively parallel computations, which complements the serial processing capabilities of CPU technology. This course teaches students how to read, write, and debug programs that use both CPU and GPU technology. Students learn to reorganize existing programs into serial code that runs on the CPU and parallel code that runs on the GPU. Students also study cases that have benefited from CPU+GPU programming and develop a CPU+GPU application for a client.

NV CUDA Teaching Center Small.jpg

External Links

Workshops

Assignments

  1. Select and Assess
  2. Parallelize
  3. Optimize

Evaluation

  • Assignments and Presentation 20%
  • Workshops 30%
  • Option 1: Tests 50%
  • Option 2: Tests 35% + Exam 15%

Resources

  • Software Support
    • CUDA Toolkit
    • Get Visual Studio 2017 | Select Software Downloads | Go To Visual Studio 2013 Ultimate 2.82GB | Download iso | Burn, if error burn again | Finally, install


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