Difference between revisions of "GPU621/DPS921"

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{{GPU621/DPS921 Index | 20167}}
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{{GPU621/DPS921 Index | 20187}}
  
 
Please help make this page resourceful for all GPU621/DPS921 students to use!
 
Please help make this page resourceful for all GPU621/DPS921 students to use!
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*# [https://scs.senecac.on.ca/~gpu621/pages/workshops/w5.html Threading Building Blocks]
 
*# [https://scs.senecac.on.ca/~gpu621/pages/workshops/w5.html Threading Building Blocks]
 
*# [https://scs.senecac.on.ca/~gpu621/pages/workshops/w6.html Vectorization]
 
*# [https://scs.senecac.on.ca/~gpu621/pages/workshops/w6.html Vectorization]
*# [https://scs.senecac.on.ca/~gpu621/pages/workshops/w7.html Data Decomposition]
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*# [https://scs.senecac.on.ca/~gpu621/pages/workshops/w7.html Fork Join]
*# [https://scs.senecac.on.ca/~gpu621/pages/workshops/w8.html Scatter Gather]
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*# [https://scs.senecac.on.ca/~gpu621/pages/workshops/w8.html Data Decomposition]
*# [https://scs.senecac.on.ca/~gpu621/pages/workshops/w9.html Fork Join]
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*# [https://scs.senecac.on.ca/~gpu621/pages/workshops/w9.html Scatter Gather]
 
*# [https://scs.senecac.on.ca/~gpu621/pages/workshops/w10.html Optimization]
 
*# [https://scs.senecac.on.ca/~gpu621/pages/workshops/w10.html Optimization]
* Grading - The due date for each workshop is noted in MySeneca.  The penalty for late submission is 50% of the workshop mark.
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* Grading - The due date for each workshop is noted in MySeneca.  The penalty for late submission is 30% of the workshop mark.
  
 
== Evaluation ==
 
== Evaluation ==
  
* Assignments and Presentation 30%
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* Project 20%
* Workshops 20%
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* Workshops 30%
* Test 20%
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* Tests without Final Exam 50%
* Exam 30%
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* Tests with Final Exam 35%
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* Final Exam (optional) 15%
  
 
= Resources =
 
= Resources =

Revision as of 22:07, 9 September 2018


GPU621/DPS921 | Participants | Groups and Projects | Resources | Glossary

Please help make this page resourceful for all GPU621/DPS921 students to use!

Course Material

GPU621/DPS921 - Parallel Algorithms and Programming Techniques

  • Large data and compute-intensive problems benefit from parallel solutions.  Modern hardware has parallel processing capabilities in varying degrees.  Students study a set of industry-standard parallel patterns and learn how to implement parallel algorithms on multi-processor accelerators, shared memory systems and distributed systems using a range of programming models.
  • GPU621 Course Outline
  • DPS921 Course Outline

Instructor

External Links

Workshops

Evaluation

  • Project 20%
  • Workshops 30%
  • Tests without Final Exam 50%
  • Tests with Final Exam 35%
  • Final Exam (optional) 15%

Resources

  • Current Software Support
    • Matrix
      • Cilk Plus - /usr/local/gcc/gcc-cilk/bin/g++ - version 4.9.0 experimental
      • TBB - /usr/local/gcc/tbb - version 4.1 update 4 (20130613)
    • T2108
      • Matrix Image (as above)
      • CUDA 5.5 July 2013 - supports Visual Studio 2012