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GPU610/DPS915

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{{GPU610/DPS915 Index | 2013120191}}
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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.
* [https://scsict.senecac.onsenecacollege.ca/course/gpu610 Course Outline]
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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 and develop a CPU+GPU application for a client.
* [https://scsict.senecac.onsenecacollege.ca/course/dps915 Course Outline]
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== The Workshops ==* 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=342 536 Moodle].
* Detail Specifications
*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w1.html Initial AssessmentProfile]*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w2.html Linear Algebra using BLAS]
*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w3.html Device Query and Selection]
*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w4.html A Simple Device OperationcuBLAS]*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w5.html Matrix Product using cuBlasThrust]*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w6.html Matrix Product using ThrustA Simple Kernel]*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w7.html Dot ProductReduction]*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w8.html Matrix ProductThread Divergence]*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w9.html Matrix Product using StreamsCoalesced Memory Access]
*# [https://scs.senecac.on.ca/~gpu610/pages/workshops/w10.html CUDA to OpenCL]
* Grading - The window due date for submission of each workshop is one week plus a day from the date of the workshop periodnoted in MySeneca. The penalty for late submission is 5020% of the workshop mark; 50% for very late submission.
== The Assignments ==# [https://scs.senecac.on.ca/~gpu610/pages/assignments/a1.html Selection Select and AssessmentAssess]# [https://scs.senecac.on.ca/~gpu610/pages/assignments/a2.html GPU ProgrammingParallelize]# [https://scs.senecac.on.ca/~gpu610/pages/assignments/a3.html OptimizationOptimize]
== Evaluation ==
* Assignments and Presentation 3020%* Workshops 2030%* Test 20Option 1: Tests 50%* Option 2: Tests 35% + Exam 3015%
= Resources =
* Software Support
** [http://developer.nvidia.com/cuda-downloads CUDA Toolkit]
** Get [https://acsinside.senecac.onsenecacollege.ca/pagesits/software/index.php Microsoft html Visual Studio 2010 Pro2017]| Select Software Downloads | Go To Visual Studio 2013 Ultimate 2.82GB | Download iso | Burn, if error burn again | Finally, install 
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** [http://developer.nvidia.com/nvidia-nsight-visual-studio-edition NSight Visual Studio Edition]
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