8th Sem, C&C

Gpu Architecture and Programming C&C 8th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective V)

Gpu Architecture and Programming C&C 8th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective V) detail syllabus for Computer & Communication Engineering (C&C), 2017 regulation is collected from the Anna Univ official website and presented for students of Anna University. The details of the course are: course code (CS8076), Category (PE), Contact Periods/week (3), Teaching hours/week (3), Practical Hours/week (0). The total course credits are given in combined syllabus.

For all other c&c 8th sem syllabus for be 2017 regulation anna univ you can visit C&C 8th Sem syllabus for BE 2017 regulation Anna Univ Subjects. For all other Professional Elective V subjects do refer to Professional Elective V. The detail syllabus for gpu architecture and programming is as follows.

Course Objective:

  • To understand the basics of GPU architectures
  • To write programs for massively parallel processors
  • To understand the issues in mapping algorithms for GPUs
  • To introduce different GPU programming models

Unit I

For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.

Unit II

Cuda Programming
Using CUDA – Multi GPU – Multi GPU Solutions – Optimizing CUDA Applications: Problem Decomposition, Memory Considerations, Transfers, Thread Usage, Resource Contentions.

Unit III

Programming Issues
Common Problems: CUDA Error Handling, Parallel Programming Issues, Synchronization, Algorithmic Issues, Finding and Avoiding Errors.

Unit IV

For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.

Unit V

Algorithms On Gpu
Parallel Patterns: Convolution, Prefix Sum, Sparse Matrix – Matrix Multiplication – Programming Heterogeneous Cluster.

Course Outcome:

Upon completion of the course, the students will be able to

  • Describe GPU Architecture
  • Write programs using CUDA, identify issues and debug them
  • Implement efficient algorithms in GPUs for common application kernels, such as matrix multiplication
  • Write simple programs using OpenCL
  • Identify efficient parallel programming patterns to solve problems

Text Books:

  1. Shane Cook, CUDA Programming: A Developers Guide to Parallel Computing with GPUs (Applications of GPU Computing), First Edition, Morgan Kaufmann, 2012.
  2. David R. Kaeli, Perhaad Mistry, Dana Schaa, Dong Ping Zhang, Heterogeneous computing with OpenCL, 3rd Edition, Morgan Kauffman, 2015.

References:

  1. Nicholas Wilt, CUDA Handbook: A Comprehensive Guide to GPU Programming, Addison -Wesley, 2013.
  2. Jason Sanders, Edward Kandrot, CUDA by Example: An Introduction to General Purpose GPU Programming^, Addison – Wesley, 2010.
  3. David B. Kirk, Wen-mei W. Hwu, Programming Massively Parallel Processors – A Hands-on Approach, Third Edition, Morgan Kaufmann, 2016.
  4. http://www.nvidia.com/object/cuda_home_new.html
  5. http://www.openCL.org

For detail syllabus of all other subjects of BE C&C, 2017 regulation do visit C&C 8th Sem syllabus for 2017 Regulation.

Dont forget to download iStudy for latest syllabus and results, class timetable and more.

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