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
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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
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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:
- Shane Cook, CUDA Programming: A Developers Guide to Parallel Computing with GPUs (Applications of GPU Computing), First Edition, Morgan Kaufmann, 2012.
- David R. Kaeli, Perhaad Mistry, Dana Schaa, Dong Ping Zhang, Heterogeneous computing with OpenCL, 3rd Edition, Morgan Kauffman, 2015.
References:
- Nicholas Wilt, CUDA Handbook: A Comprehensive Guide to GPU Programming, Addison -Wesley, 2013.
- Jason Sanders, Edward Kandrot, CUDA by Example: An Introduction to General Purpose GPU Programming^, Addison – Wesley, 2010.
- David B. Kirk, Wen-mei W. Hwu, Programming Massively Parallel Processors – A Hands-on Approach, Third Edition, Morgan Kaufmann, 2016.
- http://www.nvidia.com/object/cuda_home_new.html
- 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.
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