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  Home > Academic/Programmes > Programme Structure > CSE (2011)
Core/Elective: Elective Semester: 3 Credits: 4
Course Description

It used to be the case that parallel computing was confined to giant supercomputers. But nowadays it is literally everywhere – even in the small mobile handsets that most of us carry around. This course introduces parallel computing with a strong emphasis on programming.

Course Objectives

1. To understand the basics of parallel computing.
2. To develop programming skills required for parallel computing.


1. Introduction – parallel computing – more speed or parallelism – languages and models – sequential vs parallel – concurrent, parallel, distributed – parallel hardware architecture – modifications to the von Neumann model

2. Evolution of GPU – GPGPU – introduction to data parallelism – CUDA program structure – vector addition kernel – device global memory and data transfer

3. CUDA thread organization – mapping threads to multi-dimensional data – assigning resources to blocks – synchronization and transparent scalability – thread scheduling and latency tolerance

4. Memory access efficiency – CUDA device memory types – performance considerations – global memory bandwidth – instruction mix and thread granularity – floating point considerations

5. Parallel programming patterns – convolution – prefix sum – sparse matrix and vector multiplication – application case studies – strategies for solving problems using parallel programming


[1] David B. Kirk, Wen-mei W Hwu Programming Massively Parallel Processors, 2nd Edition, Morgan Kaufmann, 2012.
[2] Peter Pacheco, Introduction to Parallel Programming, Morgan Kaufmann, 2011.
[3] Shane Cook, CUDA Programming: A Developer’s Guide to Parallel Computing with GPUs, Morgan Kaufmann, 2012.
[4] Jason Sanders, Edward Kandrot, CUDA by Example: An Introduction to General-Purpose GPU Pro- gramming, Addison-Westley Professional, 2010.

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