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CSE3201: PROGRAMMING MASSIVELY PARALLEL PROCESSORS
Core/Elective: Core Semester: 2 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 introducesparallel computing with a strong emphasis on programming.

Course Objectives

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

Course Content

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.

REFERNCES

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, MorganKaufmann, 2012.
4. Jason Sanders, Edward Kandrot, CUDA by Example: An Introduction to General-Purpose GPU Programming, Addison-Westley Professional, 2010.


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