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CSE811 Parallel Computing for Heterogeneous Platforms
Campus
RCMS
Programs
PG
Session
Spring Semester 2017
Course Title
Parallel Computing for Heterogeneous Platforms
Course Code
CSE811
Credit Hours
30
PreRequisutes
CSE882 Computing for CSE
Course Objectives
After completing this course, the students would be able to program cluster of multicore systems and GPUs using various parallel programming models. Moreover, they would be able to apply parallel computing techniques to accelerate computationally intensive problems, involving matrixvector computations, string matching and graph algorithms on emerging HPC platforms.
Detail Content
Introduction to Parallel Computer Architecture
Parallel Programming Models
Programming Cluster of Multicore processors using Hybrid MPI/OpenMP approach
Handling Synchronization in MPI/OpenMP programs
Introduction to GPU Computing, Architecture and Programming Models
CUDA C and Kernel based Programming
Memory Model for locality, parallel convolution pattern
Performance Considerations with programming assignment of simple matrixmatrix multiplication in CUDA C
Data transfer and task parallelism in CUDA C
OpenCL and C++AMP
Introduction to OpenACC, with programming assignment of vector addition using streams in CUDA C
Parallel Algorithms for solving linear system of equations
Parallel Algorithms for Graph processing & String matching problem
Text/Ref Books
Jason Sanders, Edward Kandrot , CUDA by Example.
David B. Kirk, Wenmei W. Hwu, Programming Massively Parallel Processors A HandsOn Approach
Jung W. Suh, Yongmin Kim, Accelerating MATLAB with GPU Computing A Primer with Examples
Time Schedule
Spring Semester 2017
Faculty/Resource Person
Mr. Tariq Saeed
MS (National University of Science and Technology (NUST)
Discipline: Information Security
Specialization: High Performance Computing, Model Checking