Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2019 257992 Parallel Computing 1 3.0 3 ★ Yuan-Sen Yang GCDPE 13 0 上限15人

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2019	257992	Parallel Computing	1	3.0	3	★	Yuan-Sen Yang	GCDPE	13	0	上限15人

Course Plan：

Instructor Yuan-Sen Yang

Email ysyang@ntut.edu.tw

Last Updated 2019-06-14 09:19:46

Course Objective Complicated scientific computing, huge data processing, and image analysis intense the requirement of high-performance computing knowledge in the computer applications in engineering. Current popular computer architectures, in terms of high performance computing, are different from those a few decades ago, especially the shared-data architectures and GPU architectures in medium scale problems are getting much popular and widely used in practical applications. This course mainly focuses on OpenMP in details for shared memory architectures with C++ implementations and coding exercises of related scientific problems, as well as introduction to message passing interface (MPI) for distributed memory architectures and CUDA for GPU architectures. The problems include heat transfer problems, image processing and analysis, and matrix multiplications.

Course Schedule Current progress: W01: History and impacts of high-performance computing W02: Introduction to OpenMP and its exercise (I) W03: Introduction to OpenMP and its exercise (II) W04: Introduction to heat problems, from mathematical partial equations to computer solutions. W05: Introduction to dynamic arrays and OpenCV for matrix operations and visualization. W06: Heat problem and programming W07: OpenMP parallelization exercises W08: 2D visualization and image manipulation W09: Midterm W10: Image searching and matching with parallelization W11: Complexity analysis of summation on grid mesh architecture W12: Message-passing interface (MPI) and matrix multiplication exercise W13: GPU/CUDA and matrix multiplication W14: Midterm presentations W15: Graph partitioning W16: Graph partitioning W17: Final presentations W18: Technical report writing ---- W01: History and impacts of high-performance computing W02 ~ 05: Exercises of highrise building dynamic analysis W02: Adding N numbers using P processors W03: Adding N numbers using P processors (2) W04: Impact of HPC to engineering W05: Matrix multiplication (1): From introduction to Hilbert matrix implementation W06: Matrix multiplication (2): programming and timing demonstration W07 ~ 08: Multi-core computing for highrise building dynamic analysis W08: Distributed computing for highrise building dynamic analysis W09: Midterm (either close or open book, or take home) W11 ~ 12: Exercises of flood analysis W13: Multi-core computing for flood analysis W14 ~ 15: Distributed computing for flood analysis W16 ~ 17: Final presentation discussion W18: Final presentation

Evaluation and grading policy Midterm (programming) 30% +/- 5% Proposal presentation 15% +/- 5% Final presentation and report 40% +/- 5% Misc. 15% +/- 5%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Grama, A. (Ed.). (2003). Introduction to parallel computing. Pearson Education.

The access to curricular consultation

Note
　

Instructor	Yuan-Sen Yang
Email	ysyang@ntut.edu.tw
Last Updated	2019-06-14 09:19:46
Course Objective	Complicated scientific computing, huge data processing, and image analysis intense the requirement of high-performance computing knowledge in the computer applications in engineering. Current popular computer architectures, in terms of high performance computing, are different from those a few decades ago, especially the shared-data architectures and GPU architectures in medium scale problems are getting much popular and widely used in practical applications. This course mainly focuses on OpenMP in details for shared memory architectures with C++ implementations and coding exercises of related scientific problems, as well as introduction to message passing interface (MPI) for distributed memory architectures and CUDA for GPU architectures. The problems include heat transfer problems, image processing and analysis, and matrix multiplications.
Course Schedule	Current progress: W01: History and impacts of high-performance computing W02: Introduction to OpenMP and its exercise (I) W03: Introduction to OpenMP and its exercise (II) W04: Introduction to heat problems, from mathematical partial equations to computer solutions. W05: Introduction to dynamic arrays and OpenCV for matrix operations and visualization. W06: Heat problem and programming W07: OpenMP parallelization exercises W08: 2D visualization and image manipulation W09: Midterm W10: Image searching and matching with parallelization W11: Complexity analysis of summation on grid mesh architecture W12: Message-passing interface (MPI) and matrix multiplication exercise W13: GPU/CUDA and matrix multiplication W14: Midterm presentations W15: Graph partitioning W16: Graph partitioning W17: Final presentations W18: Technical report writing ---- W01: History and impacts of high-performance computing W02 ~ 05: Exercises of highrise building dynamic analysis W02: Adding N numbers using P processors W03: Adding N numbers using P processors (2) W04: Impact of HPC to engineering W05: Matrix multiplication (1): From introduction to Hilbert matrix implementation W06: Matrix multiplication (2): programming and timing demonstration W07 ~ 08: Multi-core computing for highrise building dynamic analysis W08: Distributed computing for highrise building dynamic analysis W09: Midterm (either close or open book, or take home) W11 ~ 12: Exercises of flood analysis W13: Multi-core computing for flood analysis W14 ~ 15: Distributed computing for flood analysis W16 ~ 17: Final presentation discussion W18: Final presentation
Evaluation and grading policy	Midterm (programming) 30% +/- 5% Proposal presentation 15% +/- 5% Final presentation and report 40% +/- 5% Misc. 15% +/- 5%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Grama, A. (Ed.). (2003). Introduction to parallel computing. Pearson Education.
The access to curricular consultation
Note