查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

113-2 342306 平行運算 1 3.0 3 ★ 楊元森防災所 8 0 土木208

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
113-2	342306	平行運算	1	3.0	3	★	楊元森	防災所	8	0	土木208

教學大綱與進度：

教師姓名楊元森　教師諮商時間(Office Hours)

Email ysyang@ntut.edu.tw

最後更新時間 2025-01-10 15:01:41

課程大綱 This course covers the basic concepts, techniques, and technologies involved in parallel computing as well as programming exercises. Emphasis will be placed on understanding the importance of parallel computing in modern scientific computing and artificial intelligence (AI) applications. The course will introduce students to various parallel programming models and tools, including C/C++ programming, multi-processing in Python, OpenMP (in C) for multi-threading, and GPU programming using CUDA and OpenCL. Web-based programming using JavaScript will be included for user interface of some hands-on exercises. No prior programming experience is required.

課程進度 W1-2: Introduction to Parallel Computing Overview of Parallel Computing: History, applications, and relevance in modern computing. Importance in Scientific Computing and AI: How parallelism drives advancements in simulation, data analysis, and machine learning. Basic Concepts: Types of parallelism (data parallelism, task parallelism), and understanding the computational model. Performance Metrics: Speedup, efficiency, and Amdahl's Law. Exercise: Parallel procedural design, diagram representation, speed-up/performance analysis of summing N numbers Exercise: Parallel procedural design, diagram representation, speed-up/performance analysis of vector addition Exercise: Parallel procedural design, diagram representation, speed-up/performance analysis of matrix multiplication W3-4: Parallel Computing in C Programming Introduction to C for Parallel Programming: Basics of C, memory management, and multi-core programming. Shared Memory and Multi-threading: Using threads and synchronization in C. Hands-on Exercises: Writing and testing parallel programs in C. W5-6: Web-based Parallel Programming with JavaScript JavaScript and Asynchronous Programming: Event loop, Promises, and asynchronous functions. Web Workers: Introduction to parallelism in web browsers using JavaScript. Hands-on Exercises: Implementing parallelism in web applications using JavaScript. W7-8: Multi-processing in Python Python for Parallel Computing: Introduction to the Python multiprocessing module. Process-based Parallelism: Differences between threading and multiprocessing in Python. Hands-on Exercises: Implementing parallel algorithms using Python for large-scale data processing. W10-11: Module 5: Multi-threading with OpenMP Introduction to OpenMP: Basics of multi-threading in shared memory systems. Parallel Directives: Using OpenMP directives to parallelize loops and sections of code. Optimization Techniques: Strategies to improve parallel performance. Hands-on Exercises: Writing parallel programs with OpenMP in C/C++. W12-13: Introduction to GPU Programming The Basics of GPU Computing: Understanding GPU architecture, SIMD (Single Instruction, Multiple Data) model. CUDA Programming: Introduction to CUDA, writing and optimizing kernel functions. OpenCL Overview: General-purpose GPU programming with OpenCL. Hands-on Exercises: Simple GPU-accelerated programs using CUDA and OpenCL. Module 14-16: Advanced Topics and Future Directions Scalable Parallel Systems: Clusters, grids, and cloud computing. Parallel Algorithms: Techniques and frameworks for designing efficient parallel algorithms. Emerging Technologies in AI: The role of parallel computing in deep learning and big data analytics. Research Trends: Future developments in parallel computing and hardware. W17-18: Final Project Presentations

評量方式與標準 Midterm (programming) 30% +/- 5% Proposal presentation 15% +/- 5% Final presentation and report 40% +/- 5% Misc. 15% +/- 5%

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：是
Grama, A. (Ed.). (2003). Introduction to parallel computing. Pearson Education. https://hpc.llnl.gov/documentation/tutorials/introduction-parallel-computing-tutorial

課程諮詢管道 8:00-9:00 AM. Monday to Thursday.
8:00-10:00 AM. Friday.
Reservation through email is required (email:ysyang@ntut.edu.tw)

課程對應SDGs指標 SDG11：永續城市與社區（Sustainable Cities and Communities）

課程是否導入AI

備註
The lecture is regularly video recorded and is available at the NTUT iStudy (北科 i 學園). In case that the lecture has to be carried out online, it will be at https://meet.google.com/yiu-jwqa-ckm

教師姓名	楊元森　教師諮商時間(Office Hours)
Email	ysyang@ntut.edu.tw
最後更新時間	2025-01-10 15:01:41
課程大綱	This course covers the basic concepts, techniques, and technologies involved in parallel computing as well as programming exercises. Emphasis will be placed on understanding the importance of parallel computing in modern scientific computing and artificial intelligence (AI) applications. The course will introduce students to various parallel programming models and tools, including C/C++ programming, multi-processing in Python, OpenMP (in C) for multi-threading, and GPU programming using CUDA and OpenCL. Web-based programming using JavaScript will be included for user interface of some hands-on exercises. No prior programming experience is required.
課程進度	W1-2: Introduction to Parallel Computing Overview of Parallel Computing: History, applications, and relevance in modern computing. Importance in Scientific Computing and AI: How parallelism drives advancements in simulation, data analysis, and machine learning. Basic Concepts: Types of parallelism (data parallelism, task parallelism), and understanding the computational model. Performance Metrics: Speedup, efficiency, and Amdahl's Law. Exercise: Parallel procedural design, diagram representation, speed-up/performance analysis of summing N numbers Exercise: Parallel procedural design, diagram representation, speed-up/performance analysis of vector addition Exercise: Parallel procedural design, diagram representation, speed-up/performance analysis of matrix multiplication W3-4: Parallel Computing in C Programming Introduction to C for Parallel Programming: Basics of C, memory management, and multi-core programming. Shared Memory and Multi-threading: Using threads and synchronization in C. Hands-on Exercises: Writing and testing parallel programs in C. W5-6: Web-based Parallel Programming with JavaScript JavaScript and Asynchronous Programming: Event loop, Promises, and asynchronous functions. Web Workers: Introduction to parallelism in web browsers using JavaScript. Hands-on Exercises: Implementing parallelism in web applications using JavaScript. W7-8: Multi-processing in Python Python for Parallel Computing: Introduction to the Python multiprocessing module. Process-based Parallelism: Differences between threading and multiprocessing in Python. Hands-on Exercises: Implementing parallel algorithms using Python for large-scale data processing. W10-11: Module 5: Multi-threading with OpenMP Introduction to OpenMP: Basics of multi-threading in shared memory systems. Parallel Directives: Using OpenMP directives to parallelize loops and sections of code. Optimization Techniques: Strategies to improve parallel performance. Hands-on Exercises: Writing parallel programs with OpenMP in C/C++. W12-13: Introduction to GPU Programming The Basics of GPU Computing: Understanding GPU architecture, SIMD (Single Instruction, Multiple Data) model. CUDA Programming: Introduction to CUDA, writing and optimizing kernel functions. OpenCL Overview: General-purpose GPU programming with OpenCL. Hands-on Exercises: Simple GPU-accelerated programs using CUDA and OpenCL. Module 14-16: Advanced Topics and Future Directions Scalable Parallel Systems: Clusters, grids, and cloud computing. Parallel Algorithms: Techniques and frameworks for designing efficient parallel algorithms. Emerging Technologies in AI: The role of parallel computing in deep learning and big data analytics. Research Trends: Future developments in parallel computing and hardware. W17-18: Final Project Presentations
評量方式與標準	Midterm (programming) 30% +/- 5% Proposal presentation 15% +/- 5% Final presentation and report 40% +/- 5% Misc. 15% +/- 5%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：是 Grama, A. (Ed.). (2003). Introduction to parallel computing. Pearson Education. https://hpc.llnl.gov/documentation/tutorials/introduction-parallel-computing-tutorial
課程諮詢管道	8:00-9:00 AM. Monday to Thursday. 8:00-10:00 AM. Friday. Reservation through email is required (email:ysyang@ntut.edu.tw)
課程對應SDGs指標	SDG11：永續城市與社區（Sustainable Cities and Communities）
課程是否導入AI
備註	The lecture is regularly video recorded and is available at the NTUT iStudy (北科 i 學園). In case that the lecture has to be carried out online, it will be at https://meet.google.com/yiu-jwqa-ckm