數值軟體開發(英文授課)

This course discusses the art to build numerical software, i.e., computer programs applying numerical methods for solving mathematical or physical problems. We will be using the combination of Python and C++ and related tools (e.g., bash, git, make, etc.) to learn the modern development processes. By completing this course, students will acquire the fundamental skills for developing modern numerical software. Cherk course notes for details: https://github.com/yungyuc/nsd/tree/master/notebook/20sp_nctu .

This is a graduate or senior level course open to students who have taken computer architecture, engineering mathematics or equivalents. Working knowledge of Linux and Unix-like is required. Prior knowledge to numerical methods is recommended. The instructor uses English in the lectures and discussions.

The instructor will use English. It is OK for students to use Mandarin in the class, but English is preferred. Computer program for homework and project should be developed against the latest Ubuntu LTS system. AWS Machine Image (AMI) and AWS educate will be used.

* You are expected to learn programming languages yourself. Python is never a problem, but you could find it challenging to self-teach C++. Students are encouraged to form study groups for practicing C++, and discuss with the instructor and/or the teaching assistant. * Grading: homework 30%, mid-term exam: 30%, term project: 40%. * There are 12 to 14 lectures for the subjects of numerical software developing using Python and C++. * There will be 6 homework assignments for you to exercise. Programming in Python and/or C++ is required. * Mid-term examination will be conducted to assess students' understandings to the analytical materials. * Term project will be used to assess students' overall coding skills. Presentation is required. Failure to present results in 0 point for this part.

• Introduction
• Fundamental engineering practices
• Python and numpy
• C++ and computer architecture
• Matrix operations
• Cache optimization
• SIMD (vector processing)
• Memory management
• Smart pointers
• Modern C++
• C++ and C for Python
• Array code in C++
• Array-oriented design
• Advanced Python

Textbook: None References: * Computer Systems: A Programmer's Perspective: https://csapp.cs.cmu.edu/ * Python documentation: https://docs.python.org/3/ * Cppreference: https://en.cppreference.com/ * Effective Modern C++, Scott Meyer, O'Reilly, 2014 * Source code: cpython, numpy, xtensor, and pybind11

地點

By appointment

時間

N/A

聯絡方式

Send email to yyc at solvcon.net, with "[nsd]" prefix in the subject line.