數值軟體開發(英文授課)

The course introduces the art of building numerical software, i.e., computer programs applying numerical methods for solving mathematical, scientfic, or engineering problems. We will be using Python, C++ and other 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. Check course notes for details: https://yyc.solvcon.net/en/latest/nsd/ .

Students should have taken computer architecture and 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.

無備註

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.

* You are expected to learn programming languages yourself. Python is easy, but C++ may be challenging. 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 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

  What is numerical software.

• Fundamental engineering

  A large chunk of efforts is spent in the infrastructure for coding. The key to the engineering system is automation.

• Python and numpy

  Python is a popular choice for the scripting engine that makes the numerical software work as a platform.

• C++ and computer architecture

  The low-level code of numerical software must be high-performance. The industries chose C++ because it can take advantage of everything that a hardware architecture offers while using any level of abstraction.

• Matrix operations

  Matrices are everywhere in numerical analysis. Arrays are the fundamental data structure and used for matrix-vector, matrix-matrix, and other linear algebraic operations.

• Cache optimization

  How cache works, its importance to performance, and optimization with cache.

• SIMD (vector processing)

  Parallelism and x86 assembly for SIMD.

• Memory management

  Numerical software tends to use as much memory as a workstation has. The memory has two major uses: (i) to hold the required huge amount of data, and (ii) to gain speed.

• Ownership and smart pointers

  Ownership and memory management using C++ smart pointers.

• Modern C++

  Copy elision and move semantics. Variadic template and perfect forwarding. Closure.

• C++ and C for Python

  Use C++ and C to control the CPython interpreter.

• Array code in C++

  Dissect the array-based code and element-based code and when to use them.

• Advanced Python

  Advanced topics in Python programming.

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-course]" prefix in the subject line.