High Performance Scientific Computing

开始时间: 04/22/2022 持续时间: 10 weeks

所在平台: CourseraArchive

课程类别: 信息，技术与设计

大学或机构: University of Washington（华盛顿大学）

授课老师： Randall J. LeVeque

课程主页: https://www.coursera.org/course/scicomp

课程评论: 1 个评论

评论课程关注课程

课程简介

Programming-oriented course on effectively using modern computers to solve scientific computing problems arising in the physical/engineering sciences and other fields. Provides an introduction to efficient serial and parallel computing using Fortran 90, OpenMP, MPI, and Python, and software development tools such as version control, Makefiles, and debugging.

课程大纲

The use of a variety of languages and techniques will be integrated throughout the course as much as possible, rather than taught linearly. The topics below will be covered at an introductory level, with the goal of learning enough to feel comfortable starting to use them in your everyday work. Once you've reached that level, abundant resources are available on the web to learn the more advanced features that are most relevant for you.

Working at the command line in Unix-like shells (e.g. Linux or a Mac OSX terminal).
Version control systems, particularly git, and the use of Github and Bitbucket repositories.
Work habits for documentation of your code and reproducibility of your results.
Interactive Python using IPython, and the IPython Notebook.
Python scripting and its uses in scientific computing.
Subtleties of computer arithmetic that can affect program correctness.
How numbers are stored: binary vs. ASCII representations, efficient I/O.
Fortran 90, a compiled language that is widely used in scientific computing.
Makefiles for building software and checking dependencies.
The high cost of data communication. Registers, cache, main memory, and how this memory hierarchy affects code performance.
OpenMP on top of Fortran for parallel programming of shared memory computers, such as a multicore laptop.
MPI on top of Fortran for distributed memory parallel programming, such as on a cluster.
Parallel computing in IPython.
Debuggers, unit tests, regression tests, verification and validation of computer codes.
Graphics and visualization of computational results using Python.

课程评论(1条)

1	钛合金蛙眼 2013-07-09 11:10 1 票支持; 0 票反对很实用，内容包括版本控制(git)，语言(fortran, python)，编译调试(makefile)，并行计算(openmp, mpi)，还有一部分科学计算（蒙特卡罗)。十分适合用来学习了解诸多知识点。Coursera上没有编程作业，但是他提供的课程网站有homework（课程是在C站和学校同步开授的)，时间原因没全跟下来，比较花时间。uw开的另一门课程 introduction to data science，也是一门实用的杂烩式课程