Computation and simulation are increasingly important in all aspects of
science and engineering. At the same time writing efficient computer programs
to take full advantage of current computers is becoming increasingly difficult.
Even laptops now have 4 or more processors, but using them all to solve
a single problem faster often requires rethinking the algorithm to introduce
parallelism, and then programming in a language that can express this parallelism.
Writing efficient programs also requires some knowledge of machine arithmetic,
computer architecture, and memory hierarchies.
Although parallel computing will be covered, this is not a class
on the most advanced techniques for using supercomputers, which these days
have tens of thousands of processors and cost millions of dollars. Instead,
the goal is to teach tools that you can use immediately on your own laptop,
desktop, or a small cluster. Cloud computing will also be discussed, and
students who don't have a multiprocessor computer of their own will still
be able to do projects using Amazon Web Services at very low cost.
Along the way there will also be discussion of software engineering tools
such as debuggers, unit testing, Makefiles, and the use of version control
systems. After all, your time is more valuable than computer time, and
a program that runs fast is totally useless if it produces the wrong results.
High performance programming is also an important aspect of high
performance scientific computing, and so another main theme of the course
is the use of basic tools and techniques to improve your efficiency as
a computational scientist.
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
Version control systems, particularly git, and the use of Github and Bitbucket
Work habits for documentation of your code and reproducibility of your
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
Graphics and visualization of computational results using Python.