P573: Introduction to Scientific Computing
P573, Introduction to Scientific Computing - Fall 2016
8:00 - 9:15 AM, Monday and Wednesday
109 Ballantine Hall
Not all of these exist yet, but will be filled in as the semester trudges
- Randall Bramley
- 301A Lindley Hall
- Office hours: Monday to Thursday: 1:30 PM - 5:00 PM or by appointment
- Ashish Abraham
- 301 Lindley Hall
- Office hours: Tuesday 11am-12pm, Friday 11am-12pm
Mathematics M343 and one of M303 or M301, and a working knowledge of
C or C++ or Fortran. The requirement in mathematics means
Because this is the one area that raises most questions,
the computational requirements include:
- At least the definition of a derivative from calculus
- Basic linear algebra, for example,
- how to add and multiply vectors and matrices
- how to compute an inner prodct, also called a dotproduct
- how to multiply a matrix times a vector, both mathematically and in
one of the languages C or C++ or Fortran
- what the null space and range space of a matrix are
- how to multiply two conformal matrices (and what
"conformal" means in matrix multiplication!)
- what an upper (or lower) triangular matrix is
Read this more detailed specification to
see if you have the kind of math and coding abilities to succeed in P573. This is
not a test to hand in; it is strictly for your own edification.
- The ability to write and run programs under a UNIX operating system, in
one of the languages C, C++, or Fortran
- The ability to write functions to a specified interface
- How to allocate memory, define and use arrays
- The ability to create executables involving multiple files
and libraries either by a script or a makefile
- Write programs that read and write formatted data from and to files
No textbook will be required or followed. The following may be useful references,
but only for small parts of the course material. Math/CS books are expensive and CS
books are quickly outdated. Don't go out and buy any of the following until/unless
you are sure you would find them useful, and don't use them as a measure of what is
going to be in the course. As an example the first two references are mathematical
and emphasize the derivation and error analysis of numerical linear algebraic
algorithms - but all we will need and use are some of the algorithms themselves,
the implementation details, and occasionally the results of the analyses.
The rest is better addressed in a numerical analysis class.
In general all of the material you need will be presented in class, is available
via the Web, or (in the case of Matlab) through Matlab's
help, lookfor, and other commands.
Introduction to Matrix Computations by G.W. Stewart
is just what the name says; it gives the basic linear algebra ideas required.
Matrix Computations by Golub and van Loan is
a more definitive but higher-level reference work, with numerical
linear algebra algorithms clearly stated in an implementable form.
High Performance Computing by Kevin Dowd
covers some performance optimization and measurement techniques.
Mastering Matlab by Hanselman and Littlefield is one of the
more definitive reference books for Matlab, and the one I use the most for my own
coding and debugging sessions. Matlab will be used for analyzing and plotting
results from some of the assignments. For the most part scripts will be provided
and they will also run using Octave, an open source Matlab wannabe.
Since this is not being used as a textbook, you can save money by getting an
earlier edition (but get one for Matlab version 6 or higher).
Better yet, don't buy a book at all. Matlab has extensive on and off-line
documentation and help that you don't need a book.
If you have access or ownership of a Unix-based system in your lab or office, use it.
You'll want to develop the tools on the system you use in real life anyway.
Otherwise all CS students have accounts on the silo Linux system.
Students from outside the CS department need to give me their UITS
login to have accounts created. If you use UITS platforms, they
may be research computers and require some professor to sponsor the account,
unless you already have one.
If your lab or research group has other machines, use them as well -
in the long run, those are the machines to learn how to use effectively.
However, assignments will require your codes to run on silo, to avoid issues
of trying to test and grade codes running on potentially dozens of different
Open to students from all scientific, engineering, and mathematical disciplines,
this course provides an overview of computer hardware, software, and numerical
methods that are useful on scientific workstations and supercomputers.
Topics include high-performance computer architectures, software tools
and packages, characteristics of commonly used numerical methods, graphical
presentation of results, and performance analysis and improvement.
The course is not the same as numerical analysis, which concentrates
on the study of convergence, stability, and error analysis in numerical
methods. For that, students should take Math 571-572. Although numerical
analysis is an important component of scientific computing, it is only
a part of the field. Instead, this course concentrates on
practical implementation of solutions of scientific problems on computers
the efficient mapping of solution methods to modern architectures
software tools and methods useful in modern scientific computing.
the basic foundations of performance analysis, modeling,
and prediction (together, called performance engineering)
The last item is the most important one. The single most fundamental skill
you will need to master is load-store analysis, and that is the
pass/fail criterion. Secondary tools and skills include how to recognize in practice
when problems in floating point arithmetic occur, how to write code that gives
scientifically reproducible results, how to efficiently implement
linear algebraic computational operations, and how to time and profile parts of codes.
P573 is not parallel computing.
Other courses including CSci
B673 concentrates on that aspect of scientific computing.
Some basic Matlab is coverd in the course,
a language that provides interactive graphing
capabilities and more importantly gives an easy way to recognize and use numerical
vector and matrix operations.
is a rapid prototyping tool with easily-accessed
graphics. Mostly scripts will be provided for you and
basic Matlab will be taught as needed in lectures.
Grades are based on small projects, a midterm, and a final.
Each programming assignment will have questions
intended to begin your thinking process, not end it. Grading will also include the
questions you raise and answer in these projects. For example, if the question is
"Which of the methods A and B is faster," simply saying "A" is not sufficient. You
should also ask (and try to answer) the underlying question "What measure of fast
is appropriate here?", "Why is A faster?", etc.
Although some assignments can be done in teams, in all cases you must
follow my attribution of work policy.
- 60% assignments
- 10% midterm exam
- 30% final exam, which is scheduled for 8:00-10:00 AM, Wednesday, 14
All of the web pages posted should have a footer like this, giving a time-stamped
log of any changes. Use that to know when something has changed or been added.
History of this page:
- Started: 21 Aug 2016, 05:46 PM
- Modified: Tue 06 Sept 2016, 12:35 AM to add AI details
- Last Modified: Tue 06 Sep 2016, 12:34 PM