Note that you don't have to physically sit at the console of those machines, and can just login to them remotely via ssh, which provides for secure login and file transfer. For Windows platforms ssh is available as the Putty app, downloadable from UITS's IUWare and other places. ssh is on all IU Unix machines. Your Kerberos password (from IU's ADS) is what will work on both CS and UITS systems, except for CS research systems.
We will use some basic Matlab 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. If you use a Windows-based machine as your console and remotely login to a IU machine, you may also want to install and use a tool like hummingbird, which allows graphics windows from a remote Unix-based machine to be opened on your local Windows machine. Another option (somewhat clunky) is to use Matlab's print command to save the graphics window as a file. To test this, type
% matlab -nojvm -nosplash
in a console or command line interface on the remote Unix system. Then test the ability to view graphics dynamically via the command
> plot(randn(1, 12))
If that does not open a window on your console, the clunky way is
> print -dpng graph
which will create a file called graph.png, that you can then transfer over to your own platform to view. Matlab should be available on all CS and IU Unix and MS Windows systems. Matlab is available for abut $99 to students, to install on a personal computer. Buying Matlab is not required, but if you find accessing a system with it difficult or inconvenient, it is an option.
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, some assignments will require your codes to run on a specified system so that we can have comparable results across the class.
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
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 just parallel computing. Another course, CSci B673, concentrates on that aspect of scientific computing.
Matlab will be as a rapid prototyping tool and for its easily-accessed graphics. The needed skills in Matlab will be taught as part of the course, but for those who would like a preview the Matlab Tutorial material prepared by Dave Hart is useful, and a good Matlab tutorial is at the University of Florida. Another tutorial, posted on is at Michigan Technical University . However, all that is needed will be covered in class, so don't go out and memorize Web pages ... yet.
The final exam is sheduled for 8:00 AM to 10:00 AM on Wednesday, 11 December 2012. The exam will be the last project, and needs to be turned in by midnight that day. You can turn it in earlier ... or even remotely. The registrar requires that the grades be posted by that Friday, so there's no room for late handins this semester.
Leveraging the existing base of tools, software, knowledge, and earlier explorations is the only practical way to carry out science and engineering via computational methods.
Cheating in this class is nearly impossible, because the course encourages collaboration, code scavaging, and using publically available resources available whenever possible. It's why they gave us ears, the Internet, the ability to read and write things and Google. You can get away with almost any lifting or scavenging of material, provided that you cite the source. If your citation is "I photocopied another student's write-up" then you may not get many points on the assignment or may be assigned something to verify you have learned the material and skills, but at least you won't be expelled for plagiarism. The distinction between plagiarism and leveraging is citation. If in doubt, ask. Better still, play it safe and give a citation for any material or help you have received or given. Using the Web board is a great way to seek help - it's public to the entire class, the questions and answers are available to everyone, and so anything posted there is automatically fair game.
In spite of it being "nearly impossible", amazingly often somebody hands in a document or code that duplicates another's, down to the mispellings and errors in the coding, without mention of its provenance. It's not that hard to put in a code comment or a text footnote about just where you got any material, guidance, or help. Make a habit of having a section just for that in any code or other material handed in or presented, so that you have to think about and remember just where everything came from.
Although Professor Bramley is older than dirt, he also knows how to use Google as well as some tools to find matches that you don't have. So ... play it safe and give a citation anytime you did not do all the work yourself.
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