Fairly detailed lecture and tutorial notes will be provided electronically (this document). These may not always be readable or complete though, as I am working on the course as it develops, and since I don't always have the time to polish every sentence1.1, or explain every detail in writing (but I'll try my best).
You will find the notes at:
http://www.cs.indiana.edu/dept/acad/courses.htmlabout 3/4 down the page, which will send you directly to the lecture notes.
You will find the latest PostScript version of the notes in my AFS home subdirectory
http://beige.ucs.indiana.edu/gustav/P573.ps.gzYou will need to have valid AFS credentials in order to obtain access to the AFS course development directory though. The notes may well grow to a considerable size by the time the course is finished. Use GNU ghostview to view the document and select pages for printing.
The lecture slides can be found in my AFS home directory too, as well as at
The following is the list of books and other publications that I have based this course on, so you can always go there and study any topic in more detail.
This text evolved from a course given to undergraduate science and engineering majors at MIT. It covers most of our syllabus and we are going to use it quite frequently, although not all the time. The book is not too expensive, given its size and scope. It is a highly recommended reading for this course.
A good concise and modular text that features excellent engineering and science applications. This is not and old Fortran 77 book merely translated to Fortran 90. New Fortran 90 features are used throughout the whole text.
This text is a typical academic brick. Covers computer hardware still in use today, although some of it is beginning to age, especially the part about vector processors. Will cover adequately point 2 of the syllabus. Don't buy it. Share it with a friend, if you can, or borrow it from the library.
A classic. For people specifically interested in linear systems. Most scientists will be perfectly happy just to call a relevant library routine without looking inside it.
A good broad review of parallel computing with numerous examples and case studies. Not a book from which to learn HPF or MPI programming though. But it gives a fairly good coverage of the issues.
Excellent book full of very interesting and relevant examples. Programming methodologies presented are old, based on Fortran-77 or even Fortran-66. But these are cosmetics. The programs can be easily rewritten in more modern styles, whereas the solutions themselves and the problems they address haven't aged at all. We will use it in our course quite frequently.
An interesting text for Physics students, who want to learn about Quantum Mechanics and Mathematica at the same time. We will make use of some of it in the course.
An authoritative reference for Mathematica users. If you plan to use Mathematica in your research extensively, you must have it.
A very brief, no-nonsense introduction to Maple. May be too scant for dedicated Maple users, but is quite sufficient for this course.
This is one of the best and the most useful books for scientists working with computers, be it to analyse their experimental results, or develop numerical models. Written by four consummate practitioners of computational science with extensive academic and industrial experience, the book is positively hated by great many numerical analysts, primarily for not having mentioned their latest favourite method and papers. A good enough reason to buy it: stick to the classics. This book will serve you well for years to come. The ISBN number quoted above refers to one of its first editions. Since then the book has been reprinted and improved many times and in many ways, much like the Bible. Go for the latest edition.
A good easy going introduction to MATLAB and to Octave. The latter is not mentioned in the book, but as you begin working with Octave you'll notice that it's uncannily similar to MATLAB (though free). The book covers some elementary numerical analysis too - not a bad way to learn about it.
A very interesting and useful introduction to this difficult but at the same time so very promising field. Quantum computing may provide performance many orders of magnitude better than the best that you will ever be able to squeeze out of conventional computers based on sloooowly diffusing semi-classical electrons trapped in a crystal lattice of even the fastest semiconductors. Quantum computing is also going to be orders of magnitude cheaper. You can do it even today with a cup of coffe (seriously) and a Nuclear Magnetic Resonance machine.
A great introduction to field theory and differential geometry.