CS 333P Science of High Performance Computing

Computers are now essential in everyday life. Incorrect and/or slow programs lead to frustration in the best case and disaster in the worst. Thus, how to construct correct programs that can attain high performance is a skill that all who program computers must master.

In this course, we teach “goal-oriented programming” the way Edsger Dijkstra intended: You will learn how to derive programs hand-in-hand with their proofs of correctness. Matrix computations (linear algebra) is the domain from which we draw examples. Typically, we end up with a family of algorithms (programs) all of which compute a given operation. From this family we can then pick the algorithm that has desirable properties. For example, one algorithm may be easier to parallelize than another algorithm or it may inherently be able to attain better performance on a given architecture. You will then learn techniques for mapping the appropriate algorithms to computer architectures so that they can attain high performance.