Objectives

Our aim in CS 213 is to help you become a better programmer by teaching you the basic concepts underlying all computer systems. We want you to learn what really happens when your programs run, so that when things go wrong (as they always do) you will have the intellectual tools to solve the problem.

Why do you need to understand computer systems if you do all of your programming in high level languages? In most of computer science, we're pushed to make abstractions and stay within their frameworks. But, any abstraction ignores effects that can become critical. As an analogy, Newtonian mechanics ignores relativistic effects. The Newtonian abstraction is completely appropriate for bodies moving at less than 0.1c, but higher speeds require working at a greater level of detail.

Our 21X sequence works as follows: 211 is based on a simplified model of program execution. 212 builds further layers of abstraction. 213 introduces greater detail about system behavior and operation. This greater detail is needed for optimizing program performance, for working within the finite memory and word size constraints of computers, and for systems-level programming.

The following "realities" are some of the major areas where the abstractions we teach in 211/212 break down:

1. Int's are not integers, Float's are not reals. Our finite representations of numbers have significant limitations, and because of these limitations we sometimes have to think in terms of bit-level representations.

2. You've got to know assembly language. Even if you never write programs in assembly, The behavior of a program cannot be understood sometimes purely based on the abstraction of a high-level language. Further, understanding the effects of bugs requires familiarity with the machine-level model.

3. Memory matters. Computer memory is not unbounded. It must be allocated and managed. Memory referencing errors are especially pernicious. An erroneous updating of one object can cause a change in some logically unrelated object. Also, the combination of caching and virtual memory provides the functionality of a uniform unbounded address space, but not the performance.

4. There is more to performance than asymptotic complexity. Constant factors also matter. There are systematic ways to evaluate and improve program performance

5. Computers do more than execute instructions. They also need to get data in and out and they interact with other systems over networks.

By the end of the course you will understand these ``realities'' in some detail. As a result, you will be prepared to take any of the upper level systems classes at Carnegie Mellon. Even more important, you will have learned skills and knowledge that will help you throughout your career.

Good luck!