1. Computer Systems & Programming (CS 367)
Prof. Elizabeth White
Spring 2016

2. Course Goals Previous courses:
CS 112, CS high-level programming in Java/C++
CS262 – C programming - Commonly used for “low-level” systems programming and embedded systems
Theme of the course: strip away abstractions provided by high-level languages such as Java and let you understand what goes on “under the hood”

3. Course Goals cont’d Most CS courses emphasize abstraction
Abstract data types
Asymptotic analysis
Abstractions have limits
Especially in the presence of bugs
Need to understand underlying implementations
Useful outcomes
Become more effective programmers
Able to find and eliminate bugs efficiently
Able to tune program performance
Prepare for later “systems” classes in CS & ECE
Compilers, Operating Systems, Networks, Computer Architecture

4. Great Reality #1 Ints are not Integers, Floats are not Reals Examples
Is x2 ≥ 0?
Floats: Yes!
Ints:
40000 * >
50000 * > ??
Is (x + y) + z = x + (y + z)?
Unsigned & Signed Ints: Yes!
Floats:
(1e e20) > 3.14
1e20 + (-1e ) --> ??

5. Computer Arithmetic Does not generate random values
Arithmetic operations have important mathematical properties
Cannot assume “usual” properties
Due to finiteness of representations
Integer operations satisfy “ring” properties
Commutativity, associativity, distributivity
Floating point operations satisfy “ordering” properties
Monotonicity, values of signs
Observation
Need to understand which abstractions apply in which contexts
Important issues for compiler writers and serious application programmers

6. Great Reality #2 You’ve got to know assembly
Chances are, you’ll never write program in assembly
Compilers are much better & more patient than you are
Understanding assembly key to machine-level execution model
Tuning program performance
Understand optimizations done/not done by the compiler
Understanding sources of program inefficiency
Implementing system software
Compiler has machine code as target
Operating systems must manage process state
Creating / fighting malware
x86 assembly is the language of choice!

7. Great Reality #3 Memory Matters Memory is not unbounded
It must be allocated and managed
Many applications are memory dominated
Memory referencing bugs especially pernicious
Effects are distant in both time and space
Memory performance is not uniform
Cache and virtual memory effects can greatly affect program performance
Adapting program to characteristics of memory system can lead to major speed improvements

8. Memory Referencing Bug Example
double fun (int i) {
double d[1] = {3.14};
long int a[2];
a[i] = ; /* Possibly out of bounds */
return d[0]; }
zeus-1$ a.out 0
result:
zeus-1$ a.out 1
zeus-1$ a.out 2
result:
zeus-1$ a.out 3
zeus-1$ a.out 4
Segmentation fault (core dumped)
zeus-1$ a.out 5
zeus-1$ a.out 6
Saved State
d7 … d4
d3 … d0
a[1]
a[0]

9. Memory Referencing Errors
C and C++ do not provide any memory protection
Out of bounds array references
Invalid pointer values
Abuses of malloc/free
Can lead to nasty bugs
Whether or not bug has any effect depends on system and compiler
Action at a distance
Corrupted object logically unrelated to one being accessed
Effect of bug may be first observed long after it is generated
How can I deal with this?
Program in Java, Lisp, or ML
Understand what possible interactions may occur
Use or develop tools to detect referencing errors

10. Memory Performance Example
void copyij(int src[2048][2048],
int dst[2048][2048]) {
int i,j;
for (i= 0; i< 2048; i++)
for (j = 0; j< 2048; j++)
dst[i][j] = src[i][j]; }
void copyij(int src[2048][2048],
int dst[2048][2048]) {
int i,j;
for (j= 0; j< 2048; j++)
for (i = 0; i< 2048;i++)
dst[i][j] = src[i][j]; }
21 times slower
Pentium 4
Hierarchical memory organization
Performance depends on access patterns
Including how we step through multi-dimensional array

11. Great Reality #4
There’s more to performance than asymptotic complexity
Constant factors matter too!
And even exact op count does not predict performance
Easily see 10:1 performance range depending on how code written
Must optimize at multiple levels: algorithm, data representations, procedures, and loops
Must understand system to optimize performance
How programs compiled and executed
How to measure program performance and identify bottlenecks
How to improve performance without destroying code modularity and generality

12. Great Reality #5 They need to get data in and out
Computers do more than execute programs
They need to get data in and out
I/O system critical to program reliability and performance
They communicate with each other over networks
Many system-level issues arise in the presence of networks
Concurrent operations by autonomous processes
Coping with unreliable media
Cross platform compatibility
Complex performance issues

13. Course Perspective Most Systems Courses are Builder-Centric
Computer Architecture
Design pipelined processor
Operating Systems
Implement portions of operating system
Compilers
Write compiler for simple language
Networking
Implement and simulate network protocols

14. Course Perspective (Cont.)
This Course is Programmer-Centric
Purpose is to show how by knowing more about the underlying system, one can be more effective as a programmer
Enable you to
Write programs that are more reliable and efficient
Incorporate features that require hooks into OS
E.g., concurrency, signal handlers
Not just a course for dedicated hackers
We bring out the hidden hacker in everyone
Cover material in this course that you won’t see elsewhere
Linking, loading, signals
If nothing else, this course will teach you how to make effective use of debuggers such as gdb

15. Relationship to other courses

16. Course Prerequisites
You must have completed the following classes with a C or better grade:
CS 262 – Introduction to Low-level Programming
or CS Computer Programming for Engineers,
ECE 301 – Digital Electronics
The programming assignments in CS 367 involve machine and assembly language (x 86), and programming in C
Things you should already know about C:
C data types
C Standard I/O library
Bit-level operators
Pointers & structures
Dynamic memory allocation and deallocation
How to use makefiles
Unix commands

17. Randal E. Bryant and David R. O’Hallaron,
Textbook
Randal E. Bryant and David R. O’Hallaron,
“Computer Systems: A Programmer’s Perspective”, Prentice Hall 2009 – Third Edition
This book really matters for the course!
How to solve labs
Practice problems typical of exam problems
Optional book: a good reference on C like
Brian Kernighan and Dennis Ritchie,
“The C Programming Language, Second Edition”, Prentice Hall, 1988

18. Logistics Lectures Homework [10% of the course grade]
Small programs in C
Written exercises
Labs [35% of the course grade]
The heart of the course
4 assignments - approx. 2 weeks each
Provides an in-depth understanding of an aspect of systems
Programming and measurement C

19. Lab Rationale
Each lab has a well-defined goal. Completing the labs will result in new skills and a better understanding of course concepts.
Each lab is to be an individual effort – ‘moss’ (and other software) may be used to detect unauthorized collaboration.
All programs must be submitted and tested on zeus.vse.gmu.edu
Even if it works on your laptop or PC, it doesn’t count unless it runs on the IT&E cluster.
You will need to install and use the university VPN to access your account off campus.

20. Logistics Making Deadlines Exams and Final exams [30%+ 25%]
Assignments are due at the stated time and day.
Some (not all) assignments can be turned in late with a penalty for each late day.
Using ‘tokens’ to help manage late days (see syllabus)
Exams and Final exams [30%+ 25%]
Test your understanding of concepts & mathematical principles
There are NO make ups for missing an exam without official documentation of your reason for missing.

21. Cheating What is cheating? What is NOT cheating? Penalty for cheating:
Sharing code: either by copying, retyping, looking at, or supplying a copy of a file.
Coaching: helping your friend to write a lab, line by line.
Copying or purchasing code from previous course or from elsewhere on WWW
What is NOT cheating?
Explaining how to use systems or tools.
Explaining what is meant by the specifications.
Looking at output and telling whether it is correct or not.
Penalty for cheating:
Removal from course with failing grade.

22. Getting Help Piazza Blackboard http://mymason.gmu.edu
Up-to-date information on the course
Slides
HW & Lab assignment
Solutions to homework
Your grades – be sure to confirm these are correct!
Piazza
Ask questions of instructors, TA, UTA, peers
Course announcements will appear here