1. Computer Systems & Programming (CS 367) Section 002: Prof. Elizabeth White Spring 2010

2. 1-2 Course Goals Previous courses: CS 112, CS 211 - 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. 1-3 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. 1-4 Great Reality #1 Ints are not Integers, Floats are not Reals Examples Is x 2 ≥ 0?  Floats: Yes!  Ints: – 40000 * 40000 --> 1600000000 – 50000 * 50000 --> ?? Is (x + y) + z = x + (y + z)?  Unsigned & Signed Ints: Yes!  Floats: – (1e20 + -1e20) + 3.14 --> 3.14 – 1e20 + (-1e20 + 3.14) --> ??

5. 1-5 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. 1-6 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. 1-7 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. 1-8 Memory Referencing Bug Example double fun (inti) { volatile double d[1] = {3.14}; volatile long int a[2]; a[i] = 1073741824; /* Possibly out of bounds */ return d[0]; } double fun (inti) { volatile double d[1] = {3.14}; volatile long int a[2]; a[i] = 1073741824; /* Possibly out of bounds */ return d[0]; } fun(0) –>3.14 fun(1) –>3.14 fun(2) –>3.1399998664856 fun(3) –>2.00000061035156 fun(4) –>3.14, then segmentation fault Saved State d7 … d4 d3 … d0 a[1] a[0]

9. 1-9 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. 1-10 Memory Performance Example Hierarchical memory organization Performance depends on access patterns Including how we step through multi-dimensional array void copyij(int src[2048][2048], int dst[2048][2048]) { inti,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]) { inti,j; for (j= 0; j< 2048; j++) for (i = 0; i< 2048;i++) dst[i][j] = src[i][j]; } 21 times slower Pentium 4

11. 1-11 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. 1-12 Great Reality #5 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. 1-13 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. 1-14 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. 1-15 Relationship to other courses

16. 1-16 Course Prerequisites You must have completed the following classes with a C or better grade: CS 262 – Introduction to Low-level Programming  or CS 222 - 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. 1-17 Textbook Randal E. Bryant and David R. O’Hallaron,  “Computer Systems: A Programmer’s Perspective”, Prentice Hall 2003. –http://csapp.cs.cmu.edu  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. 1-18 Logistics Lectures Homework [15% of the course grade] Simple program in C Written exercises Labs [35% of the course grade] The heart of the course 2 or 3 weeks each Provides an in-depth understanding of an aspect of systems Programming and measurement You can work in groups of up to size 2 on homework and labs

19. 1-19 Logistics More about Labs All programs must be submitted and tested on zeus.ite.gmu.edu You need to obtain an IT&E Unix cluster account. See the syllabus for further information. Even if it works on your laptop or PC, it doesn’t count unless it runs on the IT&E cluster. Making Deadlines 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. Midterm and Final exams [Each is 25% of the grade]  Test your understanding of concepts & mathematical principles  There are NO make ups for missing an exam.

20. 1-20 Cheating What is 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 code from previous course or from elsewhere on WWW What is NOT cheating? Explaining how to use systems or tools. Helping others with high-level design issues. Penalty for cheating: Removal from course with failing grade.

21. 1-21 Getting Help Class Web Page http://cs.gmu.edu/~white/cs367 Copies of lectures, homework, labs, handouts, suggested readings Blackboard http://courses.gmu.edu Discussion board monitored by our UTAs Homework and exam grades posted here Up-to-date information on the course  Cancellation notices  Extensions on assignments  Changes to assignments  Solutions to homework

22. 1-22 Course Outline Week 1: Overview of Computer Systems (Ch 1) Week 2, 3: Representing & Manipulating Information (Ch 2) Weeks 4 - 9: Machine-level Representation of Programs (Ch 3) Week 10: Linking (Ch 7) Weeks 11 - 12: Exceptional Control Flow (Ch 8) Week 13: Memory (Ch 10)

23. 1-23 Lab Rationale Each lab has a well-defined goal such as solving a puzzle or winning a contest. Doing a lab should result in new skills and concepts. We try to use competition in a fun and healthy way. Set a reasonable threshold for full credit. Post intermediate results (anonymized) on Web page for glory!

24. 1-24 Machine-level Representation of Data and Programs Topics Bits operations, arithmetic, assembly language programs, representation of C control and data structures Includes aspects of of architecture and compilers Assignments Homeworks including a datalab: Manipulating bits Lab 1 (bomblab): Defusing a binary bomb Lab 2 (buflab): Hacking a buffer bomb

25. 1-25 Linking and Exceptional Control Flow Topics Object files, static & dynamic linking, libraries, loading Hardware exceptions, processes, process control, Unix signals, nonlocal jumps Includes aspects of compilers, OS, and architecture Assignments Lab 3 (tshlab): Writing your own shell with job control

26. 1-26 Memory Topics Virtual memory, address translation, dynamic storage allocation Includes aspects of architecture and OS