CS 326 Programming Languages, Concepts and Implementation

1. CS 326Programming Languages, Concepts and Implementation Instructor: Mircea Nicolescu Lecture 1

2. Contacts • Instructor: Dr. Mircea Nicolescu • E-mail: mircea@cse.unr.edu • Office: SEM 232 • Office Hours: Tuesday: 9:00am-12:00pm • Class web page: • http://www.cse.unr.edu/~mircea/Teaching/cs326

3. Assignments and Exams • Homework assignments (40%) • Written exercises and small programs • More complex programs, in Scheme, Prolog, Java or ML • Midterm exam (25%) • Closed book, closed notes • Final exam (30%) • Closed book, closed notes • Comprehensive, although focused on the second half of the course • Attendance and class participation (5%)

4. Late Submission Policy • No late assignments will be accepted Warning: Dates in the calendar are closer than they appear!

5. Required Textbook • Programming Language Pragmatics, by Michael L. Scott, Morgan Kaufmann Press, 2009.

6. What Are We Studying? • Programming languages characterized by: • Syntax – what a program looks like • Semantics – what a program means • Implementation – how a program executes • Trade-offs between design (specifying syntax and semantics) and implementation • Will focus on these concepts, illustrated by various languages, and not on the languages themselves

7. Why Study Programming Languages? • Help you choose a language • Systems programming → C, Modula-3 • Scientific computations → Fortran, Ada • Embedded systems → C, Ada, Modula-2 • Symbolic computing → Lisp, Scheme, ML • Make it easier to learn new languages • Some languages are similar • Some concepts are even more similar: iteration, recursion, abstraction

8. Why Study Programming Languages? • Make better use of the languages you know • Improve ability to develop effective algorithms • Understand obscure features • Understand implementation costs • Simulate useful features in languages that do not support them • Prepare for further study in language design and implementation (compilers) • Help understand other system software (assemblers, linkers, debuggers)

9. Historic Perspective • When did the earliest high-level languages appear? • Continuous evolution since the 1950s (Fortran, Cobol) • When the Department of Defense did a survey as part of its efforts to develop Ada in the 1970s, how many languages did it find in use? • Over 500 languages were being used in various defense projects

10. Architecture Evolution • 1950s – Large expensive mainframe computers ran single programs (Batch processing) • 1960s – Interactive programming (time-sharing) on mainframes • 1970s – Development of microcomputers. Early work on windows, icons, and PCs at XEROX PARC • 1980s – Personal computer: IBM PC and Apple Macintosh. Use of windows, icons and mouse • 1990s – Client-server computing - networking, the Internet, the World Wide Web

11. Spectrum of Languages • Imperative (“how should the computer do it?”) • Von Neumann: Fortran, Basic, Pascal, C • Computing via “side-effects” (modification of variables) • Object-oriented: Smalltalk, Eiffel, C++, Java • Interactions between objects, each having an internal state and functions which manage that state • Declarative (“what should the computer do?”) • Functional: Lisp, Scheme, ML, Haskell • Program ↔ application of functions from inputs to outputs • Inspired from lambda-calculus (Alonzo Church) • Logic, constraint-based: Prolog • Specify constraints / relationships, find values that satisfy them • Based on propositional logic

12. Why Are There So Many Languages? • Evolution • We have learned better ways of doing things over time • Socio-economic factors • Proprietary interests, commercial advantage • Special purposes • Special hardware • Personal preference

13. What Makes a Language Successful? • Easy to learn • Basic, Pascal • Easy to express things • “Powerful” • Easy to use once known • C, Lisp • Easy to implement • Small machines, limited resources • Basic, Forth

14. What Makes a Language Successful? • Efficient compilers • Generate small / fast code • Fortran • Backing of a powerful sponsor • Ada, Visual Basic • Wide dissemination at a minimal cost • Pascal, Java • Inertia • Fortran, Cobol

15. Standardization • Is i = 1 && 2 + 3 | 4; legal in C? • What is assigned to i if it is? • 3 ways to answer this: • Read language manual (Problem: Can you find one?) • Read language standard (Problem: Have you ever seen it?) • Write a program to see what happens. (Easy to do!) • Most do the last, but different compilers may give different answers

16. Standardization • Language standards defined by: • ISO - International Standards Organization • IEEE - Institute of Electrical and Electronics Engineers • ANSI - American National Standards Institute • Contentious features omitted to gain consensus • Nothing is enforced

17. Standardization • When to standardize a language: • If too late – many incompatible versions already exist (Fortran in 1960) • If too early – no experience with language (Ada in 1983 had no running compilers yet) • Just right – probably Pascal in 1983, although it is rapidly becoming a dead language • Other languages: • C in 1988 • LISP in 1990 – way too late • De facto standards: ML – one major implementation: SML • Smalltalk, Prolog – none

18. Standardization • Internationalization: • How many bits to use for characters • Additional letters (German ä, French é) • Other alphabets (Cyrillic, Greek, Arabic, Chinese) • Organization that studies this problem cannot even decide between internationalization and internationalisation for its own name • Other internationali*ation issues: • Dates, times, daylight saving time • Currency

19. afbf0015 A Simple Program Compute greatest common divisor of 2 integers: • Machine language • Sequence of bits that directly controls the processor • Add, compare, move data, etc. • Computer’s time more valuable than programmers’ time

20. A Simple Program • Assembly language • Mnemonic abbreviations, 1-to-1 correspondence to machine language • Translated by an assembler • Still machine-dependent

21. Compilation and Interpretation • Need for: • Machine-independent language • Resemblance between language and mathematical computation • High-level languages (the first: Fortran, Lisp) • Translated by a compiler or interpreter • No more 1-to-1 correspondence to machine language • Accepted when compilers could produce efficient code • Today: efficient compilers, large programs, high maintenance costs, rapid development requirements • Labor cost higher than hardware cost

22. Compilation and Interpretation • Compiler translates into target language (machine language), then goes away • The target program is the locus of control at execution time • Interpreter stays around at execution time • Implements a virtual machine whose machine language is the high-level language

23. Compilation and Interpretation • Interpretation • Greater flexibility, better diagnostics • Allow program features (data types or sizes) to depend on the input • Lisp, Prolog: program can write new pieces of itself and execute them on the fly • Java: compilation, then interpretation • Compilation • Better performance • Many decisions are made only once, at compile time, not at every run time • Fortran, C

24. Readings • Chapter 1