Lecture 1: Course Introduction
CS164: Programming Languages and Compilers
P. N. Hilfinger, 787 Soda
Fall 2020
Acknowledgement. Portions taken from CS164 notes by G. Necula.
Last modified: Wed Aug 26 22:26:55 2020 CS164: Lecture #1 1
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Lecture 1: Course Introduction, Schemes and Mind Maps of Programming Languages
CS164: Programming Languages and Compilers ... Please read Chapter 2 of the online Course Notes for the next lec- tures. • Discussion sections start next ...
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Lecture 1: Course Introduction
CS164: Programming Languages and Compilers P. N. Hilfinger, 787 Soda Fall 2020
Acknowledgement. Portions taken from CS164 notes by G. Necula.
Administrivia
- All course information, readings, and documentation is online from the course web page (constantly under construction).
- Get a cs164-xxx class account electronically using Webacct with your CalId.
- Please do not take this course P/NP.
- Projects generally require partnerships of 3–4. Start looking now. We will assign you to a team if you do not find one.
- DSP students: since projects are done in teams, I cannot accommo- date requests for individual extensions on projects for disability- related reasons.
- Please read Chapter 2 of the online Course Notes for the next lec- tures.
- Discussion sections start next week. At the moment, you are all in a single section. There will be a sign-up poll for actual rooms/times later this week or weekend.
Generic General Advice
DBC!
RTFM!
Plagiarism: Obligatory Warning
- We have software to detect plagiarism, and we Know How to Use It!
- If you must use others’ work (in moderation), cite it!
- Remember that on projects, you necessarily involve your partner.
- Most cheating cases result from time pressure. Keep up, and talk to us as early as possible about problems.
Implementing Programming Languages
- Strategy 1: Interpreter: program that runs programs.
- Strategy 2: Compiler: program that translates program into machine code (interpreted by machine).
- Strategy 3: Modern trend is hybrid: - Compilers that produce virtual machine code for bytecode inter- preters. - “Just-In-Time” (JIT) compilers interpret parts of program, com- pile other parts during execution. - Variant of 2: Compiler that translates program into another pro- gramming language (such as C), or into an intermediate language (such as Java class format) for which there is a translator or compiler.
Languages
- Initially, programs “hard-wired” or entered electro-mechanically - Analytical Engine, Jacquard Loom, ENIAC, punched-card-handling machines
- Next, stored-program machines: programs encoded as numbers (ma- chine language) and stored as data: - Manchester Mark I, EDSAC.
- 1953: IBM develops the 701; all programming done in assembly
- Problem: Software costs > hardware costs!
- John Backus: “Speedcoding” made a 701 appear to have floating point and index registers. Interpreter ran 10–20 times slower than native code.
After FORTRAN
- Lisp, late 1950s: dynamic, symbolic data structures.
- Algol 60: Europe’s answer to FORTRAN: modern syntax, block struc- ture, explicit declaration. - Dijkstra: “A marked improvement on its successors.” - Algol report Set standard for language description.
- COBOL: late 1950’s (and still with us). Business-oriented. Intro- duces records (structs).
The Language Explosion
- APL (arrays), SNOBOL (strings), FORMAC (formulae), and many more.
- 1967-68: Simula 67, first "object-oriented" language.
- Algol 68: Combines FORTRANish numerical constructs, COBOLish records, pointers, all described in rigorous formalism. Remnants remain in C, but Algol68 itself did not catch on.
- 1968: "Software Crisis" announced. Trend towards simpler lan- guages: Algol W, Pascal, C
Into the Present
- Complexity increases with C++.
- Then decreases with Java.
- Then increases again (C#, Java 1.5 et seq.).
- Proliferation of little or specialized languages and scripting languages: HTML, PHP, Perl, Python, Ruby,....
Example: FORTRAN
C FORTRAN (OLD-STYLE) SORTING ROUTINE C SUBROUTINE SORT (A, N) DIMENSION A(N) IF (N - 1) 40, 40, 10 10 DO 30 I = 2, N L = I- X = A(I) DO 20 J = 1, L K = I - J IF (X - A(K)) 60, 50, 50 C FOUND INSERTION POINT: X >= A(K) 50 A(K+1) = X GO TO 30 C ELSE, MOVE ELEMENT UP 60 A(K+1) = A(K) 20 CONTINUE A(1) = X 30 CONTINUE 40 RETURN END
C ----------------------------------
C MAIN PROGRAM DIMENSION Q(500) 100 FORMAT(I5/(6F10.5)) 200 FORMAT(6F12.5)
READ(5, 100) N, (Q(J), J = 1, N) CALL SORT(Q, N) WRITE(6, 200) (Q(J), J = 1, N) STOP END
Example: APL
∩◦ An APL sorting program
∇ Z ← SORT A
Z ← A [△| A ]
Example: Python (2.5)
import sys, re
def format(x):
return "%10.5f" % x
vals = map(float, re.split(r’\s+’, sys.stdin.read().strip()))
vals.sort()
print ’\n’.join([ ’’.join(map(format, vals[i:i+6]))
for i in xrange(0,len(vals),6)])
Problems to Address
- How to describe language clearly for programmers, precisely for implementors?
- How to implement description, and know you’re right? - Answer: Automatic conversion of description to implementation
- How to test?
- How to save implementation effort? - With multiple languages to multiple targets: can we re-use effort?
- How to make languages usable? - Handle errors reasonably - Detect questionable constructs - Compile quickly
Classical Compiler Structure (Front)
A
Lexical Analysis Parsing
/* Example */ salary = base + rate[years]; output(salary);
Optimized Tree
Intermediate, Virtual−Machine
Static Semantics Optimization^ Code Generation
Push_Addr # Push_Addr # Index Add
Push #
Source Program
Token Stream
Abstract
() ID#5 ID#
= ID#
;
ID#2 [] ID#3 ID#
<ID#1, ’=’, ID#2, ’+’, ID#3, ’[’, ID#4, ’]’, ’;’, ID#5, ’(’, ID#1, ’)’, ’;’, ...>
ID#2 [] ID#3 ID#
(double) (double) (int) (double[])
(double)
Optimization
Syntax Tree (AST)
AST
Attributed
(VM) Code