Compiler Design

1. Introduction to Code
Optimization
Dipankar Nalui
M.SC. Computer Science
Dept. of Computer Science
Pondicherry University

2. Compiler
• A compiler is a program that accepts a high level language and
produces a target language as output (Assembly Language).
Compiler
Assembly Level
High Level Language Language

3.  Phases of Compiler
1) Lexical Analysis
2) Syntax Analysis
3) Semantic Analysis
4) Intermediate Code Generation
5)Code Optimization
6) Code Generation

4.  Code Optimization
•Optimization is the process of transforming a
piece of code to make more efficient (either in
terms of time or space) without changing its
output or side-effects.

5. Why optimization is needed ???
• To improve intermediate code
• Better target code
• Executes Faster
• Shorter code
• Less power
• Complexity : Time, Space & Cost
• Efficient memory usage
• Better performance.

6.  “Optimization”
• The only difference visible to the code’s user should be that it runs
faster and/or consumes less memory.
• The name implies you are finding an "optimal“ solution— in truth,
optimization aims to improve, not perfect, the result.

7. Correctness Above All!
• Optimization should not change the correctness of the generated
code.
• Transforming the code to something that runs faster but incorrectly is
of little value.
• It is expected that the un-optimized and optimized variants give the
same output for all inputs.

8. position = initial + rate * 60
id1 = id2 + id3 * 60
Intermediate Code
T1= int-to-float(60)
T2=id3*T1
T3=id2+T2
id1=T3
Eliminate int-to-float by
replacing 60 by 60.0
Code Optimization
T1=id3 * 60.0
Id1=id2 + t1
Code Optimizer can deduce
that the conversion of 60
from int to float can be done
once.

9.  The principal Sources of Optimization
• Some techniques are applied to the intermediate code, to streamline,
rearrange, compress, etc.
• Control-Flow Analysis
• Local Optimizations
• Constant Folding
• Constant Propagation
• Operator Strength Reduction
• Copy Propagation
• Dead Code Elimination
• Common Subexpression Elimination
• Global Optimizations, Data-Flow Analysis

10. Copy Propagation
Intermediate Code
tmp2 = tmp1 ;
tmp3 = tmp2 * tmp1;
tmp4 = tmp3 ;
tmp5 = tmp3 * tmp2 ;
c = tmp5 + tmp4 ;
The code on the left makes a copy
of tmp1 in tmp2 and a copy of
tmp3 in tmp4.
Code Optimization
tmp3 = tmp1 * tmp1 ;
tmp5 = tmp3 * tmp1 ;
c = tmp5 + tmp3 ;
we eliminated those
unnecessary copies

11. Dead Code Elimination
• If an instruction’s result is never used, the instruction is considered
"dead" and can be removed from the instruction stream.
• tmp1 = tmp2 + tmp3 ;
• if tmp1 is never used again, we can eliminate this instruction.
• if tmp1 holds the result of a function call:
• tmp1 = LCall _Binky;
• Even if tmp1 is never used again, we cannot eliminate the instruction
because we can’t be sure that called function has no side-effects.

12.  Common Sub-expression Elimination
Two operations are common if they produce
the same result.
main()
{
int x, y, z;
x = (1+20)* -x;
y = x*x+(x/y);
y = z = (x/y)/(x*x);
}
tmp1 = 1 + 20 ;
tmp2 = -x ;
x = tmp1 * tmp2 ;
tmp3 = x * x ;
tmp4 = x / y ;
y = tmp3 + tmp4 ;
tmp5 = x / y ;
tmp6 = x * x ;
z = tmp5 / tmp6 ;
y = z ;

13. Intermediate Code
tmp1 = 1 + 20 ;
tmp2 = -x ;
x = tmp1 * tmp2 ;
tmp3 = x * x ;
tmp4 = x / y ;
y = tmp3 + tmp4 ;
tmp5 = x / y ;
tmp6 = x * x ;
z = tmp5 / tmp6 ;
y = z ;
Code Optimization
tmp2 = -x ;
x = 21 * tmp2 ;
tmp3 = x * x ;
tmp4 = x / y ;
y = tmp3 + tmp4 ;
tmp5 = x / y ;
z = tmp5 / tmp3 ;
y = z ;

14. Algebraic Simplification
• x+0 = x
• 0+x = x
• x*1 = x
• 1*x = x
• 0/x = 0
• x-0 = x
• b && true = b
• b && false = false
• b || true = true
• b || false = b

15.  Example of Algebraic Simplification
b = 5 + a + 10 ;
Intermediate Code
_tmp0 = 5 ;
_tmp1 = _tmp0 + a ;
_tmp2 = _tmp1 + 10 ;
b = _tmp2 ;
Code Optimization
_tmp0 = 15 ;
_tmp1 = a + _tmp0 ;
b = _tmp1 ;

16.  Constant Folding
• Evaluate constant expressions at compile time
• Only possible when side-effect freeness guaranteed
c:= 1 + 3 c:= 4
true not false

17.  Constant Propagation
• Variables that have constant value, e.g. c := 3
• Later uses of c can be replaced by the constant
• If no change of c between!
b := 3
c := 1 + b
d := b + c
b := 3
c := 1 + 3
d := 3 + c

18.  Strength Reduction
• Replace expensive operations with simpler ones
• Example: Multiplications replaced by additions
y := x * 2 y := x + x

19.  Conclusion:
• Why do we optimize programs?
• Is there an optimal optimizer?
• Where in a compiler does optimization happen?
• Does the optimization capture most of the potential improvement
without an unreasonable amount of effort ?
• Does the optimization preserve the meaning of the source program ?
• Does the optimization reduce the time and space?

20. Questions ??

21. Thank You …