This document discusses code optimization techniques used by compilers. It covers the following key points in 3 sentences: Principal sources of optimization include common subexpression elimination, constant folding and propagation, code motion, dead code elimination, and strength reduction. Data flow analysis is used by optimization techniques to gather information about how data flows through a program. The document also describes local and global optimization, peephole optimization, basic blocks, and efficient data flow algorithms used in compiler design.

1. Code Optimization
Principal Sources of Optimization – Peep-hole
optimization – DAG- Optimization of Basic
Blocks–Global Data Flow Analysis – Efficient
Data Flow Algorithm – Recent trends in
Compiler Design

2. Code optimization
• program transformation technique, which tries to
improve the code by eliminating unnecessary
code lines and arranging the statements in a
sequence to make it consume less resources (i.e.
CPU, Memory) and deliver high speed.
Advantages:
 Executes faster.
 Efficient memory usage.
 yields better performance.

3. • Compiler front end
–Lexical analysis, Syntax analysis, Semantic
analysis
Tasks :
understanding the source code,
making sure the source code is written
correctly.
Code optimization

4. • Compiler back end:
–Intermediate code generation/improvement
and machine code generation/improvement.
Tasks:
translating the program to a
semantically the same program(in a different
language).
Code optimization

5. • A code optimizer sits between the frontend and
the code generated.
– Works with intermediate code
– Can do control flow analysis
– Can do data flow analysis
– Does transformations to improve the intermediate
code.
Code optimization is done on basic blocks.
Code optimization

6. Types of code optimization
• Machine Independent Optimization
– Improve the intermediate code to get a better target
code as output
– Intermediate code transformed doesn’t involve any
CPU registers or absolute memory locations
• Machine Dependent Optimization
– Improve the target code according to the target
machine architecture
– Involves CPU registers and may have absolute
memory references.
– Put efforts to take maximum advantage of the memory

7. 1. Local Optimization
Apply to a single block
Straight line code
Simplest form
No need to analyse the whole procedure body
2. Global Optimisation
Apply across basic blocks
Applied to program segments that includes functions and loops
Data flow analysis is done to perform optimisation
3. Peephole Optimization
Performed on a small set of compiler generated instructions
Operates on the target code considering few instructions at a time
Do machine dependent improvements
Most compilers do (1), many do (2) and very few do (3)
Types of code optimization

8. Basic blocks:
Straight line code with no branches
Sequence of intermediate code with a single entry
and a single exit.
CFGs show control flow among basic blocks
Optimization is done on the basic blocks
A basic block begins in one of the following ways
The entry point into the functions
The target of a branch (can be a label)
The instruction immediately following a branch
Basic blocks and flow graph

9. A basic block ends in any of the following ways
a jump statement
a conditional or unconditional branch
a return statement.
Basic blocks and flow graph Contd…

10. Properties of basic blocks
Property 1:
Sequence of consecutive statements executed in
a sequential manner.
Property 2:
One entry point and one exit point
Property 3:
Should not contain conditional or unconditional
control statements in the middle of the block. It
may be the first or last statement.

11. Algorithm for partitioning a three address
code into basic block.
Rules
1. Determine the leaders
2. Determine the basic blocks.
1. Determine the leaders
• 1st statement is a leader
• Target of a conditional or unconditional
statement is a leader
• Statement following the conditional or
unconditional jump is a leader.

12. Determine the basic block and draw the flow graph for the given 3
address code
1. PROD = 0
2. I = 1
3. T2 = addr(A)
4. T4 = addr(B)
5. T1 = 4*I
6. T3 = T2[T1]
7. PROD = PROD+T3
8. I = I+1
9. If I<=20 goto(5)
10. j = j+1
11. k = k+1
12. If j<=5 goto(7)
13. I = i+j
Three address code

13. 2. Determine the basic blocks
begin from the leader statement and end in the
statement before the next leader.
Leaders as per rules – 1,5,7,10,13
1. PROD = 0
2. I = 1 B1
3. T2 = addr(A)
4. T4 = addr(B)
5. T1 = 4*I
6. T3 = T2[T1] B2

14. 7. PROD = PROD+T3
8. I = I+1 B3
9. If I<= 20 goto(5)(B2)
10. j = j+1
11. k = k+1 B4
12. If j<=5 goto(7)(B3)
13. i = i+j B5

15. Flow graph
Directed graph in which flow control information
is added to the basic blocks.
Basic blocks are the nodes of the graph.
B2
B1
B3
B4
B5

16. Directed Acyclic Graph(DAG)
DAG representation of a basic block
represent the structure of a basic block
1. In a DAG, internal node represents operators.
2. Leaf node represents identifiers, constants.
3. Internal node also represents result of
expressions.
t = a+b t
a b
+

17. Application of DAG
• Determining the common sub expressions.
• Determining which names are used inside the
blocks & computed outside the block.
• Determining which statement of the block
could have their computed value outside the
blocks.
• Simplifying the list of quadruples by
eliminating common sub expressions.

18. (eg) Construct DAG for the expression
a + a*(b-c)+(b-c)*d
+
+
* *
a - d
b c

19. a = b + c
b = a - d c
c = b + c
d = a - d - b, d
+ a d
b c
+
+
-

20. a = b*-c + b*-c
a
b
c
=
-
*
+

21. a = b+c
b = b-d
c = c+d
e = b+c
a b c
b c d
+
+
+ -

22. d = b * c
e = a + b
b = b * c
a = e – d a
e d, b
a b c
+
-
*

23. a = (a*b+c)-(a*b+c)
=
a -
+
* c
a b

24. Optimization of basic blocks
1. Finding Local Common sub expression
2. Dead code elimination.
3. Renaming temporary variables.
4. Interchange of statements.
5. Algebraic transformations.

25. Peephole Optimization
• Applied to improve the performance of
program by examining a short sequence of
instructions in a window(peephole) and
replace the instructions by a faster or short
sequence of instructions.

26. Peephole Optimization Techniques
1. Eliminating Redundant Loads and Stores.
2. Eliminating Unreachable Code.
3. Flow of control optimizations.
4. Algebraic Simplifications
5. Machine idioms.

27. 1. Eliminating redundant loads and
stores
Consider one instruction
mov R0, a
mov a, R0
eliminate 2nd instruction since a is already in R0.

28. 2. Eliminating Unreachable Code
• Eliminate the statements which are unreachable.
(i.e) never executed.
(eg:1) i = 0; (eg:2) int add(int a, b)
if(i==1) {
{ i=0 int c=a+b;
sum = 0; return c;
} printf(“%d”, c); Eliminate
}

29. 3. Flow of Control Optimizations
• Unnecessary jumps can be eliminated
(eg)
Multiple jumps can make the code inefficient.
Above code can replaced
goto L1 goto L3
….. …..
L1: goto L2 L1: goto L3
….. …..
L2: goto L3 L2: goto L3
….. L3: Mov a, R0
L3: Mov a, R0

30. 4. Algebraic Simplifications
(eg) x = x+0 (or) x = x*1
The above statements can be eliminated because
by executing those statements the result x
won’t change.
(eg) a = replaced with a = x*x
b = y/8 replaced with b = y>>3

31. 5. Use of Machine Idioms
• It is the process of using powerful features of
CPU instructions.
(eg) auto inc/dec function can be used to inc/dec
variable.
a = a+1 replaced with inc a.
b = a-1 replaced with dec a.

32. Principal Sources of Optimization
(or) Semantic-Preserving
Transformation
1. Common sub expression elimination.
2. Compile time evaluation
 Constant folding.
 Constant propagation.
3. Code movement (or) Code motion.
4. Dead code elimination.
5. Strength reduction.

33. 1. Common Sub expression
Elimination
• Common sub expression
– an expression(E) which appears repeatedly in the
program, which is computed previously but the
values of variables in expression haven’t changed.
– replaces the redundant expression each time it is
encountered.

34. Unoptimized code Optimized code
a=b+c a=b+c
b=a-d b=a-d
c=b+c c=b+c
d=a-d d=b
c=a-d

35. 2. Compile Time Evaluation
• done at the compile time instead of run time.
a. Constant Folding:
Evaluate the expression and submit the
result.
(eg) area=(22/7)*r*r
22/7 is calculated and result 3.14 is
replaced.

36. Contd…
b. Constant Propagation:
propagate the constant through out the
expression.
Constant replace a variable.
(eg) pi = 3.14, r = 5
area = pi*r*r
area = 3.14*5*5

37. 3. Code Movement (or) Code Motion
• moves the code outside the loop if it won’t
have any difference if it executes inside or
outside the loop.
Unoptimized Code Optimized Code
for(i=0; i<n; i++) x = y+3
{ for(i=0; i<n; i++)
x = y+3; {
a[i] = 6*i; a[i] = 6*i;
} }

38. 4. Dead Code Elimination
• Includes elimination of those statements which
are never executed or if executes output is
never used.
(eg)
Unoptimized Code Optimized Code
i=0 i=0
if(i==i)
{
a= x+i;
}

39. Contd…
Unoptimized Code Optimized Code
int add(int x, int y) int add(int x, int y)
{ {
int z; int z;
z=x+y; z=x+y;
return z; return z;
printf(“%d”,z); }
}

40. 5. Reduction in Strength
• Replacing expensive operator with cheapest
operator
b = a*2 is replaced with
b = a+a

41. Data Flow Analysis (DFA)
• All the optimization techniques depend on data flow analysis.
• DFA know about how the data is flowing in any control-flow
graph.
• DFA – collects all information about entire program and
distribute this information to each block in the flow graph.
• It helps in identifying the variables that hold values at different
points in the program and how these values change over time.
• This information is used to optimize the program by
eliminating dead code, identifying constant expressions, and
reducing unnecessary computations.

42. Data Flow Analysis
• Helps in improving the efficiency of the code generated by the
compiler
• Program debugging
• Program optimization

43. Global Data Flow Analysis
• Collect the data flow information about entire program and
distribute the information to each block
• Data flow is collected using the data flow equation
out [s] = gen [s] U(in[s] – kill[s])
out [s] – Info at the end of S
gen [s] - Info generated by S
in[s] - Info enter at the beginning of S
kill[s] – Info killed by S
i.e information that comes out of S is info generated by S +
information coming to the block S and the information killed
in the block S

44. Global Data Flow Analysis
• Structured Statement
S->id :=E/S ; S
S -> if E then S1 else S2/ do S while E
E- > id+id/id

45. Global Data Flow Analysis

46. Efficient Data Flow Algorithm
• Redundant common sub expression elimination
• Copy Propagation
a :=b
y = x z = 3 + y
Copy propagation would yield:
z = 3 + x
• Induction Variable
– The value of the variable changes value every time
– With each iteration, its value either gets incremented
or decremented by some constant value.