Code Optimization
• It refers to the techniques used by the compiler
to improve execution efficiency of generated
object code.
• It involves complex analysis of the
intermediate code and the performance of
various transformations but every optimizing
transformation must also preserve the semantic
of the program.
Types Of Code Optimization
Code Optimization
Machine Independent
Optimization
can
be
performing independently
of the target machine for
which
compiler
is
generating code.
Machine Dependent
Requires the knowledge
of target machine.
Basic blocks
For performing code optimization we need to
• Detect the loops
• Detection is performed by Control Flow
Analysis on Program Flow Graph.
Atomic Block is sequence of 3-address
statement where control enters at the beginning
and leaves only at the end without any jumps or
halts.
Finding the basic blocks
In order to find the basic blocks, we need to find
the leaders in the program then a basic block
will start from one leader to next leader but not
including the next leader.
Identifying the leaders in the basic
blocks
1. First statement is a leader.
2. Any statement which is the target of conditional
or unconditional statement is a leader.
3. Any statement which immediately follows a
conditional statement or unconditional statement
is a leader.
Identifying the leaders in the basic
e.g:- blocks
fact(x)
{
int f = 1;
for (i =2; i<=x; i++)
{
f = f*i ;
}
return f;
}
Identifying the leaders in the basic
blocks
Three Address Code
Leader 1 1. f=1
2. i=2
Leader 2
Leader 3
Leader 4
3. if i<=x goto 9
4.
5.
6.
7.
8.
t1 = f*i ;
f = t1 ;
t2 = i+1 ;
i = t2 ;
goto(3)0
9. goto calling program
Block 1
Block 2
Block 3
Block 4
Flow Graph
• Directed graph represents the basic blocks and their successor
relationship.
• The nodes of the flow graph are the basic blocks. One node is
distinguished as initial (block whose leader is first statement).
• There is a directed edge from block B1 to block B2 if B2 could
immediately follow B1 during execution that is if:
a) There is a conditional or unconditional jump from the last
statement of B1 to the first statement of B2 or
b) B2 immediately follows B1 does not end in an
unconditional jump.
• We say that B1 is predecessor of B2 and B2 is successor of B1
Identifying the leaders in the basic
blocks
Three Address Code
Leader 1 1. f=1
2. i=2
Leader 2
Leader 3
Leader 4
3. if i<=x goto 9
4.
5.
6.
7.
8.
t1 = f*i ;
f = t1 ;
t2 = i+1 ;
i = t2 ;
goto(3)0
9. goto calling program
Block 1
Block 2
Block 3
Block 4
Flow Graph
Loop Optimization
The Topics under Machine Independent Optimization are as
follows:
1. Loop Optimization
a) Code Motion or Frequency reduction
b) Loop Unrolling
c) Loop Jamming
2. Strength Reduction
3. Constant Folding
4. Copy Propagation
5. Redundancy Elimination
6. Algebraic Simplification
1. Loop Optimization
• while, do-while, for statements give rise to loops in
program
• Most of the execution time is spent in relatively little
of the program .
• 90-10 rule- 90% of time is spent in 10% of code. Thus
the most heavily travelled part of a program, the
inner loops are an obvious target for optimization
• removal of loop- invariant computations
• elimination of induction variables
1- a) Code Motion or Frequency Reduction
The running time of program may be improved if we
decrease the length of one of its loop especially an inner
loop even if we increase the amount of code outside the
loops.
Code Motion is moving a code from high frequency regions
to low frequency regions.
e.g:A=30, i=1,t= sin(A)*cos(A) ;
A=30,i=1;
Code Motion while (i<=10000)
while (i<=10000)
{
{
i= t+i ;
i= (sin(A)*cos(A))+i ;
}
}
1- b) Loop Unrolling
loop unrolling is also known as loop unwinding , is a loop
transformation technique that attempts to optimize
program execution speed at the expense of binary size,
decreasing the number of comparisons.
e.g:i=0;
i= 0;
while (i<10000)
{
while (i<10000)
x[i] = i;
{
Loop Unrolling
i = i+1;
x[i] = i;
x[i] = i;
i = i+1;
i = i+1;
}
}
It checks for 10000 times
It checks for 5000 times
1- c) Loop jamming
loop jamming is combining two or more loops in a single loop. It reduces the
time taken to compile many number of loops.
i=1 ;
i=1 ;
while (i<10000)
while (i<10000)
{
{
x = 5+i;
y = 25+i;
i = i+1;
i = i+1;
}
}
Comparison = 10000
Statement execution= 20000
Comparison = 10000
Statement execution= 20000
i=1 ;
while (i<10000)
{
x = 5+i ;
y = 25+i;
i = i+1;
}
Comparison = 10000
Statement execution= 3000
2) Strength Reduction
• The replacement of expensive operation by a
cheaper one is termed as reduction in strength.
• Costly operation is reduced by cheaper
operation. * is costly operation whereas + is
less costly.
e.g.
4*i  computation time is more
i+i+i+i  computation time is less
3) Constant folding
The Computation of constant is done at compile
time instead of execution time.
Replace an expression that can be computed at
compile time by its values.
e.g.
y = x+5+4+4+3+2
y = x+14
4) Copy Propagation
The value of variable is replaced and computation
of an expression is done at compile time.
e.g.
x=5
y = 4 Copy Propagation z =5+4 Constant Folding z = 9
z= x+y
DAG REPRESENTATION OF BASIC
BLOCKS
• For analyzing basic blocks
• A DAG is a directed graph with no cycles which give a picture
of how the value computed by each statement in a basic block
is used in subsequent statements in the block.
• Constructing a DAG for three address code can give following
information.
 Determining common subexpression within a block.
 Determining which names are used inside the block but
evaluated outside the block (those for which a leaf is
created at same time).
 Determining which statements of the block could have their
value used outside the block.
• In DAG notes are given following labels
 Leaves are labeled by unique identifiers either
variable names or constants.
 Interior notes are labeled by an operator symbol.
 Notes are also optionally given an extra set of
identifiers for labels.
e.g. (a+b)(a+b+c)
from infix expression we draw a parse tree
*
+
+
a
b
c
+
a
b
t1= a+b
t2=a+b
t3=t2*c
t4= t1*t3
e.g. the postfix expression of above expression is
ab+*ab+c
Now we draw a DAG which is optimized in nature
t1= a+b
t2= t1+c
t3= t1* t2
DAG vs Flow Graph
• Each node of the flow graph can be
represented by a DAG, since each node of the
flow graph stands for a basic block.
• DAG representation of a basic block is nothing
more nor less then an optimized version of
“triples” form of intermediate code.
VALUE METHOD AND ALGEBRAIC
LAWS
• To implement DAG construction algorithm, it is
required to check whether a node with specified
children and a specified operator existed. for this
the entire list of created nodes needs to be
examined.
• To determine whether such node exists or not, a
data structure such as hash table can be used .
• This idea was given by Cocke and Schwartz.
• A node of the DAG is really just a pointer into an
array or arrays, holds the information about the
node.
ALGEBRAIC SIMPLIFICATION
Create node with left child m and right child n with
operator + , we should certainly check whether such a
node already exists. But on most m/c multiplication is
commutative, so we should also check for a node
having operator *, left child m and right child n.
a = m+n
b = n+m
* ,a
m
n n
* ,b
m
+ ,a
m
n
+ ,b
n
m
ALGEBRAIC SIMPLIFICATION
The associative law may also be applied to
improve code generated from a DAG.
e.g.
A = B+C
E = C+D+B
E= A+D
Eliminating trivial Simplification
y = z*1
y=z
y=z+0
y=z
Global data flow analysis
• To efficiently optimize the code compiler collects all the
information about the program and distribute this information
to each block of the flow graph. This process is known as dataflow graph analysis.
• Certain optimization can only be achieved by examining the
entire program. It can't be achieve by examining just a portion
of the program.
• For this kind of optimization user defined chaining is one
particular problem.
• Here using the value of the variable, we try to find out that
which definition of a variable is applicable in a statement.
Example-1
• Based on the local information a compiler can perform some
optimizations.
• Ex: x = a + b;
x = 6 * 3;
• In this code, the first assignment of x is useless. The value
computed for x is never used in the program.
• At compile time the expression 6*3 will be computed,
simplifying the second assignment statement to x = 18;
Example-2
• Some optimization needs more global information. For
example, consider the following code:
a = 1;
b = 2;
c = 3;
if (....) x = a + 5;
else x = b + 4;
c = x + 1;
• In this code, at line 3 the initial assignment is useless and x +1
expression can be simplified as 7.
Global data flow analysis
• But it is less obvious that how a compiler can discover these
facts by looking only at one or two consecutive statements. A
more global analysis is required so that the compiler knows the
following things at each point in the program.
 Which variables are guaranteed to have constant values.
 Which variables will be used before being redefined.
• Data flow analysis is used to discover this kind of property.
The data flow analysis can be performed on the program's
control flow graph (CFG).
• The control flow graph of a program is used to determine
those parts of a program to which a particular value assigned
to a variable might propagate.