Writing Efficient C and C Code Optimization

In this
article, I have gathered all the experiences and information, which can
be applied to make a C code optimized for speed as well as memory.

Introduction

During a project for developing a light JPEG library which is enough
to run on a mobile device without compromising quality graphics on a
mobile device, I have seen and worked out a number of ways in which a
given computer program can be made to run faster. In this article, I
have gathered all the experiences and information, which can be applied
to make a C code optimized for speed as well as memory.
Although a number of guidelines are available for C code
optimization, there is no substitute for having a thorough knowledge of
the compiler and machine for which you are programming.
Often, speeding up a program can also cause the code's size to
increase. This increment in code size can also have an adverse effect
on a program's complexity and readability. It will not be acceptable if
you are programming for small device like mobiles, PDAs etc., which
have strict memory restrictions. So, during optimization, our motto
should be to write the code in such a way that memory and speed both
will be optimized.

Declaration

Actually, during my project, I have used the tips from this
for optimization ARM because my project was on ARM platform, but I have
also used many other articles from Internet. All tips of every article
do not work well, so I collect only those tips together, which are very
useful and very efficient. Also, I have modified some of them in such a
way that they are almost applicable for all the environments apart from
ARM.
What I did is just make a collection of the information from various
sites but mostly from that PDF file I mentioned above. I never claimed
that these are my own discoveries. I have mentioned all information
sources in the References section at the end of this article.

Where it is needed?

Without this point, no discussion can be started. First and the most
important part of optimizing a computer program is to find out where to
optimize, which portion or which module of the program is running slow
or using huge memory. If each part is separately being optimized then
the total program will be automatically faster.
The optimizations should be done on those parts of the program that
are run the most, especially those methods which are called repeatedly
by various inner loops that the program can have.
For an experienced programmer, it will usually be quite easy to find
out the portions where a program requires the most optimization
attention. But there are a lot of tools also available for detecting
those parts of a program. I have used Visual C ++ IDE's in-built
profiler to find out where the program spends most click tricks.
Another tool I have used is Intel Vtune, which is a very good profiler
for detecting the slowest parts of a program. In my experience, it will
usually be a particular inner or nested loop, or a call to some third
party library methods, which is the main culprit for running the
program slow.

Integers

We should use

unsigned int

instead of

int

if we know the value will never be negative. Some processors can handle
unsigned integer arithmetic considerably faster than signed (this is
also good practice, and helps make for self-documenting code).
So, the best declaration for an

int

variable in a tight loop would be:

register unsigned int variable_name;

although, it is not guaranteed that the compiler will take any notice of

register

, and

unsigned

may make no difference to the processor. But it may not be applicable for all compilers.
Remember, integer arithmetic is much faster than floating-point
arithmetic, as it can usually be done directly by the processor, rather
than relying on external FPUs or floating point math libraries.
We need to be accurate to two decimal places (e.g. in a simple
accounting package), scale everything up by 100, and convert it back to
floating point as late as possible.

Division and Remainder

In standard processors, depending on the numerator and denominator,
a 32 bit division takes 20-140 cycles to execute. The division function
takes a constant time plus a time for each bit to divide.

Time (numerator / denominator) = C0 + C1* log2 (numerator / denominator)
= C0 + C1 * (log2 (numerator) - log2 (denominator)).

The current version takes about 20 + 4.3N cycles for an ARM
processor. As an expensive operation, it is desirable to avoid it where
possible. Sometimes, such expressions can be rewritten by replacing the
division by a multiplication. For example,

(a / b) > c

can be rewritten as

a > (c * b)

if it is known that

b

is positive and

b *c

fits in an integer. It will be better to use unsigned division by
ensuring that one of the operands is unsigned, as this is faster than
signed division.

Combining division and remainder

Both dividend (

x / y

) and remainder (

x % y

)
are needed in some cases. In such cases, the compiler can combine both
by calling the division function once because as it always returns both
dividend and remainder. If both are needed, we can write them together
like this example:

int func_div_and_mod (int a, int b) {
return (a / b) + (a % b);
}

Division and remainder by powers of two

We can make a division more optimized if the divisor in a division
operation is a power of two. The compiler uses a shift to perform the
division. Therefore, we should always arrange, where possible, for
scaling factors to be powers of two (for example, 64 rather than 66).
And if it is unsigned, then it will be more faster than the signed
division.

typedef unsigned int uint;

uint div32u (uint a) {
return a / 32;
}
int div32s (int a){
return a / 32;
}

Both divisions will avoid calling the division function and the
unsigned division will take fewer instructions than the signed
division. The signed division will take more time to execute because it
rounds towards zero, while a shift rounds towards minus infinity.

An alternative for modulo arithmetic

We use remainder operator to provide modulo arithmetic. But it is sometimes possible to rewrite the code using

if

statement checks.
Consider the following two examples:

uint modulo_func1 (uint count)
{
return (++count % 60);
}

uint modulo_func2 (uint count)
{
if (++count >= 60)
count = 0;
return (count);
}

The use of the

if

statement, rather than the remainder operator, is preferable, as it
produces much faster code. Note that the new version only works if it
is known that the range of count on input is 0-59.

Using array indices

If you wished to set a variable to a particular character, depending upon the value of something, you might do this:

switch ( queue ) {
case 0 :   letter = 'W';
break;
case 1 :   letter = 'S';
break;
case 2 :   letter = 'U';
break;
}

Or maybe:

if ( queue == 0 )
letter = 'W';
else if ( queue == 1 )
letter = 'S';
else
letter = 'U';

A neater (and quicker) method is to simply use the value as an index into a character array, e.g.:

static char *classes="WSU";

letter = classes[queue];

Global variables

Global variables are never allocated to registers. Global variables
can be changed by assigning them indirectly using a pointer, or by a
function call. Hence, the compiler cannot cache the value of a global
variable in a register, resulting in extra (often unnecessary) loads
and stores when globals are used. We should therefore not use global
variables inside critical loops.
If a function uses global variables heavily, it is beneficial to
copy those global variables into local variables so that they can be
assigned to registers. This is possible only if those global variables
are not used by any of the functions which are called.
For example:

int f(void);
int g(void);
int errs;
void test1(void)
{
errs += f();
errs += g();
}

void test2(void)
{
int localerrs = errs;
localerrs += f();
localerrs += g();
errs = localerrs;
}

Note that

test1

must load and store the global

errs

value each time it is incremented, whereas

test2

stores

localerrs

in a register and needs only a single instruction.

Using Aliases

Consider the following example -

void func1( int *data )
{
int i;

for(i=0; i<10; i++)
{
anyfunc( *data, i);
}
}

Even though

*data

may never change, the compiler does not know that

anyfunc ()

did not alter it, and so the program must read it from memory each time
it is used - it may be an alias for some other variable that is altered
elsewhere. If we know it won't be altered, we could code it like this
instead:

void func1( int *data )
{
int i;
int localdata;

localdata = *data;
for(i=0; i<10; i++)
{
anyfunc ( localdata, i);
}
}

This gives the compiler better opportunity for optimization.

Live variables and spilling

As any processor has a fixed set of registers, there is a limit to
the number of variables that can be kept in registers at any one point
in the program.
Some compilers support live-range splitting, where a variable can be
allocated to different registers as well as to memory in different
parts of the function. The live-range of a variable is defined as all
statements between the last assignment to the variable, and the last
usage of the variable before the next assignment. In this range, the
value of the variable is valid, thus it is alive. In between live
ranges, the value of a variable is not needed: it is dead, so its
register can be used for other variables, allowing the compiler to
allocate more variables to registers.
The number of registers needed for register-allocatable variables is
at least the number of overlapping live-ranges at each point in a
function. If this exceeds the number of registers available, some
variables must be stored to memory temporarily. This process is called
spilling.
The compiler spills the least frequently used variables first, so as
to minimize the cost of spilling. Spilling of variables can be avoided
by:
Limiting the maximum number of live variables: this is typically
achieved by keeping expressions simple and small, and not using too
many variables in a function. Subdividing large functions into smaller,
simpler ones might also help.
Using register for frequently-used variables: this tells the
compiler that the register variable is going to be frequently used, so
it should be allocated to a register with a very high priority.
However, such a variable may still be spilled in some circumstances.

Variable Types

The C compilers support the basic types

char

,

short

,

int

and

long

(signed and unsigned),

float

and

double

.
Using the most appropriate type for variables is very important, as it
can reduce code and data size and increase performance considerably.

Local variables

Where possible, it is best to avoid using

char

and

short

as local variables. For the types

char

and

short

,
the compiler needs to reduce the size of the local variable to 8 or 16
bits after each assignment. This is called sign-extending for signed
variables and zero extending for unsigned variables. It is implemented
by shifting the register left by 24 or 16 bits, followed by a signed or
unsigned shift right by the same amount, taking two instructions
(zero-extension of an unsigned

char

takes one instruction).
These shifts can be avoided by using

int

and

unsigned int

for local variables. This is particularly important for calculations
which first load data into local variables and then process the data
inside the local variables. Even if data is input and output as 8- or
16-bit quantities, it is worth considering processing them as 32-bit
quantities.
Consider the following three example functions:

int wordinc (int a)
{
return a + 1;
}
short shortinc (short a)
{
return a + 1;
}
char charinc (char a)
{
return a + 1;
}

The results will be identical, but the first code segment will run faster than others.

Pointers

If possible, we should pass structures by reference, that is pass a
pointer to the structure, otherwise the whole thing will be copied onto
the stack and passed, which will slow things down. I've seen programs
that pass structures several Kilo Bytes in size by value, when a simple
pointer will do the same thing.
Functions receiving pointers to structures as arguments should
declare them as pointer to constant if the function is not going to
alter the contents of the structure. As an example:

void print_data_of_a_structure ( const Thestruct  *data_pointer)
{
...printf contents of the structure...
}

This example informs the compiler that the function does not alter
the contents (as it is using a pointer to constant structure) of the
external structure, and does not need to keep re-reading the contents
each time they are accessed. It also ensures that the compiler will
trap any accidental attempts by your code to write to the read-only
structure and give an additional protection to the content of the
structure.

Pointer chains

Pointer chains are frequently used to access information in structures. For example, a common code sequence is:

typedef struct { int x, y, z; } Point3;
typedef struct { Point3 *pos, *direction; } Object;

void InitPos1(Object *p)
{
p->pos->x = 0;
p->pos->y = 0;
p->pos->z = 0;
}

However, this code must reload

p->pos

for each assignment, because the compiler does not know that

p->pos->x

is not an alias for

p->pos

. A better version would cache

p->pos

in a local variable:

void InitPos2(Object *p)
{
Point3 *pos = p->pos;
pos->x = 0;
pos->y = 0;
pos->z = 0;
}

Another possibility is to include the

Point3

structure in the

Object

structure, thereby avoiding pointers completely.

Conditional Execution

Conditional execution is applied mostly in the body of

if

statements, but it is also used while evaluating complex expressions with relational (

<

,

==

,

>

and so on) or boolean operators (

&&

,

!

,
and so on). Conditional execution is disabled for code sequences which
contain function calls, as on function return the flags are destroyed.
It is therefore beneficial to keep the bodies of

if

and

else

statements as simple as possible, so that they can be conditionalized.
Relational expressions should be grouped into blocks of similar
conditions.
The following example shows how the compiler uses conditional execution:

int g(int a, int b, int c, int d)
{
if (a > 0 && b > 0 && c < 0 && d < 0)
//  grouped conditions tied up together//

return a + b + c + d;
return -1;
}

As the conditions were grouped, the compiler was able to conditionalize them.

Boolean Expressions & Range checking

A common boolean expression is used to check whether a variable lies
within a certain range, for example, to check whether a graphics
co-ordinate lies within a window:

bool PointInRectangelArea (Point p, Rectangle *r)
{
return (p.x >= r->xmin && p.x < r->xmax &&
p.y >= r->ymin && p.y < r->ymax);
}

There is a faster way to implement this:

(x >= min && x < max)

can be transformed into

(unsigned)(x-min) < (max-min)

. This is especially beneficial if

min

is zero. The same example after this optimization:

bool PointInRectangelArea (Point p, Rectangle *r)
{
return ((unsigned) (p.x - r->xmin) < r->xmax &&
(unsigned) (p.y - r->ymin) < r->ymax);

}

Boolean Expressions & Compares with zero

The Processor flags are set after a compare (i.e.

CMP

) instruction. The flags can also be set by other operations, such as

MOV

,

ADD

,

AND

,

MUL

,
which are the basic arithmetic and logical instructions (the data
processing instructions). If a data processing instruction sets the
flags, the

N

and

Z

flags are set the same way as if the result was compared with zero. The

N

flag indicates whether the result is negative, the

Z

flag indicates that the result is zero.
The

N

and

Z

flags on the processor correspond to the signed relational operators

x < 0

,

x >= 0

,

x == 0

,

x != 0

, and unsigned

x == 0

,

x != 0

(or

x > 0

) in C.
Each time a relational operator is used in C, the compiler emits a
compare instruction. If the operator is one of the above, the compiler
can remove the compare if a data processing operation preceded the
compare. For example:

int aFunction(int x, int y)
{
if (x + y < 0)
return 1;
else
return 0;
}

If possible, arrange for critical routines to test the above
conditions. This often allows you to save compares in critical loops,
leading to reduced code size and increased performance. The C language
has no concept of a carry flag or overflow flag, so it is not possible
to test the

C

or

V

flag bits directly without using inline assembler. However, the
compiler supports the carry flag (unsigned overflow). For example:

int sum(int x, int y)
{
int res;
res = x + y;
if ((unsigned) res < (unsigned) x) // carry set?  //

res++;
return res;
}

Lazy Evaluation Exploitation

In a

if(a>10 && b=4)

type of thing, make sure that the first part of the AND expression is
the most likely to give a false answer (or the easiest/quickest to
calculate), therefore the second part will be less likely to be
executed.

switch() instead of if...else...

For large decisions involving

if

...

else

...

else

..., like this:

if( val == 1)
dostuff1();
else if (val == 2)
dostuff2();
else if (val == 3)
dostuff3();

It may be faster to use a

switch

:

switch( val )
{
case 1: dostuff1(); break;

case 2: dostuff2(); break;

case 3: dostuff3(); break;
}

In the

if()

statement, if the last case is required, all the previous ones will be tested first. The

switch

lets us cut out this extra work. If you have to use a big

if

..

else

.. statement, test the most likely cases first.

Binary Breakdown

Break things down in a binary fashion, e.g. do not have a list of:

if(a==1) {
} else if(a==2) {
} else if(a==3) {
} else if(a==4) {
} else if(a==5) {
} else if(a==6) {
} else if(a==7) {
} else if(a==8)

{
}

Have instead:

if(a<=4) {
if(a==1)     {
}  else if(a==2)  {
}  else if(a==3)  {
}  else if(a==4)   {

}
}
else
{
if(a==5)  {
} else if(a==6)   {
} else if(a==7)  {
} else if(a==8)  {
}
}

Or even:

if(a<=4)
{
if(a<=2)
{
if(a==1)
{
/* a is 1 */
}
else
{
/* a must be 2 */
}
}
else
{
if(a==3)
{
/* a is 3 */
}
else
{
/* a must be 4 */
}
}
}
else
{
if(a<=6)
{
if(a==5)
{
/* a is 5 */
}
else
{
/* a must be 6 */
}
}
else
{
if(a==7)
{
/* a is 7 */
}
else
{
/* a must be 8 */
}
}
}

Slow and Inefficient	Fast and Efficient
c=getch(); switch(c){ case 'A': { do something; break; } case 'H': { do something; break; } case 'Z': { do something; break; } }	c=getch(); switch(c){ case 0: { do something; break; } case 1: { do something; break; } case 2: { do something; break; } }

Compare between the two Case statements

Switch statement vs. lookup tables

The

switch

statement is typically used for one of the following reasons:
To call to one of several functions.

To set a variable or return a value.

To execute one of several fragments of code.

If the

case

labels are dense, in the first two uses of

switch

statements, they could be implemented more efficiently using a lookup
table. For example, two implementations of a routine that disassembles
condition codes to strings:

char * Condition_String1(int condition) {
switch(condition) {
case 0: return "EQ";
case 1: return "NE";
case 2: return "CS";
case 3: return "CC";
case 4: return "MI";
case 5: return "PL";
case 6: return "VS";
case 7: return "VC";
case 8: return "HI";
case 9: return "LS";
case 10: return "GE";
case 11: return "LT";
case 12: return "GT";
case 13: return "LE";
case 14: return "";
default: return 0;
}
}

char * Condition_String2(int condition) {
if ((unsigned) condition >= 15) return 0;
return
"EQ\0NE\0CS\0CC\0MI\0PL\0VS\0VC\0HI\0LS\0GE\0LT\0GT\0LE\0\0" +
3 * condition;
}

The first routine needs a total of 240 bytes, the second only 72 bytes.

Loops

Loops are a common construct in most programs; a significant amount
of the execution time is often spent in loops. It is therefore
worthwhile to pay attention to time-critical loops.

Loop termination

The loop termination condition can cause significant overhead if
written without caution. We should always write count-down-to-zero
loops and use simple termination conditions. The execution will take
less time if the termination conditions are simple. Take the following
two sample routines, which calculate n!. The first implementation uses
an incrementing loop, the second a decrementing loop.

int fact1_func (int n)
{
int i, fact = 1;
for (i = 1; i <= n; i++)
fact *= i;
return (fact);
}

int fact2_func(int n)
{
int i, fact = 1;
for (i = n; i != 0; i--)
fact *= i;
return (fact);
}

As a result, the second one

fact2_func

" will be more faster than the first one.

Faster for() loops

It is a simple concept but effective. Ordinarily, we used to code a simple

for()

loop like this:

for( i=0;  i<10;  i++){ ... }

[

i

loops through the values 0,1,2,3,4,5,6,7,8,9 ]
If we needn't care about the order of the loop counter, we can do this instead:

for( i=10; i--; ) { ... }

Using this code,

i

loops through the values 9,8,7,6,5,4,3,2,1,0, and the loop should be faster.
This works because it is quicker to process

i--

as the test condition, which says "Is

i

non-zero? If so, decrement it and continue". For the original code, the processor has to calculate "Subtract

i

from 10. Is the result non-zero? If so, increment

i

and continue.". In tight loops, this makes a considerable difference.
The syntax is a little strange, put is perfectly legal. The third
statement in the loop is optional (an infinite loop would be written as

for( ; ; )

). The same effect could also be gained by coding:

for(i=10; i; i--){}

or (to expand it further):

for(i=10; i!=0; i--){}

The only things we have to be careful of are remembering that the
loop stops at 0 (so if it is needed to loop from 50-80, this wouldn't
work), and the loop counter goes backwards. It's easy to get caught out
if your code relies on an ascending loop counter.
We can also use register allocation, which leads to more efficient
code elsewhere in the function. This technique of initializing the loop
counter to the number of iterations required and then decrementing down
to zero, also applies to

while

and

do

statements.

Loop jamming

Never use two loops where one will suffice. But if you do a lot of
work in the loop, it might not fit into your processor's instruction
cache. In this case, two separate loops may actually be faster as each
one can run completely in the cache. Here is an example.

//Original Code : for(i=0; i<100; i++){ stuff(); } for(i=0; i<100; i++){ morestuff(); }

//It would be better to do: for(i=0; i<100; i++){ stuff(); morestuff(); }

Function Looping

Functions always have a certain performance overhead when they are
called. Not only does the program pointer have to change, but in-use
variables have to be pushed onto a stack, and new variables allocated.
There is much that can be done then to the structure of a program's
functions in order to improve a program's performance. Care must be
taken though to maintain the readability of the program whilst keeping
the size of the program manageable.
If a function is often called from within a loop, it may be possible
to put that loop inside the function to cut down the overhead of
calling the function repeatedly, e.g.:

for(i=0 ; i<100 ; i++)
{
func(t,i);
}
-
-
-
void func(int w,d)
{
lots of stuff.
}

Could become....

func(t);
-
-
-
void func(w)
{
for(i=0 ; i<100 ; i++)
{
//lots of stuff.

}
}

Loop unrolling

Small loops can be unrolled for higher performance, with the
disadvantage of increased code size. When a loop is unrolled, a loop
counter needs to be updated less often and fewer branches are executed.
If the loop iterates only a few times, it can be fully unrolled, so
that the loop overhead completely disappears.
This can make a big difference. It is well known that unrolling loops can produce considerable savings, e.g.:

for(i=0; i<3; i++){ something(i); } //is less efficient than

something(0); something(1); something(2);

because the code has to check and increment the value of

i

each time round the loop. Compilers will often unroll simple loops like
this, where a fixed number of iterations is involved, but something
like:

for(i=0;i< limit;i++) { ... }

is unlikely to be unrolled, as we don't know how many iterations
there will be. It is, however, possible to unroll this sort of loop and
take advantage of the speed savings that can be gained.
The following code (Example 1) is obviously much larger than a
simple loop, but is much more efficient. The block-size of 8 was chosen
just for demo purposes, as any suitable size will do - we just have to
repeat the "loop-contents" the same amount. In this example, the
loop-condition is tested once every 8 iterations, instead of on each
one. If we know that we will be working with arrays of a certain size,
you could make the block size the same size as (or divisible into the
size of) the array. But, this block size depends on the size of the
machine's cache.

//Example 1

#include<STDIO.H>

#define BLOCKSIZE (8)

void main(void)
{
int i = 0;
int limit = 33;  /* could be anything */
int blocklimit;

/* The limit may not be divisible by BLOCKSIZE,
* go as near as we can first, then tidy up.
*/
blocklimit = (limit / BLOCKSIZE) * BLOCKSIZE;

/* unroll the loop in blocks of 8 */
while( i < blocklimit )
{
printf("process(%d)\n", i);
printf("process(%d)\n", i+1);
printf("process(%d)\n", i+2);
printf("process(%d)\n", i+3);
printf("process(%d)\n", i+4);
printf("process(%d)\n", i+5);
printf("process(%d)\n", i+6);
printf("process(%d)\n", i+7);

/* update the counter */
i += 8;

}

/*
* There may be some left to do.
* This could be done as a simple for() loop,
* but a switch is faster (and more interesting)
*/

if( i < limit )
{
/* Jump into the case at the place that will allow
* us to finish off the appropriate number of items.
*/

switch( limit - i )
{
case 7 : printf("process(%d)\n", i); i++;
case 6 : printf("process(%d)\n", i); i++;
case 5 : printf("process(%d)\n", i); i++;
case 4 : printf("process(%d)\n", i); i++;
case 3 : printf("process(%d)\n", i); i++;
case 2 : printf("process(%d)\n", i); i++;
case 1 : printf("process(%d)\n", i);
}
}

}

Population count - counting the number of bits set

This example 1 efficiently tests a single bit by extracting the
lowest bit and counting it, after which the bit is shifted out. The
example 2 was first unrolled four times, after which an optimization
could be applied by combining the four shifts of n into one. Unrolling
frequently provides new opportunities for optimization.

//Example - 1

int countbit1(uint n)
{
int bits = 0;
while (n != 0)
{
if (n & 1) bits++;
n >>= 1;
}
return bits;
}

//Example - 2

int countbit2(uint n)
{
int bits = 0;
while (n != 0)
{
if (n & 1) bits++;
if (n & 2) bits++;
if (n & 4) bits++;
if (n & 8) bits++;
n >>= 4;
}
return bits;
}

Early loop breaking

It is often not necessary to process the entirety of a loop. For
example, if we are searching an array for a particular item, break out
of the loop as soon as we have got what we need. Example: this loop
searches a list of 10000 numbers to see if there is a -99 in it.

found = FALSE;
for(i=0;i<10000;i++)
{
if( list[i] == -99 )
{
found = TRUE;
}
}

if( found ) printf("Yes, there is a -99. Hooray!\n");

This works well, but will process the entire array, no matter where
the search item occurs in it. A better way is to abort the search as
soon as we've found the desired entry.

found = FALSE;
for(i=0; i<10000; i++)
{
if( list[i] == -99 )
{
found = TRUE;
break;
}
}
if( found ) printf("Yes, there is a -99. Hooray!\n");

If the item is at, say position 23, the loop will stop there and then, and skip the remaining 9977 iterations.

Function Design

It is a good idea to keep functions small and simple. This enables
the compiler to perform other optimizations, such as register
allocation, more efficiently.

Function call overhead

Function call overhead on the processor is small, and is often small
in proportion to the work performed by the called function. There are
some limitations up to which words of arguments can be passed to a
function in registers. These arguments can be integer-compatible (

char

,

short

s,

int

s and

float

s all take one word), or structures of up to four words (including the 2-word

double

s and long

long

s).
If the argument limitation is 4, then the fifth and subsequent words
are passed on the stack. This increases the cost of storing these words
in the calling function and reloading them in the called function.
In the following sample code:

int f1(int a, int b, int c, int d) {
return a + b + c + d;
}

int g1(void) {
return f1(1, 2, 3, 4);
}

int f2(int a, int b, int c, int d, int e, int f) {
return a + b + c + d + e + f;
}

ing g2(void) {
return f2(1, 2, 3, 4, 5, 6);
}

the fifth and sixth parameters are stored on the stack in

g2

, and reloaded in

f2

, costing two memory accesses per parameter.

Minimizing parameter passing overhead

To minimize the overhead of passing parameters to functions:
Try to ensure that small functions take four or fewer arguments. These will not use the stack for argument passing.

If a function needs more than four arguments, try to ensure
that it does a significant amount of work, so that the cost of passing
the stacked arguments is outweighed.
Pass pointers to structures instead of passing the structure itself.

Put related arguments in a structure, and pass a pointer to
the structure to functions. This will reduce the number of parameters
and increase readability.
Minimize the number of

long

parameters, as these take two argument words. This also applies to

double

s if software floating-point is enabled.

Avoid functions with a parameter that is passed partially in a
register and partially on the stack (split-argument). This is not
handled efficiently by the current compilers: all register arguments
are pushed on the stack.
Avoid functions with a variable number of parameters. Those functions effectively pass all their arguments on the stack.

Leaf functions

A function which does not call any other functions is known as a
leaf function. In many applications, about half of all function calls
made are to leaf functions. Leaf functions are compiled very
efficiently on every platform, as they often do not need to perform the
usual saving and restoring of registers. The cost of pushing some
registers on entry and popping them on exit is very small compared to
the cost of the useful work done by a leaf function that is complicated
enough to need more than four or five registers. If possible, we should
try to arrange for frequently-called functions to be leaf functions.
The number of times a function is called can be determined by using the
profiling facility. There are several ways to ensure that a function is
compiled as a leaf function:
Avoid calling other functions: this includes any operations which
are converted to calls to the C-library (such as division, or any
floating-point operation when the software floating-point library is
used).
Use

__inline

for small functions which are called from it (inline functions discussed next).

Inline functions

Function inlining is disabled for all debugging options. Functions with the keyword

__inline

results in each call to an inline function being substituted by its
body, instead of a normal call. This results in faster code, but it
adversely affects code size, particularly if the inline function is
large and used often.

__inline int square(int x) {
return x * x;
}

#include <MATH.H>

double length(int x, int y){
return sqrt(square(x) + square(y));
}

There are several advantages to using inline functions:
No function call overhead.
As the code is substituted directly, there is no overhead, like saving and restoring registers.

Lower argument evaluation overhead.
The overhead of parameter passing is generally lower, since it is
not necessary to copy variables. If some of the parameters are
constants, the compiler can optimize the resulting code even further.

The big disadvantage of inline functions is that the code sizes
increase if the function is used in many places. This can vary
significantly depending on the size of the function, and the number of
places where it is used.
It is wise to only inline a few critical functions. Note that when
done wisely, inlining may decrease the size of the code: a call takes
usually a few instructions, but the optimized version of the inlined
code might translate to even less instructions.

Using Lookup Tables

A function can often be approximated using a lookup table, which
increases performance significantly. A table lookup is usually less
accurate than calculating the value properly, but for many
applications, this does not matter.
Many signal processing applications (for example, modem demodulator software) make heavy use of

sin

and

cos

functions, which are computationally expensive to calculate. For real-time systems where accuracy is not very important,

sin

/

cos

lookup tables might be essential. When using lookup tables, try to
combine as many adjacent operations as possible into a single lookup
table. This is faster and uses less space than multiple lookup tables.

Floating-Point Arithmetic

Although floating point operations are time consuming for any kind
of processors, sometimes we need to used it in case of implementing
signal processing applications. However, when writing floating-point
code, keep the following things in mind:
Floating-point division is slow.
Division is typically twice as slow as addition or multiplication.
Rewrite divisions by a constant into a multiplication with the inverse
(For example,

x = x / 3.0

becomes

x = x * (1.0/3.0)

. The constant is calculated during compilation.).

Use

float

s instead of

double

s.
Float variables consume less memory and fewer registers, and are more efficient because of their lower precision. Use

float

s whenever their precision is good enough.

Avoid using transcendental functions.
Transcendental functions, like

sin

,

exp

and

log

are implemented using series of multiplications and additions (using
extended precision). As a result, these operations are at least ten
times slower than a normal multiply.

Simplify floating-point expressions.
The compiler cannot apply many optimizations which are performed on integers to floating-point values. For example,

3 * (x / 3)

cannot be optimized to

x

,
since floating-point operations generally lead to loss of precision.
Even the order of evaluation is important: (a + b) + c is not the same
as a + (b + c). Therefore, it is beneficial to perform floating-point
optimizations manually if it is known they are correct.

However, it is still possible that the floating performance will not
reach the required level for a particular application. In such a case,
the best approach may be to change from using floating-point to fixed
point arithmetic. When the range of values needed is sufficiently
small, fixed-point arithmetic is more accurate and much faster than
floating-point arithmetic.

Misc tips

In general, savings can be made by trading off memory for speed. If
you can cache any often used data rather than recalculating or
reloading it, it will help. Examples of this would be sine/cosine
tables, or tables of pseudo-random numbers (calculate 1000 once at the
start, and just reuse them if you don't need truly random numbers).
Avoid using

++

and

--

etc. within loop expressions. E.g.:

while(n--){}

, as this can sometimes be harder to optimize.

Minimize the use of global variables.

Declare anything within a file (external to functions) as static, unless it is intended to be global.

Use word-size variables if you can, as the machine can work with these better (instead of

char

,

short

,

double

, bit fields etc.).

Don't use recursion. Recursion can be very elegant and neat,
but creates many more function calls which can become a large overhead.

Avoid the

sqrt()

square root function in loops - calculating square roots is very CPU intensive.

Single dimension arrays are faster than multi-dimension arrays.

Compilers can often optimize a whole file - avoid splitting
off closely related functions into separate files, the compiler will do
better if it can see both of them together (it might be able to inline
the code, for example).
Single precision math may be faster than double precision - there is often a compiler switch for this.

Floating point multiplication is often faster than division - use

val * 0.5

instead of

val / 2.0

.

Addition is quicker than multiplication - use

val + val + val

instead of

val * 3

.

puts()

is quicker than

printf()

, although less flexible.

Use

#define

d
macros instead of commonly used tiny functions - sometimes the bulk of
CPU usage can be tracked down to a small external function being called
thousands of times in a tight loop. Replacing it with a macro to
perform the same job will remove the overhead of all those function
calls, and allow the compiler to be more aggressive in its
optimization..
Binary/unformatted file access is faster than formatted
access, as the machine does not have to convert between human-readable
ASCII and machine-readable binary. If you don't actually need to read
the data in a file yourself, consider making it a binary file.
If your library supports the

mallopt()

function (for controlling

malloc

), use it. The

MAXFAST

setting can make significant improvements to code that does a lot of

malloc

work. If a particular structure is created/destroyed many times a second, try setting the

mallopt

options to work best with that size.

Last, but definitely not least - turn compiler optimization on!
Seems obvious, but is often forgotten in that last minute rush to get
the product out on time. The compiler will be able to optimize at a
much lower level than can be done in the source code, and perform
optimizations specific to the target processor.

References

Writing Efficient C for ARM
Document number: ARM DAI 0034A

Issued: January 1998

Copyright Advanced RISC Machines Ltd. (ARM) 1998

Richard's C Optimization page OR: How to make your C, C++ or Java program run faster with little effort.

Code Optimization Using the GNU C Compiler By Rahul U Joshi.

Compile C Faster on Linux [Christopher W. Fraser (Microsoft Research), David R. Hanson (Princeton University)]

CODE OPTIMIZATION - COMPILER [1] [2]
[Thanks to Craig Burley for the excellent comments. Thanks to
Timothy Prince for the note on architectures with Instruction Level
Parallelism].

An Evolutionary Analysis of GNU C Optimizations [Using Natural Selection to Investigate Software Complexities by Scott Robert Ladd. Updated: 16 December 2003]

Other URLs

http://www.xs4all.nl/~ekonijn/loopy.html

http://www.public.asu.edu/~sshetty/Optimizing_Code_Manual.doc

http://www.abarnett.demon.co.uk/tutorial.html

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About the Author

Koushik Ghosh



Software Developer (Senior)