4. Профилирование программ «Main_p» и «Main_c»
Исходная программа на Си ++ показала следующие результаты:
Time Counts
#include<iostream.h>
#include<math.h>
#include<conio.h>
#include<stdio.h>
float x,er,ec,res1;
int done;
0 0 float erf(float x)
{
0 0 const sqrtpi = 1.7724538,
0 0 t2 = 0.66666667,
0 0 t3 = 0.66666667,
0 0 t4 = 0.07619048,
0 0 t5 = 0.01693122,
0 0 t6 = 3.078403E-3,
0 0 t7 = 4.736005E-4,
0 0 t8 = 6.314673E-5,
0 0 t9 = 7.429027E-6,
0 0 t10 = 7.820028E-7,
0 0 t11 = 7.447646E-8,
0 0 t12 = 6.476214E-9;
float x2,sum;
int i;
0 0 x2 = x*x;
0 0 sum = t5+x2*(t6+x2*(t7+x2*(t8+x2*(t9+x2*(t10+x2*(t11+x2*t1
0 0 res1 = (2.0*exp(-x2)/sqrtpi*(x*(1+x2*(t2+x2*(t3+x2*(t4+x2*
0 0 return res1;
0 0 }
float res2;
0.0000 6 float erfc( float x )
{
0.0000 6 const sqrtpi = 1.7724538;
float x2,v,sum ;
0.0000 6 x2 = x*x;
0.0000 6 v = 1.0/(2.0*x2);
0.0000 6 sum = v/(1+8*v/(1+9*v/(1+10*v/(1+11*v/(1+12*v)))));
0.0000 6 sum = v/(1+3*v/(1+4*v/(1+5*v/(1+6*v/(1+7*sum)))));
0.0000 6 res2 = (1.0/(exp(x2)*x*sqrtpi*(1+v/(1+2*sum))));
0.0000 6 return res2;
0.0000 6 }
0.0000 1 void main()
{
0.0001 1 clrscr();
0.0000 1 done=0;
0.0000 1 printf("\n");
do
{
0.0007 7 printf("Arg? ");
8.0597 7 scanf("%f",&x);
0.0000 7 if( x < 0.0 ) { done = 1; }
else
{
0.0000 6 if (x==0.0 )
Turbo Profiler Version 2.1 Sun Apr 24 05:12:09 2005
{
0 0 er=0.0;
0 0 ec=1.0;
0 0 }
else
{
0.0000 6 if( x<1.5)
{
0 0 er=erf(x);
0 0 ec=1.0-er;
}
else
{
0.0000 6 ec=erfc(x);
0.0000 6 er=1.0-ec;
}
}
0.0011 6 printf("X= %f",x ," Erf= %f",er,", Erfc= %f",ec);
}
0.0000 7 } while(done== 0);
0.0000 1 }
Результаты профилирования по времени LABCPP.c
Turbo Profiler Version 2.1 Sun Apr 10 05:54:14 2005
Program: E:\LABO\METRO\LAB2\LABCPP.EXE
Execution Profile
Total time: 5.1952 sec
% of total: 99 %
Run: 1 of 1
Filter: All
Show: Time
Sort: Frequency
#LABCPP#55 5.1917 sec 99% |**********************************************
#LABCPP#43 0.0007 sec <1% |
#LABCPP#77 0.0006 sec <1% |
#LABCPP#54 0.0003 sec <1% |
#LABCPP#49 0.0001 sec <1% |
#LABCPP#51 0.0000 sec <1% |
#LABCPP#42 0.0000 sec <1% |
#LABCPP#41 0.0000 sec <1% |
#LABCPP#73 0.0000 sec <1% |
#LABCPP#56 0.0000 sec <1% |
#LABCPP#44 0.0000 sec <1% |
#LABCPP#74 0.0000 sec <1% |
#LABCPP#40 0.0000 sec <1% |
#LABCPP#39 0.0000 sec <1% |
#LABCPP#66 0.0000 sec <1% |
#LABCPP#59 0.0000 sec <1% |
#LABCPP#79 0.0000 sec <1% |
#LABCPP#35 0.0000 sec <1% |
#LABCPP#45 0.0000 sec <1% |
erfc 0.0000 sec <1% |
_main 0.0000 sec <1% |
#LABCPP#50 0.0000 sec <1% |
#LABCPP#80 0.0000 sec <1% |
Результаты профилирования по частоте LABCPP.C
Turbo Profiler Version 2.1 Sun Apr 10 06:04:14 2005
Program: E:\LABO\METRO\LAB2\LABCPP.EXE
Execution Profile
Total time: 5.1952 sec
% of total: 99 %
Run: 1 of 1
Filter: All
Show: Counts
Sort: Frequency
#LABCPP#79 3 6% |++++++++++++++++++++++++++++++++++++++++++++++
#LABCPP#56 3 6% |++++++++++++++++++++++++++++++++++++++++++++++
#LABCPP#54 3 6% |++++++++++++++++++++++++++++++++++++++++++++++
#LABCPP#55 3 6% |++++++++++++++++++++++++++++++++++++++++++++++
#LABCPP#73 2 4% |++++++++++++++++++++++++++++++
#LABCPP#42 2 4% |++++++++++++++++++++++++++++++
#LABCPP#43 2 4% |++++++++++++++++++++++++++++++
#LABCPP#44 2 4% |++++++++++++++++++++++++++++++
#LABCPP#45 2 4% |++++++++++++++++++++++++++++++
#LABCPP#41 2 4% |++++++++++++++++++++++++++++++
#LABCPP#77 2 4% |++++++++++++++++++++++++++++++
erfc 2 4% |++++++++++++++++++++++++++++++
#LABCPP#74 2 4% |++++++++++++++++++++++++++++++
#LABCPP#40 2 4% |++++++++++++++++++++++++++++++
#LABCPP#39 2 4% |++++++++++++++++++++++++++++++
#LABCPP#59 2 4% |++++++++++++++++++++++++++++++
#LABCPP#35 2 4% |++++++++++++++++++++++++++++++
#LABCPP#66 2 4% |++++++++++++++++++++++++++++++
#LABCPP#50 1 2% |+++++++++++++++
#LABCPP#51 1 2% |+++++++++++++++
_main 1 2% |+++++++++++++++
#LABCPP#49 1 2% |+++++++++++++++
#LABCPP#80 1 2% |+++++++++++++++
Исходная программа на Паскале показала следующие результаты:
Program: E:\LABO\METRO\LAB2\LABPAS.EXE File LABPAS.PAS
Time Counts
program erfd4;
{ evaluation of the gaussian error function }
var x,er,ec : real;
done : boolean;
function erf(x: real): real;
{ infinite series expansion of the Gaussian error function }
const sqrtpi = 1.7724538;
t2 = 0.66666667;
t3 = 0.66666667;
t4 = 0.07619048;
t5 = 0.01693122;
t6 = 3.078403E-3;
t7 = 4.736005E-4;
t8 = 6.314673E-5;
t9 = 7.429027E-6;
t10 = 7.820028E-7;
t11 = 7.447646E-8;
t12 = 6.476214E-9;
var x2,sum : real;
i : integer;
0 0 begin
0 0 x2:=x*x;
0 0 sum:=t5+x2*(t6+x2*(t7+x2*(t8+x2*(t9+x2*(t10+x2*(t11+x2*t12))))))
0 0 erf:=2.0*exp(-x2)/sqrtpi*(x*(1+x2*(t2+x2*(t3+x2*(t4+x2*sum)))))
0 0 end; { function erf }
function erfc(x: real): real;
{ complement of error function }
const sqrtpi = 1.7724538;
var x2,v,sum : real;
0.0000 4 begin
0.0000 4 x2:=x*x;
0.0000 4 v:=1.0/(2.0*x2);
0.0000 4 sum:=v/(1+8*v/(1+9*v/(1+10*v/(1+11*v/(1+12*v)))));
0.0000 4 sum:=v/(1+3*v/(1+4*v/(1+5*v/(1+6*v/(1+7*sum)))));
0.0000 4 erfc:=1.0/(exp(x2)*x*sqrtpi*(1+v/(1+2*sum)))
0.0000 4 end; { function ercf }
0.0005 1 begin { main }
0.0000 1 done:=false;
0.0000 1 writeln;
repeat
0.0004 5 write('Arg? ');
5.9373 5 readln(x);
0.0000 5 if x<0.0 then done:=true
else
begin
0.0000 4 if x=0.0 then
begin
0 0 er:=0.0;
Turbo Profiler Version 2.1 Sun Apr
Результаты профилирования по времени LABp.pas
Program: E:\LABO\METRO\LAB2\LABPAS.EXE
Execution Profile
Total time: 5.9421 sec
% of total: 100%
Run: 1 of 1
Filter: All
Show: Time
Sort: Frequency
ERFD4.53 5.9373 sec 99% |**********************************************
ERFD4.75 0.0031 sec <1% |
ERFD4.47 0.0005 sec <1% |
ERFD4.78 0.0005 sec <1% |
ERFD4.52 0.0004 sec <1% |
ERFD4.50 0.0000 sec <1% |
ERFD4.71 0.0000 sec <1% |
ERFD4.54 0.0000 sec <1% |
ERFD4.77 0.0000 sec <1% |
ERFD4.44 0.0000 sec <1% |
ERFD4.42 0.0000 sec <1% |
ERFD4.41 0.0000 sec <1% |
ERFD4.ERFC 0.0000 sec <1% |
ERFD4.57 0.0000 sec <1% |
ERFD4.64 0.0000 sec <1% |
ERFD4.43 0.0000 sec <1% |
ERFD4.72 0.0000 sec <1% |
ERFD4.45 0.0000 sec <1% |
ERFD4.40 0.0000 sec <1% |
ERFD4.49 0.0000 sec <1% |
Результаты профилирования по частоте LABp.pas
Program: E:\LABO\METRO\LAB2\LABPAS.EXE
Execution Profile
Total time: 5.9421 sec
% of total: 100%
Run: 1 of 1
Filter: All
Show: Counts
Sort: Frequency
ERFD4.52 5 6% |++++++++++++++++++++++++++++++++++++++++++++++
ERFD4.77 5 6% |++++++++++++++++++++++++++++++++++++++++++++++
ERFD4.53 5 6% |++++++++++++++++++++++++++++++++++++++++++++++
ERFD4.54 5 6% |++++++++++++++++++++++++++++++++++++++++++++++
ERFD4.ERFC 4 5% |++++++++++++++++++++++++++++++++++++
ERFD4.44 4 5% |++++++++++++++++++++++++++++++++++++
ERFD4.45 4 5% |++++++++++++++++++++++++++++++++++++
ERFD4.72 4 5% |++++++++++++++++++++++++++++++++++++
ERFD4.40 4 5% |++++++++++++++++++++++++++++++++++++
ERFD4.75 4 5% |++++++++++++++++++++++++++++++++++++
ERFD4.43 4 5% |++++++++++++++++++++++++++++++++++++
ERFD4.42 4 5% |++++++++++++++++++++++++++++++++++++
ERFD4.41 4 5% |++++++++++++++++++++++++++++++++++++
ERFD4.64 4 5% |++++++++++++++++++++++++++++++++++++
ERFD4.57 4 5% |++++++++++++++++++++++++++++++++++++
ERFD4.71 4 5% |++++++++++++++++++++++++++++++++++++
ERFD4.47 1 1% |+++++++++
ERFD4.49 1 1% |+++++++++
ERFD4.50 1 1% |+++++++++
ERFD4.78 1 1% |+++++++++
Сравнивая результаты, полученные при профилировании программ на Паскале и Си++, можно выявить следующие закономерности:
Среднее время, затрачиваемое на выполнение каждой строки кода на Паскале заметно меньше, чем аналогичное время на Си;
Общее время выполнения программы на Паскале больше, чем время выполнения программы на Си. Статистику времени для программы на Паскале портит сомнительная строка «5.9373 5 .. readln(x)»;