我已经尽可能多地优化了我的顺序运行功能。当我使用 openMP 时,我发现性能没有任何提升。我在 1 核机器和 8 核机器上试了我的程序,性能是一样的。
将年份设置为 20,我有
1 个核心:1 秒。
8 核:1 秒。
将年份设置为 25 我有
1 个核心:40 秒。
8 核:40 秒。
1核机:我笔记本的intel core 2 duo 1.8 GHz,ubuntu linux
8核机:3.25 GHz,ubuntu linux
我的程序枚举了二叉树的所有可能路径,并在每条路径上做一些工作。所以我的循环大小呈指数增长,我希望 openMP 线程的占用空间为零。在我的循环中,我只减少一个变量。所有其他变量都是只读的。我只使用我写的函数,我认为它们是线程安全的。
我还在我的程序上运行 Valgrind cachegrind。我不完全理解输出,但似乎没有缓存未命中或错误共享。
我编译
gcc -O3 -g3 -Wall -c -fmessage-length=0 -lm -fopenmp -ffast-math
我的完整程序如下。很抱歉发布了很多代码。我对 openMP 和 C 都不熟悉,而且我无法在不丢失主要任务的情况下恢复我的代码。
使用 openMP 时如何提高性能?
它们是使程序运行得更快的编译器标志或 C 技巧吗?
测试.c
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <omp.h>
#include "test.h"
int main(){
printf("starting\n");
int year=20;
int tradingdate0=1;
globalinit(year,tradingdate0);
int i;
float v=0;
long n=pow(tradingdate0+1,year);
#pragma omp parallel for reduction(+:v)
for(i=0;i<n;i++)
v+=pathvalue(i);
globaldel();
printf("finished\n");
return 0;
}
//***function on which openMP is applied
float pathvalue(long pathindex) {
float value = -ctx.firstpremium;
float personalaccount = ctx.personalaccountat0;
float account = ctx.firstpremium;
int i;
for (i = 0; i < ctx.year-1; i++) {
value *= ctx.accumulationfactor;
double index = getindex(i,pathindex);
account = account * index;
double death = fmaxf(account,ctx.guarantee[i]);
value += qx(i) * death;
if (haswithdraw(i)){
double withdraw = personalaccount*ctx.allowed;
value += px(i) * withdraw;
personalaccount = fmaxf(personalaccount-withdraw,0);
account = fmaxf(account-withdraw,0);
}
}
//last year
double index = getindex(ctx.year-1,pathindex);
account = account * index;
value+=fmaxf(account,ctx.guarantee[ctx.year-1]);
return value * ctx.discountfactor;
}
int haswithdraw(int period){
return 1;
}
float getindex(int period, long pathindex){
int ndx = (pathindex/ctx.chunksize[period])%ctx.tradingdate;
return ctx.stock[ndx];
}
float qx(int period){
return 0;
}
float px(int period){
return 1;
}
//****global
struct context ctx;
void globalinit(int year, int tradingdate0){
ctx.year = year;
ctx.tradingdate0 = tradingdate0;
ctx.firstpremium = 1;
ctx.riskfreerate = 0.06;
ctx.volatility=0.25;
ctx.personalaccountat0 = 1;
ctx.allowed = 0.07;
ctx.guaranteerate = 0.03;
ctx.alpha=1;
ctx.beta = 1;
ctx.tradingdate=tradingdate0+1;
ctx.discountfactor = exp(-ctx.riskfreerate * ctx.year);
ctx.accumulationfactor = exp(ctx.riskfreerate);
ctx.guaranteefactor = 1+ctx.guaranteerate;
ctx.upmove=exp(ctx.volatility/sqrt(ctx.tradingdate0));
ctx.downmove=1/ctx.upmove;
ctx.stock=(float*)malloc(sizeof(float)*ctx.tradingdate);
int i;
for(i=0;i<ctx.tradingdate;i++)
ctx.stock[i]=pow(ctx.upmove,ctx.tradingdate0-i)*pow(ctx.downmove,i);
ctx.chunksize=(long*)malloc(sizeof(long)*ctx.year);
for(i=0;i<year;i++)
ctx.chunksize[i]=pow(ctx.tradingdate,ctx.year-i-1);
ctx.guarantee=(float*)malloc(sizeof(float)*ctx.year);
for(i=0;i<ctx.year;i++)
ctx.guarantee[i]=ctx.beta*pow(ctx.guaranteefactor,i+1);
}
void globaldel(){
free(ctx.stock);
free(ctx.chunksize);
free(ctx.guarantee);
}
测试.h
float pathvalue(long pathindex);
int haswithdraw(int period);
float getindex(int period, long pathindex);
float qx(int period);
float px(int period);
//***global
struct context{
int year;
int tradingdate0;
float firstpremium;
float riskfreerate;
float volatility;
float personalaccountat0;
float allowed;
float guaranteerate;
float alpha;
float beta;
int tradingdate;
float discountfactor;
float accumulationfactor;
float guaranteefactor;
float upmove;
float downmove;
float* stock;
long* chunksize;
float* guarantee;
};
struct context ctx;
void globalinit();
void globaldel();
编辑我将所有全局变量简化为常量。20 年来,程序运行速度快了两倍(太棒了!)。例如,我尝试设置线程数OMP_NUM_THREADS=4 ./test
。但这并没有给我带来任何性能提升。
我的 gcc 会有问题吗?
测试.c
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <math.h>
#include <omp.h>
#include "test.h"
int main(){
starttimer();
printf("starting\n");
int i;
float v=0;
#pragma omp parallel for reduction(+:v)
for(i=0;i<numberofpath;i++)
v+=pathvalue(i);
printf("v:%f\nfinished\n",v);
endtimer();
return 0;
}
//function on which openMP is applied
float pathvalue(long pathindex) {
float value = -firstpremium;
float personalaccount = personalaccountat0;
float account = firstpremium;
int i;
for (i = 0; i < year-1; i++) {
value *= accumulationfactor;
double index = getindex(i,pathindex);
account = account * index;
double death = fmaxf(account,guarantee[i]);
value += death;
double withdraw = personalaccount*allowed;
value += withdraw;
personalaccount = fmaxf(personalaccount-withdraw,0);
account = fmaxf(account-withdraw,0);
}
//last year
double index = getindex(year-1,pathindex);
account = account * index;
value+=fmaxf(account,guarantee[year-1]);
return value * discountfactor;
}
float getindex(int period, long pathindex){
int ndx = (pathindex/chunksize[period])%tradingdate;
return stock[ndx];
}
//timing
clock_t begin;
void starttimer(){
begin = clock();
}
void endtimer(){
clock_t end = clock();
double elapsed = (double)(end - begin) / CLOCKS_PER_SEC;
printf("\nelapsed: %f\n",elapsed);
}
测试.h
float pathvalue(long pathindex);
int haswithdraw(int period);
float getindex(int period, long pathindex);
float qx(int period);
float px(int period);
//timing
void starttimer();
void endtimer();
//***constant
const int year= 20 ;
const int tradingdate0= 1 ;
const float firstpremium= 1 ;
const float riskfreerate= 0.06 ;
const float volatility= 0.25 ;
const float personalaccountat0= 1 ;
const float allowed= 0.07 ;
const float guaranteerate= 0.03 ;
const float alpha= 1 ;
const float beta= 1 ;
const int tradingdate= 2 ;
const int numberofpath= 1048576 ;
const float discountfactor= 0.301194211912 ;
const float accumulationfactor= 1.06183654655 ;
const float guaranteefactor= 1.03 ;
const float upmove= 1.28402541669 ;
const float downmove= 0.778800783071 ;
const float stock[2]={1.2840254166877414, 0.7788007830714049};
const long chunksize[20]={524288, 262144, 131072, 65536, 32768, 16384, 8192, 4096, 2048, 1024, 512, 256, 128, 64, 32, 16, 8, 4, 2, 1};
const float guarantee[20]={1.03, 1.0609, 1.092727, 1.1255088100000001, 1.1592740743, 1.1940522965290001, 1.2298738654248702, 1.2667700813876164, 1.304773183829245, 1.3439163793441222, 1.384233870724446, 1.4257608868461793, 1.4685337134515648, 1.512589724855112, 1.557967416600765, 1.6047064390987882, 1.6528476322717518, 1.7024330612399046, 1.7535060530771016, 1.8061112346694148};