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Thought this was cool: Google CPU Profiler使用指南及小工具

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Google CPU Profiler使用指南及小工具

–五竹,2012/12/27

(一) gperftools-2.0 编译

wget https://gperftools.googlecode.com/files/gperftools-2.0.tar.gz

tar –xzf gperftools-2.0.tar.gz

cd gperftools-2.0

./configure –prefix=/home/wuzhu/tools/gperftools –enable-frame-pointers

make && make install

注:

编译时打开了 –enable-frame-pointers ,这要求被测试的程序在编译时要加上gcc编译选项,否则某些多线程程序可能会 core:

CCFLAGS=-fno-omit-frame-pointer

(二) google cpu profiler 基本使用

嵌入 google cpu profiler 代码与编译

1) 在我们要测试的程序源码中先 include 头文件,然后在要 profile 的代码前后加上ProfilerStart() 和 ProfilerStop()

其接口声明如下:

#include <google/profiler.h>

int ProfilerStart(const char* fname);
void ProfilerFlush();
void ProfilerStop();

2) 在编译时加上 -fno-omit-frame-pointer 和 链接库 -ltcmalloc_and_profiler

CCFLAGS=-fno-omit-frame-pointer

-ltcmalloc_and_profiler

3) 执行程序,生成 profile 数据文件

4) 分析生成的数据文件

% pprof /bin/ls ls.prof

Enters “interactive” mode

% pprof –text /bin/ls ls.prof

Outputs one line per procedure

% pprof –gv /bin/ls ls.prof

Displays annotated call-graph via ‘gv’

% pprof –gv –focus=Mutex /bin/ls ls.prof

Restricts to code paths including a .*Mutex.* entry

% pprof –gv –focus=Mutex –ignore=string /bin/ls ls.prof

Code paths including Mutex but not string

% pprof –list=getdir /bin/ls ls.prof

(Per-line) annotated source listing for getdir()

% pprof –disasm=getdir /bin/ls ls.prof

(Per-PC) annotated disassembly for getdir()

% pprof –text localhost:1234

Outputs one line per procedure for localhost:1234

% pprof –callgrind /bin/ls ls.prof

Outputs the call information in callgrind format

示例一:

1) cpu_profiler_example.cpp

#include <stdio.h>
#include <string.h>
#include <sys/types.h>
#include <unistd.h>
#include <gperftools/profiler.h>
using namespace std;

int loopop()
{
	char buffer1 [1024];
	char buffer2 [1024];
	int n = 0;
	for(int i = 0; i < 10000000; i++)
	{
		for(int j = 0; j < 1000; j++)
		{
			n |= i%100 + j/100;
		}
		memset(buffer1,0xa,1024);
		memcpy(buffer2,buffer1,1024);
	}
	return n;
}

int main(int argc,char** argv)
{
	char program[1024]={0};
	snprintf(program,1023,"%s_%d.prof",argv[0],getpid());
	ProfilerStart(program);
    printf("result:  %d\n", (loopop)() );
    ProfilerStop();
    return 0;
}

2) Makefile

GPerfTools=/home/wuzhu/tools/gperftools

CCFLAGS=-fno-omit-frame-pointer -g

ALL_BINS=cpu_profiler_example

all:$(ALL_BINS)

cpu_profiler_example :cpu_profiler_example.o

g++ $(CCFLAGS) -o $@ $^ -L./ -L$(GPerfTools)/lib -Wl,-Bdynamic -ltcmalloc_and_profiler

.cpp.o:

g++ $(CCFLAGS) -c -I./ -I$(GPerfTools)/include -fPIC -o $@ $<

clean:

rm -f $(ALL_BINS) *.o

3) 执行

[wuzhu@search041142.sqa.cm4 cpu_profiler]$ ./cpu_profiler_example

loopop: 255

PROFILE: interrupts/evictions/bytes = 5848/3861/185584

会产生性能数据文件: cpu_profiler_example_29502.prof

4). 分析性能数据

pprof –text cpu_profiler_example cpu_profiler_example_29502.prof

Using local file cpu_profiler_example.

Using local file cpu_profiler_example_29502.prof.

Removing killpg from all stack traces.

Removing main from all stack traces.

Removing __libc_start_main from all stack traces.

Total: 5962 samples

5894 98.9% 98.9% 5894 98.9% loopop

38 0.6% 99.5% 38 0.6% memcpy

28 0.5% 100.0% 28 0.5% memset

2 0.0% 100.0% 2 0.0% _init

注:

更详细的使用方法请见 google cpu profiler wiki:

http://gperftools.googlecode.com/svn/trunk/doc/cpuprofile.html

(三) Google CPU Profiler支持对动态链接库进行性能分析

下面,通过示例2来说明 Google Cpu Profiler 是支持对动态链接库进行性能分析的。

1) 源代码

//TestProfiler.h
extern "C"
{
	int loopop();
}

//TestProfiler.cpp只定义了一个耗时计算函数,便于分析。
#include <string.h>
#include "TestProfiler.h"

extern "C"
{
int loopop()
{
	char buffer1 [1024];
	char buffer2 [1024];
	int n = 0;
	for(int i = 0; i < 10000000; i++)
	{
		for(int j = 0; j < 1000; j++)
		{
			n |= i%100 + j/100;
		}
		memset(buffer1,0xa,1024);
		memcpy(buffer2,buffer1,1024);
	}
	return n;
}
}

#include <stdio.h>
#include <string.h>
#include <sys/types.h>
#include <unistd.h>
#include <gperftools/profiler.h>
#include "TestProfiler.h"
using namespace std;

int main(int argc,char** argv)
{
	char program[1024]={0};
	snprintf(program,1023,"%s_%d.prof",argv[0],getpid());
	ProfilerStart(program);
    printf("result:  %d\n", (loopop)() );
    ProfilerStop();
    return 0;
}

2) Makefile

GPerfTools=/home/wuzhu/tools/gperftools

CCFLAGS=-fno-omit-frame-pointer -g

ALL_BINS=libTestProfiler.so main_dynamic_link

all:$(ALL_BINS)

main_dynamic_link :main_dynamic_link.o

g++ $(CCFLAGS) -o $@ $^ -L./ -L$(GPerfTools)/lib -Wl,-Bdynamic -lTestProfiler -ltcmalloc_and_profiler

libTestProfiler.so:TestProfiler.o

g++ -shared $(CCFLAGS) -o $@ $^

.cpp.o:

g++ $(CCFLAGS) -c -I./ -I$(GPerfTools)/include -fPIC -o $@ $<

clean:

rm -f $(ALL_BINS) *.o *.prof

3) 执行

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:`pwd`

./main_dynamic_link

result: 127

PROFILE: interrupts/evictions/bytes = 5991/3915/188368

产生性能数据文件: main_dynamic_link_24327.prof

4) 分析性能数据

pprof –text main_dynamic_link main_dynamic_link_24327.prof

Using local file main_dynamic_link.

Using local file main_dynamic_link_24327.prof.

Removing killpg from all stack traces.

Removing main from all stack traces.

Removing __libc_start_main from all stack traces.

Total: 5991 samples

5899 98.5% 98.5% 5899 98.5% loopop

53 0.9% 99.3% 53 0.9% memcpy

38 0.6% 100.0% 38 0.6% memset

1 0.0% 100.0% 1 0.0% _init

由此证明,Google CPU Profiler支持对动态链接库的性能分析

(四) Google CPU Profiler 对用 dlopen 方式打开动态库的程序支持

运行时加载允许程序可以有选择地调用库中的函数。使用动态加载过程,程序可以先加载一个特定的库(已加载则不必),然后调用该库中的某一特定函数,这是构建支持插件的应用程序的一个普遍的方法。

示例 3 主要演示 Google CPU Profiler 是否支持对用 dlopen 方式打开动态库的程序进行性能分析。

还是以上述程序为例,对主程序代码进修改。

1) 源代码 (libTestProfiler.so 的源码同示例 2)

#include <stdio.h>
#include <stdlib.h>
#include <dlfcn.h>
#include <string.h>
#include <sys/types.h>
#include <unistd.h>
#include <gperftools/profiler.h>

typedef int (*op_t) ();

int main(int argc,char** argv)
{
	int not_close=0;
	if(argc >= 2)
		not_close=atoi(argv[1]);
	void* dl_handle=NULL;
	op_t loopop;
	char* error=NULL;

	char program[1024]={0};
	snprintf(program,1023,"%s_%d.prof",argv[0],getpid());
	ProfilerStart(program);

	dl_handle = dlopen("./libTestProfiler.so", RTLD_LAZY );
	if (!dl_handle)
	{
		printf( "dlopen failed! %s\n", dlerror() );
		return -1;
	}

	loopop = (op_t)dlsym( dl_handle, "loopop");
	error = dlerror();
	if (error != NULL)
	{
		printf( "dlsym failed! %s\n", error );
		return -1;
	}

    printf("result:  %d\n", (loopop)() );
    if(not_close == 0)
    {
    	printf("do dlclose()\n");
    	dlclose( dl_handle );
    }
    else
    {
    	printf("not dlclose()\n");
    }

    ProfilerStop();
    return 0;
}

2) Makefile

GPerfTools=/home/wuzhu/tools/gperftools

CCFLAGS=-fno-omit-frame-pointer -g

ALL_BINS=libTestProfiler.so main_dlopen_link

all:$(ALL_BINS)

main_dlopen_link :main_dlopen_link.o

g++ $(CCFLAGS) -o $@ $^ -L./ -L$(GPerfTools)/lib -Wl,-Bdynamic -ltcmalloc_and_profiler -ldl

libTestProfiler.so:TestProfiler.o

g++ -shared $(CCFLAGS) -o $@ $^

.cpp.o:

g++ $(CCFLAGS) -c -I./ -I$(GPerfTools)/include -fPIC -o $@ $<

clean:

rm -f $(ALL_BINS) *.o *.prof

3) 运行

[wuzhu@search041142.sqa.cm4 cpu_profiler]$ ./main_dlopen_link

result: 127

do dlclose()

PROFILE: interrupts/evictions/bytes = 5984/3559/171280

产生性能分析数据:main_dlopen_link_1256.prof

4) 分析性能数据

pprof –text main_dlopen_link main_dlopen_link_13598.prof

Using local file main_dlopen_link.

Using local file main_dlopen_link_13598.prof.

Removing killpg from all stack traces.

Removing main from all stack traces.

Removing __libc_start_main from all stack traces.

Total: 5984 samples

801 13.4% 13.4% 801 13.4% 0x00002b124c0b4688

736 12.3% 25.7% 736 12.3% 0x00002b124c0b4652

735 12.3% 38.0% 735 12.3% 0x00002b124c0b4685

425 7.1% 45.1% 425 7.1% 0x00002b124c0b4671

414 6.9% 52.0% 414 6.9% 0x00002b124c0b464b

412 6.9% 58.9% 412 6.9% 0x00002b124c0b4676

401 6.7% 65.6% 401 6.7% 0x00002b124c0b4656

401 6.7% 72.3% 401 6.7% 0x00002b124c0b465c

392 6.6% 78.8% 392 6.6% 0x00002b124c0b4624

387 6.5% 85.3% 387 6.5% 0x00002b124c0b463e

382 6.4% 91.7% 382 6.4% 0x00002b124c0b467f

368 6.1% 97.8% 368 6.1% 0x00002b124c0b4639

45 0.8% 98.6% 45 0.8% memcpy

42 0.7% 99.3% 42 0.7% memset

21 0.4% 99.6% 21 0.4% 0x00002b124c0b468c

9 0.2% 99.8% 9 0.2% 0x00002b124c0b4645

3 0.1% 99.8% 3 0.1% 0x00002b124c0b467d

3 0.1% 99.9% 3 0.1% 0x00002b124c0b469c

2 0.0% 99.9% 2 0.0% 0x00002b124c0b46a6

1 0.0% 99.9% 1 0.0% 0x00002b124c0b4518

1 0.0% 99.9% 1 0.0% 0x00002b124c0b461b

1 0.0% 100.0% 1 0.0% 0x00002b124c0b4643

1 0.0% 100.0% 1 0.0% 0x00002b124c0b4695

1 0.0% 100.0% 1 0.0% 0x00002b124c0b46d4

很奇怪,这个结果显示 libTestProfiler.so 库中的符号没有正确解析,main_dlopen_link_1256.prof 文件也没有包含 libTestProfiler.so 的内存映射信息,但是我们确实在主程序已经通过dlopen将动态库装载到内存并执行成功了,为何在主程序的内存映射表中找不到动态库的信息呢?

经过一番分析和调查,终于找到原因,因为main_dlopen_link_1256.prof 文件的输出工作是在dlclose()函数执行之后调用的,而在此时主程序执行了dlclose()函数卸载了libTestProfiler.so,所以随后dump出的内存映射表当然就不会包含libTestProfiler.so的信息了。

下面,我们通过传入参数,禁止dlclose()

[wuzhu@search041142.sqa.cm4 cpu_profiler]$ ./main_dlopen_link 1

result: 127

not dlclose()

PROFILE: interrupts/evictions/bytes = 5968/3520/169408

pprof –text main_dlopen_link main_dlopen_link_18394.prof

Using local file main_dlopen_link.

Using local file main_dlopen_link_18394.prof.

Removing killpg from all stack traces.

Removing main from all stack traces.

Removing __libc_start_main from all stack traces.

Total: 5968 samples

5893 98.7% 98.7% 5893 98.7% loopop

46 0.8% 99.5% 46 0.8% memcpy

27 0.5% 100.0% 27 0.5% memset

2 0.0% 100.0% 2 0.0% _init

此时,动态库中的符号又能正常解析了。

(五) GoogleProfiler.h 小工具

#ifndef __GOOGLE_PROFILER__
#define __GOOGLE_PROFILER__

#include <stdio.h>
#include <stdint.h>
#include <string>
#ifdef CPU_PROFILER
#include <google/profiler.h>
#endif

class GoogleProfiler
{
public:
	enum
	{
		MAXBUFSIZE=1024
	};
public:
	GoogleProfiler():_ready(false)
	{
	}
	~GoogleProfiler()
	{
		ProfilerStop();
	}
	void ProfilerStart()
	{
#ifdef CPU_PROFILER
		char buf[MAXBUFSIZE]={0};

		int count = readlink("/proc/self/exe",buf, MAXBUFSIZE );
		if(count < 0 || count > MAXBUFSIZE)
			return;
		_programBin.assign(buf);
		printf("ProgramBin=%s\n",_programBin.c_str());
		_pid=getpid();
		size_t pos=_programBin.find_last_of('/');
		if(pos != std::string::npos)
		{
			_programName=_programBin.substr(pos+1);
		}
		else
		{
			_programName=_programBin;
		}
		printf("ProfilerStart(%s)\n",buf);
		snprintf(buf,MAXBUFSIZE,"./%s_%d.prof",_programName.c_str(),_pid);
		::ProfilerStart(buf);
		_ready=true;
#endif
	}
	void ProfilerStop()
	{
	#ifdef CPU_PROFILER
		if(_ready)
		{
			::ProfilerStop();
			char cmd[2048]={0};
			snprintf(cmd,2047,"pprof --callgrind  %s %s_%d.prof >%s_%d.callgrind"
					,_programBin.c_str(),_programName.c_str(),_pid,_programName.c_str(),_pid);
			printf("cmd=%s\n",cmd);
			system(cmd);
			_ready=false;
		}
	#endif
	}
	void ProfilerFlush()
	{
		::ProfilerFlush();
	}
private:
	bool			_ready;
	int				_pid;
	std::string		_programBin;
	std::string 		_programName;
};

#endif

该小工具主要是提供如下功能:

 自动生成性能数据文件的名称:程序名_进程号.prof

 将性能数据文件自动转换成 .callgrind 格式,方便 kcachegrind 上展示

 提供宏定义,可通过是否传 -DCPU_PROFILER 宏来打开或关闭google cpu profiler 性能采集.

下面是使用示例:

 Main 程序

#include <stdio.h>
#include <stdlib.h>
#include <dlfcn.h>
#include <string.h>
#include <sys/types.h>
#include <unistd.h>
#include <gperftools/profiler.h>
#include "GoogleProfiler.h"

typedef int (*op_t) ();

int main(int argc,char** argv)
{
	int not_close=0;
	if(argc >= 2)
		not_close=atoi(argv[1]);
	void* dl_handle=NULL;
	op_t loopop;
	char* error=NULL;

	GoogleProfiler profiler;
	profiler.ProfilerStart();

	dl_handle = dlopen("./libTestProfiler.so", RTLD_LAZY );
	if (!dl_handle)
	{
		printf( "dlopen failed! %s\n", dlerror() );
		return -1;
	}

	loopop = (op_t)dlsym( dl_handle, "loopop");
	error = dlerror();
	if (error != NULL)
	{
		printf( "dlsym failed! %s\n", error );
		return -1;
	}

    printf("result:  %d\n", (loopop)() );
    if(not_close == 0)
    {
    	printf("do dlclose()\n");
    	dlclose( dl_handle );
    }
    else
    {
    	printf("not dlclose()\n");
    }

    profiler.ProfilerStop();
    return 0;
}

 Makefile

GPerfTools=/home/wuzhu/tools/gperftools

CCFLAGS=-fno-omit-frame-pointer -g -DCPU_PROFILER

ALL_BINS=libTestProfiler.so \

main_dlopen_link2

all:$(ALL_BINS)

main_dlopen_link2 :main_dlopen_link2.o

g++ $(CCFLAGS) -o $@ $^ -L./ -L$(GPerfTools)/lib -Wl,-Bdynamic -ltcmalloc_and_profiler -ldl

libTestProfiler.so:TestProfiler.o

g++ -shared $(CCFLAGS) -o $@ $^

.cpp.o:

g++ $(CCFLAGS) -c -I./ -I$(GPerfTools)/include -fPIC -o $@ $<

clean:

rm -f $(ALL_BINS) *.o *.prof

 执行

[wuzhu@search041142.sqa.cm4 cpu_profiler]$ ./main_dlopen_link2 1

ProgramBin=/home/wuzhu/develop/mytest/cpu_profiler/main_dlopen_link2

ProfilerStart(/home/wuzhu/develop/mytest/cpu_profiler/main_dlopen_link2)

result: 127

not dlclose()

PROFILE: interrupts/evictions/bytes = 5962/3555/171088

cmd=pprof –callgrind /home/wuzhu/develop/mytest/cpu_profiler/main_dlopen_link2 main_dlopen_link2_24515.prof >main_dlopen_link2_24515.callgrind

Using local file /home/wuzhu/develop/mytest/cpu_profiler/main_dlopen_link2.

Using local file main_dlopen_link2_24515.prof.

Removing killpg from all stack traces.

Removing main from all stack traces.

Removing __libc_start_main from all stack traces.

[wuzhu@search041142.sqa.cm4 cpu_profiler]$

生成 main_dlopen_link2_24515.prof 和 main_dlopen_link2_24515.callgrind

 用 kcachegrind 打开查看

(六) 参考

http://www.cnblogs.com/lenolix/archive/2010/12/13/1904868.html

http://gperftools.googlecode.com/svn/trunk/doc/cpuprofile.html


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Written by cwyalpha

十二月 28, 2012 在 3:55 下午

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