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Linux application profiling

How can I record the performance of an application on a Linux machine? I won't have an IDE.

Ideally, I need an application that will attach to a process and log periodic snapshots of:


Ideally, I need an application that will attach to a process and log periodic snapshots of:

  • memory usage
  • number of threads
  • CPU usage

Well, in order to collect this type of information about your process, you don't actually need a profiler on Linux.

  1. You can use top in batch mode. It runs in the batch mode either until it is killed or until N iterations is done:

    top -b -p `pidof a.out`
    

    or

    top -b -p `pidof a.out` -n 100
    

    and you will get this:

    $ top -b -p `pidof a.out`
    
    top - 10:31:50 up 12 days, 19:08,  5 users,  load average: 0.02, 0.01, 0.02
    Tasks:   1 total,   0 running,   1 sleeping,   0 stopped,   0 zombie
    Cpu(s):  0.0%us,  0.0%sy,  0.0%ni,100.0%id,  0.0%wa,  0.0%hi,  0.0%si,  0.0%st
    Mem:  16330584k total,  2335024k used, 13995560k free,   241348k buffers
    Swap:  4194296k total,        0k used,  4194296k free,  1631880k cached
    
      PID USER      PR  NI  VIRT  RES  SHR S %CPU %MEM    TIME+  COMMAND
    24402 SK        20   0 98.7m 1056  860 S 43.9  0.0   0:11.87 a.out
    
    
    top - 10:31:53 up 12 days, 19:08,  5 users,  load average: 0.02, 0.01, 0.02
    Tasks:   1 total,   0 running,   1 sleeping,   0 stopped,   0 zombie
    Cpu(s):  0.9%us,  3.7%sy,  0.0%ni, 95.5%id,  0.0%wa,  0.0%hi,  0.0%si,  0.0%st
    Mem:  16330584k total,  2335148k used, 13995436k free,   241348k buffers
    Swap:  4194296k total,        0k used,  4194296k free,  1631880k cached
    
    PID USER      PR  NI  VIRT  RES  SHR S %CPU %MEM    TIME+  COMMAND
    24402 SK      20   0 98.7m 1072  860 S 19.0  0.0   0:12.44 a.out
    
  2. You can use ps (for instance in a shell script)

    ps --format pid,pcpu,cputime,etime,size,vsz,cmd -p `pidof a.out`
    

    I need some means of recording the performance of an application on a Linux machine

    In order to do this you need to use perf if your Linux kernel is greater than 2.6.32 or OProfile if it is older. Both programs don't require from you to instrument your program (like Gprof requires). However, in order to get the call graph correctly in perf you need to build you program with -fno-omit-frame-pointer. For example: g++ -fno-omit-frame-pointer -O2 main.cpp.

As for Linux perf:

  1. To record performance data:

    perf record -p `pidof a.out`
    

    or to record for 10 seconds:

    perf record -p `pidof a.out` sleep 10
    

    or to record with a call graph ()

    perf record -g -p `pidof a.out`
    
  2. To analyze the recorded data

    perf report --stdio
    perf report --stdio --sort=dso -g none
    perf report --stdio -g none
    perf report --stdio -g
    

    On RHEL 6.3 it is allowed to read /boot/System.map-2.6.32-279.el6.x86_64, so I usually add --kallsyms=/boot/System.map-2.6.32-279.el6.x86_64 when doing a performance report:

    perf report --stdio -g --kallsyms=/boot/System.map-2.6.32-279.el6.x86_64
    

    Here I wrote some more information on using Linux `perf`:

    First of all - this is tutorial about Linux profiling with perf

    You can use perf if your Linux Kernel is greater than 2.6.32 or OProfile if it is older. Both programs don't require from you to instrument your program (like Gprof requires). However, in order to get call graph correctly in perf you need to build you program with -fno-omit-frame-pointer. For example: g++ -fno-omit-frame-pointer -O2 main.cpp.

    You can see a "live" analysis of your application with perf top:

     sudo perf top -p `pidof a.out` -K
    

 

Or you can record performance data of a running application and analyze them after that:

  1. To record performance data:

    perf record -p `pidof a.out`
    

    or to record for 10 seconds:

    perf record -p `pidof a.out` sleep 10
    

    or to record with a call graph ()

    perf record -g -p `pidof a.out`
    
  2. To analyze the recorded data

perf report --stdio
perf report --stdio --sort=dso -g none
perf report --stdio -g none
perf report --stdio -g

Or you can record performance data of an application and analyze them after that just by launching the application in this way and waiting for it to exit:

perf record ./a.out

This is an example of profiling a test program.

The test program is in file main.cpp (main.cpp is at the bottom of the answer):

I compile it in this way:

g++ -m64 -fno-omit-frame-pointer -g main.cpp -L.  -ltcmalloc_minimal -o my_test

I use libmalloc_minimial.so since it is compiled with -fno-omit-frame-pointer while libc malloc seems to be compiled without this option. Then I run my test program:

./my_test 100000000

Then I record performance data of a running process:

perf record -g  -p `pidof my_test` -o ./my_test.perf.data sleep 30

Then I analyze the load per module:

perf report --stdio -g none --sort comm,dso -i ./my_test.perf.data

# Overhead  Command                 Shared Object
# ........  .......  ............................
#
    70.06%  my_test  my_test
    28.33%  my_test  libtcmalloc_minimal.so.0.1.0
     1.61%  my_test  [kernel.kallsyms]

Then load per function is analyzed:

perf report --stdio -g none -i ./my_test.perf.data | c++filt

# Overhead  Command                 Shared Object                       Symbol
# ........  .......  ............................  ...........................
#
    29.30%  my_test  my_test                       [.] f2(long)
    29.14%  my_test  my_test                       [.] f1(long)
    15.17%  my_test  libtcmalloc_minimal.so.0.1.0  [.] operator new(unsigned long)
    13.16%  my_test  libtcmalloc_minimal.so.0.1.0  [.] operator delete(void*)
     9.44%  my_test  my_test                       [.] process_request(long)
     1.01%  my_test  my_test                       [.] operator delete(void*)@plt
     0.97%  my_test  my_test                       [.] operator new(unsigned long)@plt
     0.20%  my_test  my_test                       [.] main
     0.19%  my_test  [kernel.kallsyms]             [k] apic_timer_interrupt
     0.16%  my_test  [kernel.kallsyms]             [k] _spin_lock
     0.13%  my_test  [kernel.kallsyms]             [k] native_write_msr_safe

     and so on ...

Then call chains are analyzed:

perf report --stdio -g graph -i ./my_test.perf.data | c++filt

# Overhead  Command                 Shared Object                       Symbol
# ........  .......  ............................  ...........................
#
    29.30%  my_test  my_test                       [.] f2(long)
            |
            --- f2(long)
               |
                --29.01%-- process_request(long)
                          main
                          __libc_start_main

    29.14%  my_test  my_test                       [.] f1(long)
            |
            --- f1(long)
               |
               |--15.05%-- process_request(long)
               |          main
               |          __libc_start_main
               |
                --13.79%-- f2(long)
                          process_request(long)
                          main
                          __libc_start_main

    15.17%  my_test  libtcmalloc_minimal.so.0.1.0  [.] operator new(unsigned long)
            |
            --- operator new(unsigned long)
               |
               |--11.44%-- f1(long)
               |          |
               |          |--5.75%-- process_request(long)
               |          |          main
               |          |          __libc_start_main
               |          |
               |           --5.69%-- f2(long)
               |                     process_request(long)
               |                     main
               |                     __libc_start_main
               |
                --3.01%-- process_request(long)
                          main
                          __libc_start_main

    13.16%  my_test  libtcmalloc_minimal.so.0.1.0  [.] operator delete(void*)
            |
            --- operator delete(void*)
               |
               |--9.13%-- f1(long)
               |          |
               |          |--4.63%-- f2(long)
               |          |          process_request(long)
               |          |          main
               |          |          __libc_start_main
               |          |
               |           --4.51%-- process_request(long)
               |                     main
               |                     __libc_start_main
               |
               |--3.05%-- process_request(long)
               |          main
               |          __libc_start_main
               |
                --0.80%-- f2(long)
                          process_request(long)
                          main
                          __libc_start_main

     9.44%  my_test  my_test                       [.] process_request(long)
            |
            --- process_request(long)
               |
                --9.39%-- main
                          __libc_start_main

     1.01%  my_test  my_test                       [.] operator delete(void*)@plt
            |
            --- operator delete(void*)@plt

     0.97%  my_test  my_test                       [.] operator new(unsigned long)@plt
            |
            --- operator new(unsigned long)@plt

     0.20%  my_test  my_test                       [.] main
     0.19%  my_test  [kernel.kallsyms]             [k] apic_timer_interrupt
     0.16%  my_test  [kernel.kallsyms]             [k] _spin_lock
     and so on ...

So at this point you know where your program spends time.

And this is the main.cpp file for the test:

#include <stdio.h>
#include <stdlib.h>
#include <time.h>

time_t f1(time_t time_value)
{
  for (int j = 0; j < 10; ++j) {
    ++time_value;
    if (j%5 == 0) {
      double *p = new double;
      delete p;
    }
  }
  return time_value;
}

time_t f2(time_t time_value)
{
  for (int j = 0; j < 40; ++j) {
    ++time_value;
  }
  time_value = f1(time_value);
  return time_value;
}

time_t process_request(time_t time_value)
{
  for (int j = 0; j < 10; ++j) {
    int *p = new int;
    delete p;
    for (int m = 0; m < 10; ++m) {
      ++time_value;
    }
  }
  for (int i = 0; i < 10; ++i) {
    time_value = f1(time_value);
    time_value = f2(time_value);
  }
  return time_value;
}

int main(int argc, char* argv2[])
{
  int number_loops = argc > 1 ? atoi(argv2[1]) : 1;
  time_t time_value = time(0);
  printf("number loops %d\n", number_loops);
  printf("time_value: %d\n", time_value);

  for (int i = 0; i < number_loops; ++i) {
    time_value = process_request(time_value);
  }
  printf("time_value: %ld\n", time_value);
  return 0;
}


Quoting Linus Torvalds himself:

Don't use gprof. You're much better off using the newish Linux 'perf' tool.

And later ...

I can pretty much guarantee that once you start using it, you'll never use gprof or oprofile again.

See Re: [PATCH] grep: do not do external grep on skip-worktree entries (2010-01-04)


If you are looking for things to do to possibly speed up the program, you need stackshots. A simple way to do this is to use the pstack utility, or lsstack if you can get it.

You can do better than Gprof. If you want to use an official profiling tool, you want something that samples the call stack on wall-clock time and presents line-level cost, such as OProfile or RotateRight Zoom.


You can use Valgrind. It records data in a file which you can analyse later using a proper GUI, like KCacheGrind.

A usage example would be:

valgrind --tool=callgrind --dump-instr=yes --simulate-cache=yes your_program

It'll generate a file called callgrind.out.xxx where xxx is the PID of the program.

Unlike Gprof, Valgrind works with many different languages, including Java, with some limitations.


Look into Gprof. You need to compile the code with the -pg option, which instruments the code. After that, you can run the program and use Gprof to see the results.


You can also try out cpuprofiler.com. It gets the information you would normally get from the top command, and the CPU usage data can be even viewed remotely from a web browser.

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