# Why is my code compiled with GFortran on Windows slower than on Ubuntu?

**URL:** <https://fortran-lang.discourse.group/t/why-is-my-code-compiled-with-gfortran-on-windows-slower-than-on-ubuntu/1666>\
**Category:** Uncategorized\
**Created:** [August 10, 2021, 10:16pm UTC](https://fortran-lang.discourse.group/t/why-is-my-code-compiled-with-gfortran-on-windows-slower-than-on-ubuntu/1666 "2021-08-10T22:16:26Z")\
**Posts on this page:** 1\
**Showing post:** 52

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**Author:** ![JohnCampbell](https://avatars.discourse-cdn.com/v4/letter/j/5daacb/32.png) [@JohnCampbell](https://fortran-lang.discourse.group/u/JohnCampbell)\
**Post date:** [May 3, 2022, 7:03am UTC](https://fortran-lang.discourse.group/t/why-is-my-code-compiled-with-gfortran-on-windows-slower-than-on-ubuntu/1666/52 "2022-05-03T07:03:18Z")

</div>

> [@CRquantum](#):
>
> By the way, how did you get the profile information below?

I have now tried using the gfortran profiling utility : gprof  
This is my first use of gprof, so my option selection may not be the best, but I get a useful table.  
It is much easier than my approach.

The batch file I used to test with the profiling report is:

```auto
del *.o
del *.mod
del em_mix.exe

set options=-g -fimplicit-none -fallow-argument-mismatch -march=native -pg

gfortran ran.f90 samplers.f90 em_mix.f90 %options% -o em_mix.exe

dir *.exe

em_mix

gprof -b -J -p em_mix.exe > em_mix_profile.log

notepad em_mix_profile.log

notepad em_mix.log

```

The resulting profile log I recovered is useful

```auto
Flat profile:

Each sample counts as 0.01 seconds.
  % cumulative self self total           
 time seconds seconds calls ms/call ms/call name    
 56.00 1.82 1.82 exp
 26.46 2.68 0.86 10198404 0.00 0.00 __samplers_MOD_pyq_i_detail
  3.38 2.79 0.11 __logl_internal
  3.08 2.89 0.10 10200 0.01 0.09 __samplers_MOD_mc_gauss_ptheta_w_sig
  2.15 2.96 0.07 __fentry__
  2.15 3.03 0.07 _mcount_private
  1.54 3.08 0.05 1000000 0.00 0.00 __samplers_MOD_pyq_more_o_log
  1.54 3.13 0.05 100 0.50 1.20 __samplers_MOD_metroplis_gik_k_more_o_log
  0.92 3.16 0.03 6040500 0.00 0.00 __random2_MOD_ran1
  0.62 3.18 0.02 __cosl_internal
  0.62 3.20 0.02 log
  0.31 3.21 0.01 4000008 0.00 0.00 __random_MOD_randn
  0.31 3.22 0.01 208 0.05 0.05 __random_MOD_gaussian
  0.31 3.23 0.01 __sinl_internal
  0.31 3.24 0.01 cos
  0.31 3.25 0.01 sin
  0.00 3.25 0.00 10660 0.00 0.00 delta_sec_
  0.00 3.25 0.00 102 0.00 0.10 __samplers_MOD_gauss_thetas
  0.00 3.25 0.00 90 0.00 0.00 __samplers_MOD_corrchk_internal
  0.00 3.25 0.00 51 0.00 19.02 __samplers_MOD_prep
  0.00 3.25 0.00 50 0.00 0.40 __samplers_MOD_metroplis_gik_all_o_log
  0.00 3.25 0.00 50 0.00 21.82 __samplers_MOD_steptest
  0.00 3.25 0.00 9 0.00 0.00 __samplers_MOD_get_musigma
  0.00 3.25 0.00 1 0.00 0.00 __random_MOD_savern
  0.00 3.25 0.00 1 0.00 0.00 __random_MOD_setrn
  0.00 3.25 0.00 1 0.00 0.00 __samplers_MOD_get_datetime
  0.00 3.25 0.00 1 0.00 0.00 __samplers_MOD_get_musigma_maxll
  0.00 3.25 0.00 1 0.00 0.00 __samplers_MOD_push_yji
  0.00 3.25 0.00 1 0.00 0.00 __samplers_MOD_samplers_init

```

This clearly identifies the exp intrinsic and function pyq\_i\_detail as the main time usage in “self seconds”  
The “cumulative seconds” is 3.25 seconds, which is less than 5.07 seconds I have obtained from SYSTEM\_CLOCK, but is hopefully explained in the documentation.  
It does not provide call counts for intrinsics exp, log, sin and cos

gprof is a very easy way to identify where time is being spent.  
It would be useful to perform this test on the range of gfortran implementations you have available.

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