An object-oriented library for computing elapsed CPU time. More specifically, you can determine the CPU time consumed by a particular piece of your code, that is:
!... start timer
do i = 1, n
!...some big calculation...
end do
!...stop timer
print *, 'Elapsed CPU time = ', blahThis project is still a work in progress and anyone is free to contribute under the proviso that they abstain from using the dreaded go to.
For bug reports or feature requests please open an issue on github.
- The GNU Make tool https://www.gnu.org/software/make/
- The GNU gfortran compiler https://gcc.gnu.org/wiki/GFortran
Type the following command line arguments
git clone https://github.com/jlokimlin/cpu_timer.git
cd cpu_timer; make all
use, intrinsic :: ISO_C_binding, only: &
wp => C_DOUBLE, &
ip => C_INT
use cpu_timer_library, only: &
CpuTimer
! Explicit typing only
implicit none
type (CpuTimer) :: timer
real (wp) :: wall_clock_time
real (wp) :: total_processor_time
integer (ip) :: units = 0 ! (optional argument) = 0 for seconds, or 1 for minutes, or 2 for hours
! Starting the timer
call timer%start()
! Stopping the timer
call timer%stop()
! Reading the time
wall_clock_time = timer%get_elapsed_time(units)
total_processor_time = timer%get_total_cpu_time(units)
! Write time stamp to standard output
call timer%print_time_stamp()
! Write compiler info to standard output
call timer%print_compiler_info()
Demonstrate the usage of type (CpuTimer).
August 20 2016 6:07:14.320 PM
*********************************************
time_random_number_routine
times the intrinsic random_number routine:
call random_number( x(1:n) )
Data vectors will be of minimum size 1
Data vectors will be of maximum size 1048576
Number of repetitions of the operation: 5
Timing results:
Size Rep #1 Rep #2 Rep #3 Rep #4 Rep #5 MINVAL SUM MAXVAL
1 1.76191E-02 1.60609E-02 1.44475E-02 1.45581E-02 1.44137E-02 1.44137E-02 1.54199E-02 1.76191E-02
2 1.44452E-02 1.44981E-02 2.08713E-02 2.02675E-02 1.47711E-02 1.44452E-02 1.69706E-02 2.08713E-02
4 1.47064E-02 1.46856E-02 1.61794E-02 1.57239E-02 1.47874E-02 1.46856E-02 1.52165E-02 1.61794E-02
8 1.47851E-02 1.47053E-02 1.50504E-02 1.52278E-02 1.53919E-02 1.47053E-02 1.50321E-02 1.53919E-02
16 1.77258E-02 1.70115E-02 1.48718E-02 1.52452E-02 1.51357E-02 1.48718E-02 1.59980E-02 1.77258E-02
32 1.47998E-02 1.48585E-02 1.46464E-02 1.46236E-02 1.43961E-02 1.43961E-02 1.46649E-02 1.48585E-02
64 1.44532E-02 1.43647E-02 1.43166E-02 1.45666E-02 1.51698E-02 1.43166E-02 1.45742E-02 1.51698E-02
128 1.46443E-02 1.92390E-02 1.45861E-02 1.46580E-02 1.44909E-02 1.44909E-02 1.55236E-02 1.92390E-02
256 1.49488E-02 1.54366E-02 1.46981E-02 1.46584E-02 1.54032E-02 1.46584E-02 1.50290E-02 1.54366E-02
512 1.50235E-02 1.53618E-02 1.51830E-02 1.50881E-02 1.44276E-02 1.44276E-02 1.50168E-02 1.53618E-02
1024 1.43234E-02 1.43455E-02 1.44745E-02 1.43079E-02 1.43134E-02 1.43079E-02 1.43529E-02 1.44745E-02
2048 1.43336E-02 1.46181E-02 1.43538E-02 1.43268E-02 1.42984E-02 1.42984E-02 1.43861E-02 1.46181E-02
4096 1.42910E-02 1.49904E-02 1.54230E-02 1.50998E-02 1.63991E-02 1.42910E-02 1.52407E-02 1.63991E-02
8192 2.22919E-02 1.61469E-02 1.52908E-02 1.52855E-02 1.48256E-02 1.48256E-02 1.67681E-02 2.22919E-02
16384 1.44122E-02 1.47464E-02 1.44421E-02 1.42999E-02 1.42838E-02 1.42838E-02 1.44369E-02 1.47464E-02
32768 1.42835E-02 1.42998E-02 1.42837E-02 1.43208E-02 1.43324E-02 1.42835E-02 1.43040E-02 1.43324E-02
65536 1.42861E-02 1.42856E-02 1.43340E-02 1.42667E-02 1.42299E-02 1.42299E-02 1.42804E-02 1.43340E-02
131072 1.42717E-02 1.42638E-02 1.42335E-02 1.42323E-02 1.42705E-02 1.42323E-02 1.42543E-02 1.42717E-02
262144 1.42374E-02 1.45162E-02 1.43331E-02 1.42933E-02 1.42409E-02 1.42374E-02 1.43242E-02 1.45162E-02
524288 1.45305E-02 1.46229E-02 1.46774E-02 1.43601E-02 1.42351E-02 1.42351E-02 1.44852E-02 1.46774E-02
1048576 1.42561E-02 1.42542E-02 1.42915E-02 1.42444E-02 1.42370E-02 1.42370E-02 1.42567E-02 1.42915E-02
*********************************************
time_vectorized_exp_routine:
y(1:n) = x(1:n)
y(1:n) = PI * x(1:n)
y(1:n) = sqrt( x(1:n) )
y(1:n) = exp( x(1:n) )
Data vectors will be of minimum size 4096
Data vectors will be of maximum size 4194304
Number of repetitions of the operation: 5
Timing results:
Size Rep #1 Rep #2 Rep #3 Rep #4 Rep #5
4096 3.66000E-07 2.82000E-07 2.99000E-07 2.78000E-07 2.79000E-07
8192 2.84000E-07 2.71000E-07 2.75000E-07 2.72000E-07 2.91000E-07
16384 3.82000E-07 3.00000E-07 2.77000E-07 2.90000E-07 6.16000E-07
32768 3.47000E-07 3.01000E-07 4.47000E-07 2.86000E-07 4.66000E-07
65536 3.08000E-07 2.92000E-07 3.54000E-07 2.88000E-07 2.75000E-07
131072 2.76000E-07 2.75000E-07 2.87000E-07 2.79000E-07 4.21000E-07
262144 3.45000E-07 2.87000E-07 2.94000E-07 2.81000E-07 2.74000E-07
524288 3.54000E-07 2.96000E-07 2.95000E-07 3.76000E-07 2.74000E-07
1048576 3.05000E-07 2.79000E-07 2.95000E-07 2.76000E-07 2.95000E-07
2097152 2.73000E-07 3.26000E-07 2.74000E-07 2.78000E-07 2.87000E-07
4194304 2.95000E-07 3.14000E-07 2.71000E-07 2.82000E-07 3.96000E-07
Timing results:
Size Rep #1 Rep #2 Rep #3 Rep #4 Rep #5
4096 3.79000E-07 2.74000E-07 2.82000E-07 3.07000E-07 2.75000E-07
8192 2.78000E-07 2.74000E-07 2.89000E-07 3.10000E-07 2.98000E-07
16384 3.89000E-07 2.84000E-07 2.87000E-07 2.95000E-07 2.83000E-07
32768 7.84000E-07 3.03000E-07 2.98000E-07 2.76000E-07 2.88000E-07
65536 4.64000E-07 3.09000E-07 2.87000E-07 3.03000E-07 2.74000E-07
131072 3.52000E-07 3.03000E-07 3.03000E-07 2.87000E-07 3.09000E-07
262144 2.96000E-07 2.96000E-07 3.06000E-07 2.82000E-07 2.95000E-07
524288 2.77000E-07 2.91000E-07 2.82000E-07 3.00000E-07 3.03000E-07
1048576 3.07000E-07 2.95000E-07 3.01000E-07 3.08000E-07 2.86000E-07
2097152 2.89000E-07 3.17000E-07 2.83000E-07 2.82000E-07 3.03000E-07
4194304 2.79000E-07 3.08000E-07 3.44000E-07 2.92000E-07 2.87000E-07
Timing results:
Size Rep #1 Rep #2 Rep #3 Rep #4 Rep #5
4096 3.64000E-07 3.06000E-07 2.76000E-07 2.76000E-07 3.17000E-07
8192 3.13000E-07 3.36000E-07 3.48000E-07 3.11000E-07 2.96000E-07
16384 2.81000E-07 5.65000E-07 3.01000E-07 2.82000E-07 3.01000E-07
32768 2.76000E-07 2.81000E-07 2.74000E-07 3.07000E-07 2.76000E-07
65536 2.75000E-07 3.39000E-07 2.85000E-07 3.01000E-07 3.12000E-07
131072 2.71000E-07 2.87000E-07 2.79000E-07 2.80000E-07 2.96000E-07
262144 2.86000E-07 3.60000E-07 2.95000E-07 2.99000E-07 2.75000E-07
524288 2.76000E-07 4.03000E-07 2.89000E-07 2.74000E-07 2.88000E-07
1048576 2.86000E-07 2.76000E-07 2.80000E-07 2.79000E-07 2.74000E-07
2097152 2.85000E-07 2.79000E-07 3.02000E-07 2.99000E-07 2.78000E-07
4194304 3.00000E-07 3.02000E-07 2.77000E-07 2.73000E-07 2.95000E-07
Timing results:
Size Rep #1 Rep #2 Rep #3 Rep #4 Rep #5
4096 3.52000E-07 2.74000E-07 2.71000E-07 2.81000E-07 3.20000E-07
8192 2.98000E-07 3.73000E-07 2.75000E-07 3.08000E-07 2.90000E-07
16384 2.80000E-07 2.96000E-07 3.00000E-07 2.76000E-07 1.81300E-06
32768 2.75000E-07 2.74000E-07 3.02000E-07 2.78000E-07 2.77000E-07
65536 2.99000E-07 2.74000E-07 2.96000E-07 3.02000E-07 2.80000E-07
131072 2.73000E-07 2.97000E-07 2.90000E-07 2.98000E-07 3.02000E-07
262144 2.91000E-07 2.77000E-07 2.85000E-07 2.83000E-07 2.78000E-07
524288 2.74000E-07 2.74000E-07 3.17000E-07 2.97000E-07 2.97000E-07
1048576 2.97000E-07 3.08000E-07 2.95000E-07 2.82000E-07 3.01000E-07
2097152 2.73000E-07 2.74000E-07 3.79000E-07 2.74000E-07 2.86000E-07
4194304 2.84000E-07 2.78000E-07 3.08000E-07 2.98000E-07 2.80000E-07
*********************************************
time_unvectorized_exp_routine
do i = 1, n
y(i) = x(i)
y(i) = PI * x(i)
y(i) = sqrt(x(i))
y(i) = exp(x(i))
end do
Data vectors will be of minimum size 4096
Data vectors will be of maximum size 4194304
Number of repetitions of the operation: 5
*********************************************
Timing results:
Size Rep #1 Rep #2 Rep #3 Rep #4 Rep #5
4096 2.83500E-06 1.71700E-06 1.71900E-06 1.71900E-06 1.72000E-06
8192 3.13300E-06 3.13200E-06 3.13100E-06 3.12800E-06 3.14000E-06
16384 5.96200E-06 5.96100E-06 5.98700E-06 5.96000E-06 5.96000E-06
32768 1.16440E-05 1.16390E-05 1.16320E-05 1.16300E-05 1.16380E-05
65536 2.29470E-05 2.29490E-05 2.29530E-05 2.29490E-05 2.29680E-05
131072 4.56090E-05 4.56150E-05 4.56620E-05 4.56670E-05 4.56700E-05
262144 1.02447E-04 9.09140E-05 9.09750E-05 9.09640E-05 9.58640E-05
524288 1.81567E-04 1.81517E-04 1.81586E-04 1.81575E-04 1.81579E-04
1048576 3.62764E-04 3.62957E-04 3.62778E-04 3.86554E-04 3.67647E-04
2097152 7.29749E-04 7.30339E-04 7.25192E-04 7.30078E-04 7.25190E-04
4194304 1.44997E-03 1.45979E-03 1.63218E-03 1.45459E-03 1.44997E-03
*********************************************
Timing results:
Size Rep #1 Rep #2 Rep #3 Rep #4 Rep #5
4096 1.84400E-06 1.72400E-06 1.72400E-06 1.72300E-06 1.72200E-06
8192 3.13900E-06 3.13700E-06 3.14500E-06 3.14400E-06 3.14000E-06
16384 5.96600E-06 5.95800E-06 5.96800E-06 5.97300E-06 5.97800E-06
32768 1.16310E-05 1.16320E-05 1.16360E-05 1.16330E-05 1.16470E-05
65536 2.29570E-05 2.29670E-05 2.29410E-05 2.29650E-05 2.29720E-05
131072 4.56760E-05 4.56730E-05 4.96240E-05 4.56790E-05 4.56210E-05
262144 9.09620E-05 1.81755E-04 9.24530E-05 1.81777E-04 9.09840E-05
524288 1.81562E-04 3.07141E-04 1.81572E-04 1.81583E-04 1.81706E-04
1048576 3.62825E-04 3.62710E-04 3.62762E-04 3.62770E-04 3.62780E-04
2097152 7.25155E-04 7.27263E-04 7.25156E-04 7.28876E-04 7.30041E-04
4194304 1.47559E-03 1.47717E-03 1.44997E-03 1.47738E-03 1.45533E-03
*********************************************
Timing results:
Size Rep #1 Rep #2 Rep #3 Rep #4 Rep #5
4096 1.79200E-06 2.48900E-06 1.72600E-06 1.72500E-06 1.72700E-06
8192 3.14200E-06 4.63400E-06 3.13100E-06 3.13100E-06 3.13200E-06
16384 5.99100E-06 6.62700E-06 5.97400E-06 5.96200E-06 5.96000E-06
32768 1.16510E-05 1.27240E-05 1.24910E-05 1.16270E-05 1.16260E-05
65536 2.66640E-05 2.29740E-05 3.28560E-05 2.29480E-05 2.29590E-05
131072 4.56070E-05 4.56700E-05 5.29360E-05 4.56720E-05 4.56780E-05
262144 9.09920E-05 9.09900E-05 1.01756E-04 9.09160E-05 9.09680E-05
524288 1.81559E-04 1.81654E-04 1.81563E-04 2.02443E-04 1.85238E-04
1048576 3.62845E-04 3.73505E-04 3.62770E-04 3.62795E-04 3.67811E-04
2097152 7.31032E-04 7.66585E-04 7.33419E-04 7.30162E-04 7.27893E-04
4194304 1.60041E-03 1.52588E-03 1.45480E-03 1.45483E-03 1.47871E-03
*********************************************
Timing results:
Size Rep #1 Rep #2 Rep #3 Rep #4 Rep #5
4096 1.89400E-06 1.71700E-06 1.72400E-06 1.71500E-06 1.71900E-06
8192 3.14600E-06 3.13300E-06 3.69200E-06 3.13800E-06 3.13200E-06
16384 5.97000E-06 5.96400E-06 1.17780E-05 5.96000E-06 5.96200E-06
32768 1.16490E-05 1.16310E-05 1.16450E-05 1.16180E-05 1.16320E-05
65536 2.29860E-05 2.29550E-05 2.29580E-05 2.29430E-05 2.29530E-05
131072 4.56220E-05 4.56830E-05 4.56090E-05 4.56820E-05 4.56790E-05
262144 9.09790E-05 9.09070E-05 9.09830E-05 9.97750E-05 9.09740E-05
524288 1.81771E-04 1.81572E-04 2.12770E-04 1.81569E-04 1.81575E-04
1048576 3.62770E-04 3.68055E-04 3.74247E-04 3.62812E-04 3.62769E-04
2097152 7.25126E-04 7.30202E-04 7.25179E-04 7.25123E-04 7.69377E-04
4194304 2.32765E-03 1.45476E-03 1.44998E-03 1.46017E-03 1.44994E-03
*********************************************
time_2d_nearest_neighbor_problem
Given x(2,n) and y(2),
find x(2,*) closest to y(2).
do i = 1, n
if distance( x(2,i), y ) < minimum so far
x_min = x(2,i)
end do
Data vectors will be of minimum size 1024
Data vectors will be of maximum size 1048576
Number of repetitions of the operation: 5
Timing results:
Size Rep #1 Rep #2 Rep #3 Rep #4 Rep #5
1024 8.29000E-07 6.45000E-07 6.47000E-07 6.44000E-07 6.43000E-07
2048 1.00000E-06 9.95000E-07 9.96000E-07 9.95000E-07 9.98000E-07
4096 1.70500E-06 1.70700E-06 1.70300E-06 1.70300E-06 1.70300E-06
8192 3.13500E-06 3.11500E-06 3.12000E-06 3.12200E-06 3.12000E-06
16384 5.94700E-06 5.95200E-06 5.94700E-06 5.94900E-06 5.95600E-06
32768 1.16150E-05 1.16140E-05 1.16160E-05 1.16130E-05 1.16150E-05
65536 2.78350E-05 2.29340E-05 2.29360E-05 2.29370E-05 2.29400E-05
131072 4.55930E-05 4.55870E-05 4.55880E-05 4.55870E-05 4.55910E-05
262144 9.08930E-05 9.08860E-05 1.11564E-04 9.08870E-05 9.08860E-05
524288 1.81487E-04 1.81489E-04 2.05870E-04 1.81486E-04 1.81489E-04
1048576 3.62689E-04 3.99389E-04 3.62688E-04 3.62684E-04 3.62705E-04
*********************************************
time_matrix_multiplication_problem
Compute C = A * B
where
A is an L by M matrix,
B is an M by N matrix,
and so
C is an L by N matrix.
Minimum value of L = M = N = 4
Maximum value of L = M = N = 1024
Number of repetitions of the operation: 5
Use nested do loops for matrix multiplication.
Timing results using nested do loops:
Size Rep #1 Rep #2 Rep #3 Rep #4 Rep #5
4 7.11000E-07 5.88000E-07 5.74000E-07 7.17000E-07 5.32000E-07
16 7.92100E-06 8.18500E-06 8.17900E-06 8.81500E-06 7.96500E-06
64 4.68022E-04 4.73999E-04 4.84999E-04 5.03218E-04 4.63941E-04
256 4.90532E-02 4.63972E-02 5.22809E-02 6.63487E-02 4.62401E-02
1024 9.25886E+00 9.29856E+00 8.90819E+00 9.25549E+00 8.51152E+00
Use the matmul routine for matrix multiplication.
Timing results using matmul:
Size Rep #1 Rep #2 Rep #3 Rep #4 Rep #5
4 1.28800E-06 8.50000E-07 9.12000E-07 1.02600E-06 8.21000E-07
16 2.24210E-05 1.16830E-05 1.28290E-05 1.16680E-05 1.15010E-05
64 6.94844E-04 6.79922E-04 7.13658E-04 6.86136E-04 6.85324E-04
256 4.19184E-02 4.03149E-02 4.28599E-02 4.05020E-02 4.05167E-02
1024 2.65927E+00 2.71235E+00 2.71320E+00 2.85616E+00 2.67387E+00
type (CpuTimer) tests.
Normal end of execution.
August 20 2016 6:08:30.878 PM
This file was compiled by GCC version 6.1.1 20160802 using the options -I ../objs -I ../lib -mtune=generic -march=x86-64 -auxbase-strip ../objs/type_CpuTimer.o -Wall -fimplicit-none -fcheck=all -J ../lib