Profiling Python Code
Profiling Python code – as a whole or by function – shows where you should spend time speeding up your programs.
To improve the performance of your applications, you need to conduct some kind of dynamic (program, software, code) analysis, also called profiling, to measure metrics of interest. One key metric for developers is time – where is the code spending most of its time? – because you can the focus on areas, or hotspots, that can be made to run faster.
It might seem obvious, but it is worth noting that if you don’t profile for code optimization, you could flounder all around the code improving sections you think might be bottlenecks. I have watched people spend hours and hours working a particular part of their code when a simple profile showed that portion of the code contributed very little to the overall run time. I admit that I have done this before, focusing on loops that I thought would take the most time, without profiling; however, once I profiled, I found that I had wasted my time and I should focus elsewhere.
Different kinds of profiling (e.g., event-based, statistical, instrumented, simulation), are used in different situations. In this article, I focus on two types: deterministic and statistical. Deterministic profiling captures every computation of the code and produces very accurate profiles, but it can greatly slow down code performance. Although you achieve very good accuracy with the profile, run times are greatly increased, and you have to wonder whether the profiling didn’t adversely affect how the code ran; for example, did the profiling cause the computation bottlenecks to move to a different place in the code?
On the other hand, statistical profiling takes periodic “samples” of the code computations and uses them as representations of the profile of the code. This method usually has very little effect on code performance, so you can get a profile that is very close to the real execution of the code. One has to wonder about the correct time interval to get an accurate profile of the application while not affecting the run time. Usually this means setting the time intervals to smaller and smaller values to capture the profile accurately. If the interval becomes too small, then it almost becomes deterministic profiling, and run time is greatly increased.
On the other hand, if your code takes a long time to execute (e.g., hours or days), deterministic profiling might be impossible because the increase in run time is unacceptable. In this case, statistical profiling is appropriate because of the longer periods of time available to sample performance.
In this article, I focus on profiling Python code, primarily because of a current lack of Python profiling but also because I think profiling Python code, creating functions, and using Numba to then compile these functions for CPUs or GPUs is a good process that improves performance.
To help illustrate some tools you can use to profile Python code, I will use an example of an idealized molecular dynamics (MD) application. I’ll work through some profiling tools and modify the code in a reasonable manner for better profiling. The first, and probably most used and flexible, method I want to mention is “manual” profiling.
Manual Profiling
The manual profiling approach is fairly simple but involves inserting timing points into your code. Timing points surround a section of code and collect the total elapsed time(s) for the section, as well as how many times the section is executed. From this information you can calculate an average elapsed time. The timing points can be spread throughput the code, so you get an idea of how much time each section of the code takes. The elapsed times are printed at the end of execution, to give you an idea of where you should focus your efforts to improve performance.
A key advantage of this approach is its generally low overhead. Additionally, you can control what portions of the code are timed (you don’t have to profile the entire code). A downside is that you have to instrument your code by inserting timing points throughout. However, inserting these points is not difficult.
One easy way to accomplish this uses the Python time module. Simple code from an article on the Better Programming website (example 16) is shown in Listing 1. The code simply calls the current time before and after a section of code of interest. The difference is elapsed time, or the amount of time needed to execute that section of code.
Listing 1: Time to Execute
import time
start_time = time.time()
# Code to check follows
a, b = 1,2
c = a + b
# Code to check ends
end_time = time.time()
time_taken = (end_time- start_time)
print(" Time taken in seconds: {0} s").format(time_taken_in_micro)If a section of code is called repeatedly, just sum the elapsed times for the section and sum the number of times that section is used; then, you can compute the average time through the code section. If the number of calls is large enough, you can do some quick descriptive statistics and compute the mean, median, variance, min, max, and deviations.
cProfile
cProfile is a deterministic profiler for Python and is recommended “… for most users.” In general terms, it creates a set of statistics that lists the total time spent in certain parts of the code, as well as how often the portion of the code was called.
cProfile, as the name hints, is written in C as a Python extension and comes in the standard Python 3, which keeps the overhead low, so the profiler doesn’t affect the amount of time much.
cProfile outputs a few stats about the test code:
- ncalls – number of calls to the portion of code
- tottime– total time spent in the given function (excludes time made in calls to subfunctions)
- percall – tottime divided by ncalls
- cumtime – cumulative time spent in the specific function, including all subfunctions
- percall – cumtime divided by ncalls
cProfile also outputs the file name of the code, in case multiple file are involved, as well as the line number of the function (lineno).
Running cProfile is fairly simple:
$ python -m cProfile -s cumtime script.py
The first part of the command tells Python to use the cProfile module. The output from cProfile is sorted (-s) by cumtime (cumulative time). The last option on the command line is the Python code of interest. cProfile also has an option (-o) to send the stats to an output file instead of stdout. Listing 2 is a sample of the first few lines from cProfile on a variation of the MD code.
Listing 2: cProfile Output
Thu Nov 7 08:09:57 2019
12791143 function calls (12788375 primitive calls) in 156.745 seconds
Ordered by: cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
148/1 0.001 0.000 156.745 156.745 {built-in method builtins.exec}
1 149.964 149.964 156.745 156.745 md_002.py:3()
12724903 3.878 0.000 3.878 0.000 {built-in method builtins.min}
1 2.649 2.649 2.727 2.727 md_002.py:10(init)
50 0.168 0.003 0.168 0.003 md_002.py:127(update)
175/2 0.001 0.000 0.084 0.042 :978(_find_and_load)
...pprofile
To get a line-by-line profile of your code, you can use the pprofile tool, for a granular, thread-aware analysis for deterministic or statistical profiling (pure Python). The form of pprofile is:
$ pprofile some_python_executable arg1 ...
After the tool finishes, it prints annotated code of each file involved in the execution.
By default, pprofile profiling is deterministic, which although it slows down the code, produces a very complete profile. You can also use pprofile in a statistical manner, which uses much less time:
$ pprofile --statistic .01 code.py
With the statistic option, you also need to specify the period of time between sampling. In this example, a period of 0.01 seconds was used.
Be careful when using the statistic option because, if the sample time is too long, you can miss a number of computations, and the output will incorrectly record zero percent activity. Conversely, to get a better estimation of the time spent in certain portions of the code, you have to reduce the time between samples to the point of almost deterministic profiling.
The deterministic pprofile sample output in Listing 3 uses the same code as the previous cProfile example. I cut out sections of the output because it is very extensive. One thing I want to point out is the increase in execution time by about a factor of 10 (i.e., it ran 10 times slower than without profiling).
Listing 3: pprofile Output
Command line: md_002.py Total duration: 1662.48s File: md_002.py File duration: 1661.74s (99.96%) Line #| Hits| Time| Time per hit| %|Source code ------+----------+-------------+-------------+-------+----------- 1| 0| 0| 0| 0.00%|# md test code 2| 0| 0| 0| 0.00%| 3| 2| 3.50475e-05| 1.75238e-05| 0.00%|import platform 4| 1| 2.19345e-05| 2.19345e-05| 0.00%|from time import clock (call)| 1| 2.67029e-05| 2.67029e-05| 0.00%|# :1009 _handle_fromlist 5| 1| 2.55108e-05| 2.55108e-05| 0.00%|import numpy as np (call)| 1| 0.745732| 0.745732| 0.04%|# :978 _find_and_load 6| 1| 2.57492e-05| 2.57492e-05| 0.00%|from sys import exit (call)| 1| 1.7643e-05| 1.7643e-05| 0.00%|# :1009 _handle_fromlist 7| 1| 7.86781e-06| 7.86781e-06| 0.00%|import time ... 234| 0| 0| 0| 0.00%| # Compute the potential energy and forces 235| 12525000| 51.0831| 4.07849e-06| 3.07%| for j in range(0, p_num): 236| 12500000| 51.6473| 4.13179e-06| 3.11%| if (i != j): 237| 0| 0| 0| 0.00%| # Compute RIJ, the displacement vector 238| 49900000| 210.704| 4.22253e-06| 12.67%| for k in range(0, d_num): 239| 37425000| 177.055| 4.73093e-06| 10.65%| rij[k] = pos[k,i] - pos[k,j] 240| 0| 0| 0| 0.00%| # end for 241| 0| 0| 0| 0.00%| 242| 0| 0| 0| 0.00%| # Compute D and D2, a distance and a truncated distance 243| 12475000| 50.5158| 4.04936e-06| 3.04%| d = 0.0 244| 49900000| 209.465| 4.1977e-06| 12.60%| for k in range(0, d_num): 245| 37425000| 175.823| 4.69801e-06| 10.58%| d = d + rij[k] ** 2 246| 0| 0| 0| 0.00%| # end for 247| 12475000| 78.9422| 6.32803e-06| 4.75%| d = np.sqrt(d) 248| 12475000| 64.7463| 5.19008e-06| 3.89%| d2 = min(d, np.pi / 2.0) 249| 0| 0| 0| 0.00%| 250| 0| 0| 0| 0.00%| # Attribute half of the total potential energy to particle J 251| 12475000| 84.7846| 6.79636e-06| 5.10%| potential = potential + 0.5 * np.sin(d2) * np.sin(d2) 252| 0| 0| 0| 0.00%| 253| 0| 0| 0| 0.00%| # Add particle J's contribution to the force on particle I. 254| 49900000| 227.88| 4.56674e-06| 13.71%| for k in range(0, d_num): 255| 37425000| 244.374| 6.52971e-06| 14.70%| force[k,i] = force[k,i] - rij[k] * np.sin(2.0 * d2) / d 256| 0| 0| 0| 0.00%| # end for 257| 0| 0| 0| 0.00%| # end if 258| 0| 0| 0| 0.00%| 259| 0| 0| 0| 0.00%| # end for ...
Line-by-Line Function Profiling
The useful pprofile analyzes your entire code line by line. It can also do deterministic and statistical profiling. If you want to focus on a specific function within your code, line_profiler and kernprof can help. The line_profiler module performs line-by-line profiling of functions, and the kernprof script allows you to run either line_profiler or standard Python profilers such as cProfile.
To have kernprof run line_profiler, enter,
$ kernprof -l script_to_profile.py
which produces a binary file, script_to_profile.py.lprof. To “decode” the data, you can enter the command
$ python3 -m line_profiler script_to_profile.py.lprof > results.txt
and look at the results.txt file.
To get line_profiler to profile only certain functions, put an @profile decorator before the function declaration. The output is the elapsed time for the routine. The percentage of time, which is something I tend to check first, is relative to the total time for the function (be sure to remember that). The example in Listing 4 is output for some example code discussed in the next section.
Listing 4: Profiling a Function
Total time: 0.365088 s File: ./md_002.py Function: update at line 126 Line # Hits Time Per Hit % Time Line Contents ============================================================== 126 @profile 127 def update(d_num, p_num, rmass, dt, pos, vel, acc, force): 128 129 # Update 130 131 # Update positions 132 200 196.0 1.0 0.1 for i in range(0, d_num): 133 75150 29671.0 0.4 8.1 for j in range(0, p_num): 134 75000 117663.0 1.6 32.2 pos[i,j] = pos[i,j] + vel[i,j]*dt + 0.5 * acc[i,j]*dt*dt 135 # end for 136 # end for 137 138 # Update velocities 139 200 99.0 0.5 0.0 for i in range(0, d_num): 140 75150 29909.0 0.4 8.2 for j in range(0, p_num): 141 75000 100783.0 1.3 27.6 vel[i,j] = vel[i,j] + 0.5*dt*( force[i,j] * rmass + acc[i,j] ) 142 # end for 143 # end for 144 145 # Update accelerations. 146 200 95.0 0.5 0.0 for i in range(0, d_num): 147 75150 29236.0 0.4 8.0 for j in range(0, p_num): 148 75000 57404.0 0.8 15.7 acc[i,j] = force[i,j]*rmass 149 # end for 150 # end for 151 152 50 32.0 0.6 0.0 return pos, vel, acc
Example Code
To better illustrate the process of using a profiler, I chose some MD Python code with a fair amount of arithmetic intensity that could easily be put into functions. Because I’m not a computational chemist, let me quote from the website: “The computation involves following the paths of particles which exert a distance-dependent force on each other. The particles are not constrained by any walls; if particles meet, they simply pass through each other. The problem is treated as a coupled set of differential equations. The system of differential equation is discretized by choosing a discrete time step. Given the position and velocity of each particle at one time step, the algorithm estimates these values at the next time step. To compute the next position of each particle requires the evaluation of the right hand side of its corresponding differential equation.”
Serial Code and Profiling
When you download the Python version of the code, it already has several functions. To better illustrate profiling the code, I converted it to simple serial code and called it md_001.py (Listing 5). Then, I profiled the code with cProfile:
$ python3 -m cProfile -s cumtime md_001.py
Listing 5: md001.py
## MD is the main program for the molecular dynamics simulation.
#
# Discussion:
# MD implements a simple molecular dynamics simulation.
#
# The velocity Verlet time integration scheme is used.
# The particles interact with a central pair potential.
#
# Licensing:
# This code is distributed under the GNU LGPL license.
# Modified:
# 26 December 2014
#
# Author:
# John Burkardt
#
# Parameters:
# Input, integer D_NUM, the spatial dimension.
# A value of 2 or 3 is usual.
# The default value is 3.
#
# Input, integer P_NUM, the number of particles.
# A value of 1000 or 2000 is small but "reasonable".
# The default value is 500.
#
# Input, integer STEP_NUM, the number of time steps.
# A value of 500 is a small but reasonable value.
# The default value is 500.
#
# Input, real DT, the time step.
# A value of 0.1 is large; the system will begin to move quickly but the
# results will be less accurate.
# A value of 0.0001 is small, but the results will be more accurate.
# The default value is 0.1.
#
import platform
from time import clock
import numpy as np
from sys import exit
import time
def timestamp ( ):
t = time.time ( )
print ( time.ctime ( t ) )
return None
# end def
# ===================
# Main section of code
# ====================
timestamp()
print('')
print('MD_TEST')
print(' Python version: %s' % (platform.python_version( ) ))
print(' Test the MD molecular dynamics program.')
# Initialize variables
d_num = 3
p_num = 500
step_num = 50
dt = 0.1
mass = 1.0
rmass = 1.0 / mass
wtime1 = clock( )
# output:
print('' )
print('MD' )
print(' Python version: %s' % (platform.python_version( ) ) )
print(' A molecular dynamics program.' )
print('' )
print(' D_NUM, the spatial dimension, is %d' % (d_num) )
print(' P_NUM, the number of particles in the simulation is %d.' % (p_num) )
print(' STEP_NUM, the number of time steps, is %d.' % (step_num) )
print(' DT, the time step size, is %g seconds.' % (dt) )
print('' )
print(' At each step, we report the potential and kinetic energies.' )
print(' The sum of these energies should be a constant.' )
print(' As an accuracy check, we also print the relative error' )
print(' in the total energy.' )
print('' )
print(' Step Potential Kinetic (P+K-E0)/E0' )
print(' Energy P Energy K Relative Energy Error')
print('')
step_print_index = 0
step_print_num = 10
step_print = 0
for step in range(0, step_num+1):
if (step == 0):
# Initialize
# Positions
seed = 123456789
a = 0.0
b = 10.0
i4_huge = 2147483647
if (seed < 0):
seed = seed + i4_huge
# end if
if (seed == 0):
print('' )
print( 'R8MAT_UNIFORM_AB - Fatal error!')
print(' Input SEED = 0!' )
sys.ext('R8MAT_UNIFORM_AB - Fatal error!')
# end if
pos = np.zeros( (d_num, p_num) )
for j in range(0, p_num):
for i in range(0, d_num):
k = (seed // 127773)
seed = 16807 * (seed - k * 127773) - k * 2836
seed = (seed % i4_huge)
if (seed < 0):
seed = seed + i4_huge
# end if
pos[i,j] = a + (b - a) * seed * 4.656612875E-10
# end for
# end for
# Velocities
vel = np.zeros([ d_num, p_num ])
# Accelerations
acc = np.zeros([ d_num, p_num ])
else:
# Update
# Update positions
for i in range(0, d_num):
for j in range(0, p_num):
pos[i,j] = pos[i,j] + vel[i,j] * dt + 0.5 * acc[i,j] * dt * dt
# end for
# end for
# Update velocities
for i in range(0, d_num):
for j in range(0, p_num):
vel[i,j] = vel[i,j] + 0.5 * dt * ( force[i,j] * rmass + acc[i,j] )
# end for
# end for
# Update accelerations.
for i in range(0, d_num):
for j in range(0, p_num):
acc[i,j] = force[i,j] * rmass
# end for
# end for
# endif
# compute force, potential, kinetic
force = np.zeros([ d_num, p_num ])
rij = np.zeros(d_num)
potential = 0.0
for i in range(0, p_num):
# Compute the potential energy and forces.
for j in range(0, p_num):
if (i != j):
# Compute RIJ, the displacement vector.
for k in range(0, d_num):
rij[k] = pos[k,i] - pos[k,j]
# end for
# Compute D and D2, a distance and a truncated distance.
d = 0.0
for k in range(0, d_num):
d = d + rij[k] ** 2
# end for
d = np.sqrt(d)
d2 = min(d, np.pi / 2.0)
# Attribute half of the total potential energy to particle J.
potential = potential + 0.5 * np.sin(d2) * np.sin(d2)
# Add particle J's contribution to the force on particle I.
for k in range(0, d_num):
force[k,i] = force[k,i] - rij[k] * np.sin(2.0 * d2) / d
# end for
# end if
# end for
# end for
# Compute the kinetic energy
kinetic = 0.0
for k in range(0, d_num):
for j in range(0, p_num):
kinetic = kinetic + vel[k,j] ** 2
# end for
# end for
kinetic = 0.5 * mass * kinetic
if (step == 0):
e0 = potential + kinetic
# endif
if (step == step_print):
rel = (potential + kinetic - e0) / e0
print(' %8d %14f %14f %14g' % (step, potential, kinetic, rel) )
step_print_index = step_print_index + 1
step_print = (step_print_index * step_num) // step_print_num
#end if
# end step
wtime2 = clock( )
print('')
print(' Elapsed wall clock time = %g seconds.' % (wtime2 - wtime1) )
# Terminate
print('')
print('MD_TEST')
print(' Normal end of execution.')
timestamp ( )
# end ifListing 6 is the top of the profile output ordered by cumulative time (cumtime). Notice that the profile output only lists the code itself. Because it doesn’t profile the code line-by-line, it’s impossible to learn anything about the code.
Listing 6: cProfile Output
Sat Oct 26 09:43:21 2019
12791090 function calls (12788322 primitive calls) in 163.299 seconds
Ordered by: cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
148/1 0.001 0.000 163.299 163.299 {built-in method builtins.exec}
1 159.297 159.297 163.299 163.299 md_001.py:3()
12724903 3.918 0.000 3.918 0.000 {built-in method builtins.min}
175/2 0.001 0.000 0.083 0.042 :978(_find_and_load)
175/2 0.001 0.000 0.083 0.042 :948(_find_and_load_unlocked)
165/2 0.001 0.000 0.083 0.041 :663(_load_unlocked)
...I also used pprofile:
$ pprofile md_001.pyThe default options cause the code to run much slower because it is tracking all computations (i.e., it is not sampling), but the code lines relative to the run time still impart some good information (Listing 7). Note that the code ran slower by about a factor of 10. Only the parts of the code with some fairly large percentages of time are shown.
Listing 7: pprofile Output
Command line: ./md_001.py Total duration: 1510.79s File: ./md_001.py File duration: 1510.04s (99.95%) Line #| Hits| Time| Time per hit| %|Source code ------+----------+-------------+-------------+-------+----------- ... 141| 25551| 0.0946999| 3.70631e-06| 0.01%| for i in range(0, p_num): 142| 0| 0| 0| 0.00%| 143| 0| 0| 0| 0.00%| # Compute the potential energy and forces. 144| 12775500| 47.1989| 3.69449e-06| 3.12%| for j in range(0, p_num): 145| 12750000| 47.4793| 3.72387e-06| 3.14%| if (i != j): 146| 0| 0| 0| 0.00%| 147| 0| 0| 0| 0.00%| # Compute RIJ, the displacement vector. 148| 50898000| 194.963| 3.83046e-06| 12.90%| for k in range(0, d_num): 149| 38173500| 166.983| 4.37432e-06| 11.05%| rij[k] = pos[k,i] - pos[k,j] 150| 0| 0| 0| 0.00%| # end for 151| 0| 0| 0| 0.00%| 152| 0| 0| 0| 0.00%| # Compute D and D2, a distance and a truncated distance. 153| 0| 0| 0| 0.00%| 154| 12724500| 46.7333| 3.6727e-06| 3.09%| d = 0.0 155| 50898000| 195.426| 3.83956e-06| 12.94%| for k in range(0, d_num): 156| 38173500| 165.494| 4.33531e-06| 10.95%| d = d + rij[k] ** 2 157| 0| 0| 0| 0.00%| # end for 158| 12724500| 72.0723| 5.66406e-06| 4.77%| d = np.sqrt(d) 159| 12724500| 59.0492| 4.64059e-06| 3.91%| d2 = min(d, np.pi / 2.0) 160| 0| 0| 0| 0.00%| 161| 0| 0| 0| 0.00%| # Attribute half of the total potential energy to particle J. 162| 12724500| 76.7099| 6.02852e-06| 5.08%| potential = potential + 0.5 * np.sin(d2) * np.sin(d2) 163| 0| 0| 0| 0.00%| 164| 0| 0| 0| 0.00%| # Add particle J's contribution to the force on particle I. 165| 50898000| 207.158| 4.07005e-06| 13.71%| for k in range(0, d_num): 166| 38173500| 228.123| 5.97595e-06| 15.10%| force[k,i] = force[k,i] - rij[k] * np.sin(2.0 * d2) / d 167| 0| 0| 0| 0.00%| # end for 168| 0| 0| 0| 0.00%| # end if 169| 0| 0| 0| 0.00%| 170| 0| 0| 0| 0.00%| # end for 171| 0| 0| 0| 0.00%| # end for 172| 0| 0| 0| 0.00%| ...
The output from pprofile provides some indication of where the code uses the most time:
- The loop computing rij[k]
- The loop summing d (collective operation)
- Computing the square root of d
- Computing d2
- Computing the potential energy
- The loop computing the force array
Another option is to put timing points throughout the code, focusing primarily on the section of the code computing potential energy and forces. This code produced the output shown in Listing 8. Notice that the time to compute the potential and forces is 181.9 seconds with a total time of 189.5 seconds. Obviously, this is where you would need to focus your efforts to improve code performance.
Listing 8: md_001b.py Output
Elapsed wall clock time = 189.526 seconds. Total Time for position update = 0.089102 seconds Avg Time for position update = 0.001782 seconds Total Time for velocity update = 0.073946 seconds Avg Time for velocity update = 0.001479 seconds Total Time for acceleration update = 0.031308 seconds Avg Time for acceleration update = 0.000626 seconds Total Time for potential update = 103.215999 seconds Avg Time for potential update = 0.000008 seconds Total Time for force update = 181.927011 seconds Avg Time for force update = 0.000014 seconds Total Time for force loop = 75.269342 seconds Avg Time for force loop = 0.000006 seconds Total Time for rij loop = 25.444300 seconds Avg Time for rij loop = 0.000002 seconds MD_TEST Normal end of execution.
First Function Creation
The potential energy and force computations are the dominant part of the run time, so to better profile them, it is best to isolate that code in a function. Perhaps a bit counterintuitively, I created a function that initializes the algorithm and a second function for the update loops, and called the resulting code md_002.py. (My modified code is available online.) Because the potential energy and force computations change very little, I won’t be profiling this version of the code. All I did was make sure I was getting the same answers as in the previous version. However, feel free to practice profiling it.
Final Version
The final version of the code moves the section of code computing the potential energy and forces into a function for better profiling. The code, md_003.py, has a properties function that computes the potential energy and forces.
The cProfile results don’t show anything useful, so I will skip that output. On the other hand, the pprofile output has some useful information (Listing 9). The excerpts mostly focus on the function that computes potential energy and forces. Notice that the overall time to run the code is still about 10 times longer.
Listing 9: md_003.py Output Excerpts
Command line: md_003.py Total duration: 1459.49s File: md_003.py File duration: 1458.73s (99.95%) Line #| Hits| Time| Time per hit| %|Source code ------+----------+-------------+-------------+-------+----------- 1| 0| 0| 0| 0.00%|# md test code 2| 0| 0| 0| 0.00%| 3| 2| 3.40939e-05| 1.70469e-05| 0.00%|import platform 4| 1| 2.28882e-05| 2.28882e-05| 0.00%|from time import clock (call)| 1| 2.71797e-05| 2.71797e-05| 0.00%|# :1009 _handle_fromlist 5| 1| 2.69413e-05| 2.69413e-05| 0.00%|import numpy as np (call)| 1| 0.751632| 0.751632| 0.05%|# :978 _find_and_load 6| 1| 2.47955e-05| 2.47955e-05| 0.00%|from sys import exit (call)| 1| 1.78814e-05| 1.78814e-05| 0.00%|# :1009 _handle_fromlist 7| 1| 8.34465e-06| 8.34465e-06| 0.00%|import time ... 160| 51| 0.000295162| 5.78749e-06| 0.00%|def properties(p_num, d_num, pos, vel, mass): 161| 0| 0| 0| 0.00%| 162| 50| 0.000397682| 7.95364e-06| 0.00%| import numpy as np 163| 0| 0| 0| 0.00%| 164| 0| 0| 0| 0.00%| 165| 0| 0| 0| 0.00%| # compute force, potential, kinetic 166| 50| 0.000529528| 1.05906e-05| 0.00%| force = np.zeros([ d_num, p_num ]) 167| 50| 0.000378609| 7.57217e-06| 0.00%| rij = np.zeros(d_num) 168| 0| 0| 0| 0.00%| 169| 50| 0.000226259| 4.52518e-06| 0.00%| potential = 0.0 170| 0| 0| 0| 0.00%| 171| 25050| 0.0909703| 3.63155e-06| 0.01%| for i in range(0, p_num): 172| 0| 0| 0| 0.00%| 173| 0| 0| 0| 0.00%| # Compute the potential energy and forces 174| 12525000| 44.7758| 3.57492e-06| 3.07%| for j in range(0, p_num): 175| 12500000| 45.4399| 3.63519e-06| 3.11%| if (i != j): 176| 0| 0| 0| 0.00%| # Compute RIJ, the displacement vector 177| 49900000| 183.525| 3.67786e-06| 12.57%| for k in range(0, d_num): 178| 37425000| 155.539| 4.15603e-06| 10.66%| rij[k] = pos[k,i] - pos[k,j] 179| 0| 0| 0| 0.00%| # end for 180| 0| 0| 0| 0.00%| 181| 0| 0| 0| 0.00%| # Compute D and D2, a distance and a truncated distance 182| 12475000| 44.5996| 3.57512e-06| 3.06%| d = 0.0 183| 49900000| 184.464| 3.69668e-06| 12.64%| for k in range(0, d_num): 184| 37425000| 155.339| 4.15067e-06| 10.64%| d = d + rij[k] ** 2 185| 0| 0| 0| 0.00%| # end for 186| 12475000| 68.8519| 5.51919e-06| 4.72%| d = np.sqrt(d) 187| 12475000| 56.0835| 4.49567e-06| 3.84%| d2 = min(d, np.pi / 2.0) 188| 0| 0| 0| 0.00%| 189| 0| 0| 0| 0.00%| # Attribute half of the total potential energy to particle J 190| 12475000| 74.6307| 5.98242e-06| 5.11%| potential = potential + 0.5 * np.sin(d2) * np.sin(d2) 191| 0| 0| 0| 0.00%| 192| 0| 0| 0| 0.00%| # Add particle J's contribution to the force on particle I. 193| 49900000| 199.233| 3.99264e-06| 13.65%| for k in range(0, d_num): 194| 37425000| 212.352| 5.67406e-06| 14.55%| force[k,i] = force[k,i] - rij[k] * np.sin(2.0 * d2) / d 195| 0| 0| 0| 0.00%| # end for 196| 0| 0| 0| 0.00%| # end if 197| 0| 0| 0| 0.00%| 198| 0| 0| 0| 0.00%| # end for 199| 0| 0| 0| 0.00%| # end for 200| 0| 0| 0| 0.00%| 201| 0| 0| 0| 0.00%| # Compute the kinetic energy 202| 50| 0.000184059| 3.68118e-06| 0.00%| kinetic = 0.0 203| 200| 0.000753641| 3.76821e-06| 0.00%| for k in range(0, d_num): 204| 75150| 0.265535| 3.5334e-06| 0.02%| for j in range(0, p_num): 205| 75000| 0.298971| 3.98628e-06| 0.02%| kinetic = kinetic + vel[k,j] ** 2 206| 0| 0| 0| 0.00%| # end for 207| 0| 0| 0| 0.00%| # end for 208| 0| 0| 0| 0.00%| 209| 50| 0.000200987| 4.01974e-06| 0.00%| kinetic = 0.5 * mass * kinetic 210| 0| 0| 0| 0.00%| 211| 50| 0.000211716| 4.23431e-06| 0.00%| return force, kinetic, potential 212| 0| 0| 0| 0.00%| 213| 0| 0| 0| 0.00%|# end def ...
Again, most of the time in the code is spent in the properties function, which computes the potential energy and forces. The first few loops take up most of the time.
Out of curiosity, I looked at the output from line_profiler (Listing 10). Remember that all time percentages are relative to the time for that particular routine (not the entire code). The first couple of loops used a fair percentage of the run time. The last loop that computes the force array,
for k in range(0, d_num): force[k,i] = force[k,i] - rij[k] * np.sin(2.0 * d2) / d
used about 25 percent of the run time for this function.
Listing 10: md_003.py line_profiler Output
Total time: 358.778 s File: ./md_003.py Function: properties at line 159 Line # Hits Time Per Hit % Time Line Contents ============================================================== 159 @profile 160 def properties(p_num, d_num, pos, vel, mass): 161 162 50 164.0 3.3 0.0 import numpy as np 163 164 165 # compute force, potential, kinetic 166 50 351.0 7.0 0.0 force = np.zeros([ d_num, p_num ]) 167 50 153.0 3.1 0.0 rij = np.zeros(d_num) 168 169 50 30.0 0.6 0.0 potential = 0.0 170 171 25050 14447.0 0.6 0.0 for i in range(0, p_num): 172 173 # Compute the potential energy and forces 174 12525000 7036459.0 0.6 2.0 for j in range(0, p_num): 175 12500000 7730669.0 0.6 2.2 if (i != j): 176 # Compute RIJ, the displacement vector 177 49900000 33530834.0 0.7 9.3 for k in range(0, d_num): 178 37425000 39827594.0 1.1 11.1 rij[k] = pos[k,i] - pos[k,j] 179 # end for 180 181 # Compute D and D2, a distance and a truncated distance 182 12475000 7182783.0 0.6 2.0 d = 0.0 183 49900000 33037923.0 0.7 9.2 for k in range(0, d_num): 184 37425000 39131501.0 1.0 10.9 d = d + rij[k] ** 2 185 # end for 186 12475000 25236413.0 2.0 7.0 d = np.sqrt(d) 187 12475000 13375864.0 1.1 3.7 d2 = min(d, np.pi / 2.0) 188 189 # Attribute half of the total potential energy to particle J 190 12475000 31104186.0 2.5 8.7 potential = potential + 0.5 * np.sin(d2) * np.sin(d2) 191 192 # Add particle J's contribution to the force on particle I. 193 49900000 34782251.0 0.7 9.7 for k in range(0, d_num): 194 37425000 86664317.0 2.3 24.2 force[k,i] = force[k,i] - rij[k] * np.sin(2.0 * d2) / d 195 # end for 196 # end if 197 198 # end for 199 # end for 200 201 # Compute the kinetic energy 202 50 33.0 0.7 0.0 kinetic = 0.0 203 200 147.0 0.7 0.0 for k in range(0, d_num): 204 75150 44048.0 0.6 0.0 for j in range(0, p_num): 205 75000 78144.0 1.0 0.0 kinetic = kinetic + vel[k,j] ** 2 206 # end for 207 # end for 208 209 50 47.0 0.9 0.0 kinetic = 0.5 * mass * kinetic 210 211 50 37.0 0.7 0.0 return force, kinetic, potential
If you look at this routine in the md_003.py file, I’m sure you can find some optimizations that would improve performance.
Using Numba
As part of the profiling, I moved parts of the code to functions. With Python, this allowed me to perform deterministic profiling without resulting in a long run time. Personally, I like deterministic profiling better than statistical because I don’t have to find the time interval that results in a good profile.
Putting parts of the code in functions provides a good starting point for using Numba. I described the use of Numba in a previous high-performance Python article. I made a few more changes to the last version of the code (md_003.py), and because the properties routine took a majority of the run time, I targeted this routine with Numba and simply used jit to compile the code.
The original code, before using Numba, was around 140 seconds on my laptop. After using Numba and running on all eight cores of my laptop (four "real" cores and four Hyper Threading (HT) cores), it ran in about 3.6 seconds. I would call that a success.
Summary
Profiling Python is not always the easiest of tasks, but I hope I’ve covered some of the tools you might use. Before using any of the tools, be sure you know how it does the profiling – deterministic or statistical – and what it is profiling – the entire code or just a function.
Although I didn’t talk much about putting timing points in code (manual profiling), I’m a bit old school. That level of control lets me gather timing data for various portions of code pretty easily. If you are old school like me, you are probably already using this method in your code. If you haven’t done it before, I suggest giving it a try.
In using one or more of the profiling tools, I suggest putting code in functions and profiling those functions deterministically, if possible, so you can isolate various parts of a program. While you are isolating parts of your code in functions, why not take advantage of the situation and look at using Numba to compile these functions? The speed-up obtained can be pretty amazing.
Tags:
HPC , Programming , Python