# Particle simulator using Python - Why is the NumPy version slower than pure Python?

This is an interesting example from G.Lanaro's book, Python High Performance. The program is a simple simulator which describes movement of particles based on their positions and angular velocities (x, y, and ang_v attributes of a Particle class). The bottleneck of the program is the evolve method which is initially implemented using pure Python, and then the author tries to show how we can improve the performance of this specific method using NumPy operations.

My problem is that when I compare the run-time of NumPy version (evolve_numpy) versus the pure Python version (evolve), I can hardly see any improvement in terms of runtime; in fact, it seems like pure Python is a bit faster here.

I wrote all of this code myself, based off of what I learned in the book.

The initial version of evolve is as follows

    def evolve(self, dt):
timestep = .00001
nstep = int(dt / timestep)
for i in range(nstep):
for p in self.particles:
t_x_ang = timestep * p.ang_v
norm = (p.x ** 2 + p.y ** 2) ** .5
p.x, p.y = (p.x - t_x_ang * p.y / norm, p.y + t_x_ang * p.x/norm)

# repeat for all particles
# repeat for all time-steps


Here is the NumPy version of the same method,

    def evolve_numpy(self, dt):
timestep = 0.00001
nsteps = int(dt / timestep)
r_i = np.array([[p.x, p.y] for p in self.particles])
ang_vel_i = np.array([p.ang_v for p in self.particles])
for i in range(nsteps):
norm_i = np.sqrt((r_i ** 2).sum(axis=1))
v_i = r_i[:, [1, 0]]
v_i[:, 0] *= -1
v_i /= norm_i[:, np.newaxis]
d_i = timestep * ang_vel_i[:, np.newaxis] * v_i
r_i += d_i
for i, p in enumerate(self.particles):
p.x, p.y = r_i[i]


To test the runtime of both versions, a benchmark2 function is defined as follows:

def benchmark2(nparts, m):
particles = [Particle(uniform(-1, 1), uniform(-1, 1), uniform(-1, 1))
for i in range(nparts)]
sim = ParticleSimualtor(particles)
if m == 1:
sim.evolve(.1)
elif m == 2:
sim.evolve_numpy(.1)
else:
raise Exception('not a valid method')


You can access the complete code here: https://gist.github.com/OmidMiresmaeili/2891c4f1ce95f65cfab8ed0469e427d4

My results are as follows:

1min 40s for NumPy version and 1min 29s for Python version

But the author claim (and I understand the reasoning) that the NumPy version runs much faster than the initial version of evolve without any NumPy operations:

This is a figure from page 71 of the book. It shows run-time for different particle size (nparts argument in the benchmark2 function). The author then concludes:

The plot shows that both the implementations scale linearly with particle size, but the runtime in the pure Python version grows much faster than the NumPy version; at greater sizes, we have a greater NumPy advantage. In general, when using NumPy, you should try to pack things into large arrays and group the calculations using the broadcasting feature.

Now I'm not sure if it is a typo in the book or maybe I am not understanding this correctly. I would appreciate any input you can give me.

• I've rolled back the edit to your question, as Code Review doesn't allow revisions to your code based on feedback. Technically this is a little weird, because you didn't actually get an answer, but the comments did provide a review. Graipher/AlexV (unfortunately can't @ you both), I've posted Omid's update + your details as a CW answer. – Dan Oberlam Oct 3 '19 at 13:52
• I wrote all of this code myself, based off of what I learned in the book. - Given this, why are there close votes claiming authorship issues? – Reinderien Oct 4 '19 at 3:40

Community wiki - update as posted by the OP in the question based on review by Graipher and AlexV

As Graipher and AlexV mentioned the problem with NumPy version was that the inner loop which updates particle positions (x and y) should be performed independently (not repeated in each timestep). The updated version is as follows,

def evolve_numpy(self, dt):
timestep = 0.00001
nsteps = int(dt / timestep)
r_i = np.array([[p.x, p.y] for p in self.particles])
ang_vel_i = np.array([p.ang_v for p in self.particles])
for i in range(nsteps):
norm_i = np.sqrt((r_i ** 2).sum(axis=1))
v_i = r_i[:, [1, 0]]
v_i[:, 0] *= -1
v_i /= norm_i[:, np.newaxis]
d_i = timestep * ang_vel_i[:, np.newaxis] * v_i
r_i += d_i
for i, p in enumerate(self.particles):
p.x, p.y = r_i[i]


Doing this improves the results considerably (see the figure below) which shows that the results stated in the book were accurate and this was my mistake (as expected:)),

(The runtime of numpy method is now less than 4 seconds.)