My python code takes about 6.2 seconds to run. The Matlab code runs in under 0.05 seconds. Why is this and what can I do to speed up the Python code? Is Cython the solution?
Matlab:
function X=Test
nIter=1000000;
Step=.001;
X0=1;
X=zeros(1,nIter+1); X(1)=X0;
tic
for i=1:nIter
X(i+1)=X(i)+Step*(X(i)^2*cos(i*Step+X(i)));
end
toc
figure(1) plot(0:nIter,X)
Python:
nIter = 1000000
Step = .001
x = np.zeros(1+nIter)
x[0] = 1
start = time.time()
for i in range(1,1+nIter):
x[i] = x[i-1] + Step*x[i-1]**2*np.cos(Step*(i-1)+x[i-1])
end = time.time()
print(end - start)
How to speed up your Python code
Your largest time sink is np.cos which performs several checks on the format of the input.
These are relevant and usually negligible for high-dimensional inputs, but for your one-dimensional input, this becomes the bottleneck.
The solution to this is to use math.cos, which only accepts one-dimensional numbers as input and thus is faster (though less flexible).
Another time sink is indexing x multiple times.
You can speed this up by having one state variable which you update and only writing to x once per iteration.
With all of this, you can speed up things by a factor of roughly ten:
import numpy as np
from math import cos
nIter = 1000000
Step = .001
x = np.zeros(1+nIter)
state = x[0] = 1
for i in range(nIter):
state += Step*state**2*cos(Step*i+state)
x[i+1] = state
Now, your main problem is that your truly innermost loop happens completely in Python, i.e., you have a lot of wrapping operations that eat up time.
You can avoid this by using uFuncs (e.g., created with SymPy’s ufuncify) and using NumPy’s accumulate:
import numpy as np
from sympy.utilities.autowrap import ufuncify
from sympy.abc import t,y
from sympy import cos
nIter = 1000000
Step = 0.001
state = x[0] = 1
f = ufuncify([y,t],y+Step*y**2*cos(t+y))
times = np.arange(0,nIter*Step,Step)
times[0] = 1
x = f.accumulate(times)
This runs practically within an instant.
… and why that’s not what you should worry about
If your exact code (and only that) is what you care about, then you shouldn’t worry about runtime anyway, because it’s very short either way.
If on the other hand, you use this to gauge efficiency for problems with a considerable runtime, your example will fail because it considers only one initial condition and is a very simple dynamics.
Moreover, you are using the Euler method, which is either not very efficient or robust, depending on your step size.
The latter (Step) is absurdly low in your case, yielding much more data than you probably need:
With a step size of 1, You can see what’s going on just fine.
If you want a robust integration in such cases, it’s almost always best to use a modern adaptive integrator, that can adjust its step size itself, e.g., here is a solution to your problem using a native Python integrator:
from math import cos
import numpy as np
from scipy.integrate import solve_ivp
T = 1000
dt = 0.001
x = solve_ivp(
lambda t,state: state**2*cos(t+state),
t_span = (0,T),
t_eval = np.arange(0,T,dt),
y0 = [1],
rtol = 1e-5
).y
This automatically adjusts the step size to something higher, depending on the error tolerance rtol.
It still returns the same amount of output data, but that’s via interpolation of the solution.
It runs in 0.3 s for me.
How to speed up things in a scalable manner
If you still need to speed up something like this, chances are that your derivative (f) is considerably more complex than in your example and thus it is the bottleneck.
Depending on your problem, you may be able to vectorise its calcultion (using NumPy or similar).
If you can’t vectorise, I wrote a module that specifically focusses on this by hard-coding your derivative under the hood.
Here is your example in with a sampling step of 1.
import numpy as np
from jitcode import jitcode,y,t
from symengine import cos
T = 1000
dt = 1
ODE = jitcode([y(0)**2*cos(t+y(0))])
ODE.set_initial_value([1])
ODE.set_integrator("dop853")
x = np.hstack([ODE.integrate(t) for t in np.arange(0,T,dt)])
This runs again within an instant. While this may not be a relevant speed boost here, this is scalable to huge systems.
The difference is jit-compilation, which Matlab uses per default. Let's try your example with Numba(a Python jit-compiler)
Code
import numba as nb
import numpy as np
import time
nIter = 1000000
Step = .001
#nb.njit()
def integrate(nIter,Step):
x = np.zeros(1+nIter)
x[0] = 1
for i in range(1,1+nIter):
x[i] = x[i-1] + Step*x[i-1]**2*np.cos(Step*(i-1)+x[i-1])
return x
#Avoid measuring the compilation time,
#this would be also recommendable for Matlab to have a fair comparison
res=integrate(nIter,Step)
start = time.time()
for i in range(100):
res=integrate(nIter,Step)
end=time.time()
print((end - start)/100)
This results in 0.022s runtime per call.
Related
I would like to know, is there any simple method to parallel einsum in Numpy?
I found some discussions
Numpy np.einsum array multiplication using multiple cores
Any chance of making this faster? (numpy.einsum)
numpy.tensordot() only for binary contraction with a single axis, Numba needs to specify certain loops. Is there any simple and robust approach to parallel einsum (possibly including opt-einsum, tf-einsum etc) with arbitrary contractions?
A sample code is as following (if necessary I can use more complicated contraction as the example)
import numpy as np
import timeit
import time
na = nc = 1000
nb = 1000
n_iter = 10
A = np.random.random((na,nb))
B = np.random.random((nb,nc))
t_total = 0.
for i in range(n_iter):
start = time.time()
C = np.einsum('ij,jk->ik', A, B)
end = time.time()
t_total += end - start
print('AB->C',(t_total)/n_iter)
I want to use the method of lines to solve the thin-film equation. I have implemented it (with gamma=mu=0) Matlab using ode15s and it seems to work fine:
N = 64;
x = linspace(-1,1,N+1);
x = x(1:end-1);
dx = x(2)-x(1);
T = 1e-2;
h0 = 1+0.1*cos(pi*x);
[t,h] = ode15s(#(t,y) thinFilmEq(t,y,dx), [0,T], h0);
function dhdt = thinFilmEq(t,h,dx)
phi = 0;
hxx = (circshift(h,1) - 2*h + circshift(h,-1))/dx^2;
p = phi - hxx;
px = (circshift(p,-1)-circshift(p,1))/dx;
flux = (h.^3).*px/3;
dhdt = (circshift(flux,-1) - circshift(flux,1))/dx;
end
The film just flattens after some time, and for large time the film should tend to h(t->inf)=1. I haven't done any rigorous check and convergence analysis, but at least the result looks promising after only spending less than 5 mins to code it.
I want to do the same thing in Python, and I tried the following:
import numpy as np
import scipy.integrate as spi
def thin_film_eq(t,h,dx):
print(t) # to check the current evaluation time for debugging
phi = 0
hxx = (np.roll(h,1) - 2*h + np.roll(h,-1))/dx**2
p = phi - hxx
px = (np.roll(p,-1) - np.roll(p,1))/dx
flux = h**3*px/3
dhdt = (np.roll(flux,-1) - np.roll(flux,1))/dx
return dhdt
N = 64
x = np.linspace(-1,1,N+1)[:-1]
dx = x[1]-x[0]
T = 1e-2
h0 = 1 + 0.1*np.cos(np.pi*x)
sol = spi.solve_ivp(lambda t,h: thin_film_eq(t,h,dx), (0,T), h0, method='BDF', vectorized=True)
I add a print statement inside the function so I can check the current progress of the program. For some reasons, it is taking very tiny time step and after waiting for a few minutes it is still stuck at t=3.465e-5, with dt smaller than 1e-10. (haven't finished yet by the time I finished typing this question, and it probably won't within any reasonable time). For the Matlab program, it is done within a second with only 14 time steps taken (I only specify the time span, and it outputs 14 time steps with everything else kept at default). I want to ask the following:
Have I done anything wrong which dramatically slows down the computation time for my Python code? What settings should I choose for the solve_ivp function call? One thing I'm not sure is if I do the vectorization properly. Also did I write the function in the correct way? I know this is a stiff ODE, but the ultra-small time step taken by
Is the difference really just down to the difference in the ode solver? scipy.integrate.solve_ivp(f, method='BDF') is the recommended substitute of ode15s according to the official numpy website. But for this particular example the performance difference is one second vs takes ages to solve. The difference is a lot bigger than I thought.
Are there other alternative methods I can try in Python for solving similar PDEs? (something along the line of finite difference/method of lines) I mean utilizing existing libraries, preferably those in scipy.
I am trying to integrate over some matrix entries in Python. I want to avoid loops, because my tasks includes 1 Mio simulations. I am looking for a specification that will efficiently solve my problem.
I get the following error: only size-1 arrays can be converted to Python scalars
from scipy import integrate
import numpy.random as npr
n = 1000
m = 30
x = npr.standard_normal([n, m])
def integrand(k):
return k * x ** 2
integrate.quad(integrand, 0, 100)
This is a simplied example of my case. I have multiple nested functions, such that I cannot simple put x infront of the integral.
Well you might want to use parallel execution for this. It should be quite easy as long as you just want to execute integrate.quad 30000000 times. Just split your workload in little packages and give it to a threadpool. Of course the speedup is limited to the number of cores you have in your pc. I'm not a python programer but this should be possible. You can also increase epsabs and epsrel parameters in the quad function, depending on the implemetation this should speed up the programm as well. Of course you'll get a less precise result but this might be ok depending on your problem.
import threading
from scipy import integrate
import numpy.random as npr
n = 2
m = 3
x = npr.standard_normal([n,m])
def f(a):
for j in range(m):
integrand = lambda k: k * x[a,j]**2
i =integrate.quad(integrand, 0, 100)
print(i) ##write it to result array
for i in range(n):
threading.Thread(target=f(i)).start();
##better split it up even more and give it to a threadpool to avoid
##overhead because of thread init
This is maybe not the ideal solution but it should help a bit. You can use numpy.vectorize. Even the doc says: The vectorize function is provided primarily for convenience, not for performance. The implementation is essentially a for loop. But still, a %timeit on the simple example you provided shows a 2.3x speedup.
The implementation is
from scipy import integrate
from numpy import vectorize
import numpy.random as npr
n = 1000
m = 30
x = npr.standard_normal([n,m])
def g(x):
integrand = lambda k: k * x**2
return integrate.quad(integrand, 0, 100)
vg = vectorize(g)
res = vg(x)
quadpy (a project of mine) does vectorized quadrature:
import numpy
import numpy.random as npr
import quadpy
x = npr.standard_normal([1000, 30])
def integrand(k):
return numpy.multiply.outer(x ** 2, k)
scheme = quadpy.line_segment.gauss_legendre(10)
val = scheme.integrate(integrand, [0, 100])
This is much faster than all other answers.
Following my former question [1], I would like to apply multiprocessing to matplotlib's griddata function. Is it possible to split the griddata into, say 4 parts, one for each of my 4 cores? I need this to improve performance.
For example, try the code below, experimenting with different values for size:
import numpy as np
import matplotlib.mlab as mlab
import time
size = 500
Y = np.arange(size)
X = np.arange(size)
x, y = np.meshgrid(X, Y)
u = x * np.sin(5) + y * np.cos(5)
v = x * np.cos(5) + y * np.sin(5)
test = x + y
tic = time.clock()
test_d = mlab.griddata(
x.flatten(), y.flatten(), test.flatten(), x+u, y+v, interp='linear')
toc = time.clock()
print 'Time=', toc-tic
I ran the example code below in Python 3.4.2, with numpy version 1.9.1 and matplotlib version 1.4.2, on a Macbook Pro with 4 physical CPUs (i.e., as opposed to "virtual" CPUs, which the Mac hardware architecture also makes available for some use cases):
import numpy as np
import matplotlib.mlab as mlab
import time
import multiprocessing
# This value should be set much larger than nprocs, defined later below
size = 500
Y = np.arange(size)
X = np.arange(size)
x, y = np.meshgrid(X, Y)
u = x * np.sin(5) + y * np.cos(5)
v = x * np.cos(5) + y * np.sin(5)
test = x + y
tic = time.clock()
test_d = mlab.griddata(
x.flatten(), y.flatten(), test.flatten(), x+u, y+v, interp='linear')
toc = time.clock()
print('Single Processor Time={0}'.format(toc-tic))
# Put interpolation points into a single array so that we can slice it easily
xi = x + u
yi = y + v
# My example test machine has 4 physical CPUs
nprocs = 4
jump = int(size/nprocs)
# Enclose the griddata function in a wrapper which will communicate its
# output result back to the calling process via a Queue
def wrapper(x, y, z, xi, yi, q):
test_w = mlab.griddata(x, y, z, xi, yi, interp='linear')
q.put(test_w)
# Measure the elapsed time for multiprocessing separately
ticm = time.clock()
queue, process = [], []
for n in range(nprocs):
queue.append(multiprocessing.Queue())
# Handle the possibility that size is not evenly divisible by nprocs
if n == (nprocs-1):
finalidx = size
else:
finalidx = (n + 1) * jump
# Define the arguments, dividing the interpolation variables into
# nprocs roughly evenly sized slices
argtuple = (x.flatten(), y.flatten(), test.flatten(),
xi[:,(n*jump):finalidx], yi[:,(n*jump):finalidx], queue[-1])
# Create the processes, and launch them
process.append(multiprocessing.Process(target=wrapper, args=argtuple))
process[-1].start()
# Initialize an array to hold the return value, and make sure that it is
# null-valued but of the appropriate size
test_m = np.asarray([[] for s in range(size)])
# Read the individual results back from the queues and concatenate them
# into the return array
for q, p in zip(queue, process):
test_m = np.concatenate((test_m, q.get()), axis=1)
p.join()
tocm = time.clock()
print('Multiprocessing Time={0}'.format(tocm-ticm))
# Check that the result of both methods is actually the same; should raise
# an AssertionError exception if assertion is not True
assert np.all(test_d == test_m)
and I got the following result:
/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/matplotlib/tri/triangulation.py:110: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.self._neighbors)
Single Processor Time=8.495998
Multiprocessing Time=2.249938
I'm not really sure what is causing the "future warning" from triangulation.py (evidently my version of matplotlib did not like something about the input values that were originally provided for the question), but regardless, the multiprocessing does appear to achieve the desired speedup of 8.50/2.25 = 3.8, (edit: see comments) which is roughly in the neighborhood of about 4X that we would expect for a machine with 4 CPUs. And the assertion statement at the end also executes successfully, proving that the two methods get the same answer, so in spite of the slightly weird warning message, I believe that the code above is a valid solution.
EDIT: A commenter has pointed out that both my solution, as well as the code snippet posted by the original author, are likely using the wrong method, time.clock(), for measuring execution time; he suggests using time.time() instead. I think I'm also coming around to his point of view. (Digging into the Python documentation a bit further, I'm still not convinced that even this solution is 100% correct, as newer versions of Python appear to have deprecated time.clock() in favor of time.perf_counter() and time.process_time(). But regardless, I do agree that whether or not time.time() is absolutely the most correct way of taking this measurement, it's still probably more correct than what I had been using before, time.clock().)
Assuming the commenter's point is correct, then it means the approximately 4X speedup that I thought I had measured is in fact wrong.
However, that does not mean that the underlying code itself wasn't correctly parallelized; rather, it just means that parallelization didn't actually help in this case; splitting up the data and running on multiple processors didn't improve anything. Why would this be? Other users have pointed out that, at least in numpy/scipy, some functions run on multiple cores, and some do not, and it can be a seriously challenging research project for an end-user to try to figure out which ones are which.
Based on the results of this experiment, if my solution correctly achieves parallelization within Python, but no further speedup is observed, then I would suggest the simplest likely explanation is that matplotlib is probably also parallelizing some of its functions "under the hood", so to speak, in compiled C++ libraries, just like numpy/scipy already do. Assuming that's the case, then the correct answer to this question would be that nothing further can be done: further parallelizing in Python will do no good if the underlying C++ libraries are already silently running on multiple cores to begin with.
Maybe I'm doing something odd, but maybe found a surprising performance loss when using numpy, seems consistent regardless of the power used. For instance when x is a random 100x100 array
x = numpy.power(x,3)
is about 60x slower than
x = x*x*x
A plot of the speed up for various array sizes reveals a sweet spot with arrays around size 10k and a consistent 5-10x speed up for other sizes.
Code to test below on your own machine (a little messy):
import numpy as np
from matplotlib import pyplot as plt
from time import time
ratios = []
sizes = []
for n in np.logspace(1,3,20).astype(int):
a = np.random.randn(n,n)
inline_times = []
for i in range(100):
t = time()
b = a*a*a
inline_times.append(time()-t)
inline_time = np.mean(inline_times)
pow_times = []
for i in range(100):
t = time()
b = np.power(a,3)
pow_times.append(time()-t)
pow_time = np.mean(pow_times)
sizes.append(a.size)
ratios.append(pow_time/inline_time)
plt.plot(sizes,ratios)
plt.title('Performance of inline vs numpy.power')
plt.ylabel('Nx speed-up using inline')
plt.xlabel('Array size')
plt.xscale('log')
plt.show()
Anyone have an explanation?
It's well known that multiplication of doubles, which your processor can do in a very fancy way, is very, very fast. pow is decidedly slower.
Some performance guides out there even advise people to plan for this, perhaps even in some way that might be a bit overzealous at times.
numpy special-cases squaring to make sure it's not too, too slow, but it sends cubing right off to your libc's pow, which isn't nearly as fast as a couple multiplications.
I suspect the issue is that np.power always does float exponentiation, and it doesn't know how to optimize or vectorize that on your platform (or, probably, most/all platforms), while multiplication is easy to toss into SSE, and pretty fast even if you don't.
Even if np.power were smart enough to do integer exponentiation separately, unless it unrolled small values into repeated multiplication, it still wouldn't be nearly as fast.
You can verify this pretty easily by comparing the time for int-to-int, int-to-float, float-to-int, and float-to-float powers vs. multiplication for a small array; int-to-int is about 5x as fast as the others—but still 4x slower than multiplication (although I tested with PyPy with a customized NumPy, so it's probably better for someone with the normal NumPy installed on CPython to give real results…)
The performance of numpys power function scales very non-linearly with the exponent. Constrast this with the naive approach which does. The same type of scaling should exist, regardless of matrix size. Basically, unless the exponent is sufficiently large, you aren't going to see any tangible benefit.
import matplotlib.pyplot as plt
import numpy as np
import functools
import time
def timeit(func):
#functools.wraps(func)
def newfunc(*args, **kwargs):
startTime = time.time()
res = func(*args, **kwargs)
elapsedTime = time.time() - startTime
return (res, elapsedTime)
return newfunc
#timeit
def naive_power(m, n):
m = np.asarray(m)
res = m.copy()
for i in xrange(1,n):
res *= m
return res
#timeit
def fast_power(m, n):
# elementwise power
return np.power(m, n)
m = np.random.random((100,100))
n = 400
rs1 = []
ts1 = []
ts2 = []
for i in xrange(1, n):
r1, t1 = naive_power(m, i)
ts1.append(t1)
for i in xrange(1, n):
r2, t2 = fast_power(m, i)
ts2.append(t2)
plt.plot(ts1, label='naive')
plt.plot(ts2, label='numpy')
plt.xlabel('exponent')
plt.ylabel('time')
plt.legend(loc='upper left')