I looked at Numba a few weeks ago and it looked very impressive. One question I had that I did not see addressed in Numba's documentation was whether Numba is generally applicable or if it is focused on mathematical computation? I understand that math loops might be most in need to speed-up, but it would still be nice to speed-up non-math-centric code (e.g. Django...).
do you really think it'll ever be able to compile arbitrary Python and make it fast most of the time? Cython never got there and numba is having a vastly different purpose.
There are a lot of ambitious projects in this arena such as PyPy. Numba's approach is focused on getting computational codes faster. It's hard to say what "fast" means in many situations, because people don't realize that Python is actually quite fast for most tasks.
Cython isn't able to compile all Python. It definitely can compile a large subset of Python, but certainly not all of it. I don't actually know how fast the compiled Cython is - in my (very limited) experience, if you don't provide types, it's not a massive speedup over CPython.
I get 10ms to cluster and segment an image with tuned numpy and opencv. With better results. Indeed the loops regarding the inner patch convolution are the killer. I used fingmineig, corner detectors, to turn a pixel location into a vector of corners at different spatial scales. Those routines are performed in parralell on opencv so I got rid of the inner patch kernel and it all delegates to blas. No loops
I'd say that yes, if you are just performing an operation that already exists in a common C library just use that. But I could see the advantages for an algorithm that did not already exist for which you wanted to express in Python.
OpenCV is great as a black box but its built in functions are not always sufficient or optimal.
EDIT: Formatting is a bit whacked. I have two consecutive frames of a sonar image (same size) as the input, you will have to exchange those parts but I ran it fine. There are more parameters in the "DEFAULT_PARAMS" dict as I copied this from a much larger program. I work in greyscale so that might actually be a problem for genralization.
Hi tlarkworthy, thanks for sharing your code with me!
- It's really hard to make valid comparisons against data I don't have access to. Do you have any open data sets to try this comparison against?
- It looks like the meat of the work is being done here by scikit-learn. As was mentioned earlier, Numba at this stage is mostly useful for improving kernel performance, not large library routines.
I'm planning on taking a deeper look into some of the scikit-learn kernels in the future. Keep an eye open for a blog post from Continuum on this.
Yeah I use openCV and scikit learn to do the heavy lifting indeed. But then if I used Numba surely that is what you are advocating too??
I tried unrolling the inner kernel a few different ways before settling on the code you have before you. It doesn't compute the feature vector exactly how I would want, BUT ITS really FAST, which for sonar analysis to run on AUV in
real time that's essential. I will fit my math to the library to meet my CPU budgets. Anyway I hope you can use this as a benchmark or something even if its implemented in a totally different way than you might do.
feel free to email me tom dot larkworthy <at> gmail
oh I should also add that the clustering (kmeans) is very fast. The analogous part to the inner loop is "feature_vectors". Which has 2 main cases:
case 1, in the case nothing has been computed it calculates the corner response images at all the different spatial scales (big operations).
case 2, feature_vector just selects data from the corner images for the pixels demanded.
Now my algorithm is sparse so its normally jsut selecting a subset of pixels, although as it calculated the spatial responses of the whole image it doesn't really make any odds to my runtime.
It is a bit unfortunate that you have to "unroll" your code to get the most out of Numba. Hopefully at some point it will be able to translate NumPy functions such as np.abs(X - y).sum(axis=1)) efficiently into LLVM. Those extreme performance improvements are misleading though; in my experience the speed up compared to concise, optimal NumPy code is more in the range 50-100%.
I'm sure numba is an impressive product. But I keep seeing examples on the continuum blog with an obscene number of for loops, and correspondingly great speedups.
My only response is: "My code doesn't look like that."
I'd be more impressed if they didn't cherry pick code with a dozen for-loops, and instead showed a moderate speedup from more standard (and vectorized) code.
In general, we're looking for results that will get people excited, and in some communities, we can get fairly silly speedups over native Python, so we are still picking that low-hanging fruit.
However, it's also important to be tied in to real applications. I'd be happy to take a crack at applying numba to your code. Do you have a reasonably small example we could take a look at?
The Ultrafast Shape Recognition (USR) algorithm is a very simple yet interesting application used in drug discovery that I tried speeding up with Numba (the similarity calculation part). The NumPy implementation looks roughly like this:
Where X.shape could be (2000000, 12) (or more rows) and y.shape (12,). The idea is to retrieve the top N most similar hits above a similarity score of S.
The numba code isn't as pretty as it could be because slicing doesn't work for overlapping memory regions or wraparound indexing yet, and we don't have inlining :(
Here's what I get on a 2.6 GhZ Intel Core i7.
I rewrote your code to minimize memory traffic, then jitted it with numba:
def usr_numba(x, y, S, num_best):
m, n = x.shape
best = np.zeros(num_best)
best_low = 0.0
for i in xrange(m):
d = abs(x[i,0]-y[0])
for j in xrange(1,n):
d += abs(x[i,j] - y[j])
d = 1.0 / (1.0 + 1/12.0 *d)
if d > best_low and d > S:
k = 0
for k in xrange(0,num_best):
if d > best[k]:
break
for l in xrange(num_best-1, k, -1):
best[l] = best[l-1]
best[k] = d
best_low = best[num_best-1]
return best
_usr = autojit()(usr_numba)
In [1]: import numba_usr
jitted kernel checks out
N = 1000000
usr (s): 0.233645
numba (s): 0.0115586
20X speedup
N = 2000000
usr (s): 0.566954
numba (s): 0.023487
24X speedup
N = 4000000
usr (s): 1.14992
numba (s): 0.0472016
24X speedup
N = 8000000
usr (s): 2.34968
numba (s): 0.092601
25X speedup
N = 10000000
usr (s): 2.96395
numba (s): 0.116032
26X speedup
N = 20000000
usr (s): 17.4779
numba (s): 0.236304
74X speedup
For the case aschreyer is interested in, I see a 24x speedup from half a second to two hundredths of a second. For a really big problem (2 x 10^7), numba is still well under a second and the numpy code is starting to really suffer.
Thanks, that was a good example indeed! Improving the memory traffic was actually really important because the real-life application has N=20M+, up to 200M. Around 85M calculations per seconds is pretty impressive I have to say and the example really helps to understand how to write efficient Numba code.
Numba's original purpose was not to speed up concise, optimal NumPy code. The fact that it can actually do that is an extra bonus. Numba's purpose was and still remains best at allowing you to still write the "hard-to vectorize" algorithms that require for-loops without having to use C/C++ or Fortran.
If you never are resorting to using C/C++ or Fortran, then Numba is not necessarily going to be helpful for you.
I don't think we are trying to mislead. We are simply showing examples that illustrate what Numba can be used for. Of course your code does not have many for-loops in it because you never could write code like that and have it be fast in Python. The big-deal about Numba is that now you can. You don't have to rely on a C/C++ library that either write or get someone else to write for you. You can just use Python.
I agree with you about the "unrolling". I don't recommend adding for-loops when good vectorized expressions work. Keep using them as we will be able to do a better job of parallelizing that code in the future. However, when you can't vectorize and for-loops are all you have, Numba is great.
We have spent some time on optimizing NumPy expressions like you describe and some array expressions can be translated down to equivalent LLVM loops. But most of the effort in that direction has been happening in the Blaze project which is generalizing NumPy so it can deal with much larger data but still produce optimized code from those vector expressions. This actually requires a re-thinking of how NumPy is implemented from the ground up.
It's not common to see C/Fortran comparisons because they can be a pain to write. I'm implementing a serial C version for next week's blog post, though.
Note that the array-expressions previously in numbapro only have been moved to numba (our plan for all premium products is to move features into open source as we get funds to support that).
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[ 3.1 ms ] story [ 74.5 ms ] threadHate to have to tell all my Cython code it needs to break itself because the hackernews thinks so. =P
OpenCV is great as a black box but its built in functions are not always sufficient or optimal.
EDIT2: deleted as the src has been truncated, http://pastebin.com/dPMsRF78
- It's really hard to make valid comparisons against data I don't have access to. Do you have any open data sets to try this comparison against?
- It looks like the meat of the work is being done here by scikit-learn. As was mentioned earlier, Numba at this stage is mostly useful for improving kernel performance, not large library routines.
I'm planning on taking a deeper look into some of the scikit-learn kernels in the future. Keep an eye open for a blog post from Continuum on this.
Yeah I use openCV and scikit learn to do the heavy lifting indeed. But then if I used Numba surely that is what you are advocating too?? I tried unrolling the inner kernel a few different ways before settling on the code you have before you. It doesn't compute the feature vector exactly how I would want, BUT ITS really FAST, which for sonar analysis to run on AUV in real time that's essential. I will fit my math to the library to meet my CPU budgets. Anyway I hope you can use this as a benchmark or something even if its implemented in a totally different way than you might do.
feel free to email me tom dot larkworthy <at> gmail
Tom
Now my algorithm is sparse so its normally jsut selecting a subset of pixels, although as it calculated the spatial responses of the whole image it doesn't really make any odds to my runtime.
My only response is: "My code doesn't look like that."
I'd be more impressed if they didn't cherry pick code with a dozen for-loops, and instead showed a moderate speedup from more standard (and vectorized) code.
In general, we're looking for results that will get people excited, and in some communities, we can get fairly silly speedups over native Python, so we are still picking that low-hanging fruit.
However, it's also important to be tied in to real applications. I'd be happy to take a crack at applying numba to your code. Do you have a reasonably small example we could take a look at?
The numba code isn't as pretty as it could be because slicing doesn't work for overlapping memory regions or wraparound indexing yet, and we don't have inlining :(
Here's what I get on a 2.6 GhZ Intel Core i7.
I rewrote your code to minimize memory traffic, then jitted it with numba:
For the case aschreyer is interested in, I see a 24x speedup from half a second to two hundredths of a second. For a really big problem (2 x 10^7), numba is still well under a second and the numpy code is starting to really suffer.My full code is here: https://gist.github.com/ahmadia/5550933
I'm putting it into a wakari notebook so you can actually check me on this :)
Edit 1 - Made the speedup a little more comprehensible (and fixed gist)
If you never are resorting to using C/C++ or Fortran, then Numba is not necessarily going to be helpful for you.
I don't think we are trying to mislead. We are simply showing examples that illustrate what Numba can be used for. Of course your code does not have many for-loops in it because you never could write code like that and have it be fast in Python. The big-deal about Numba is that now you can. You don't have to rely on a C/C++ library that either write or get someone else to write for you. You can just use Python.
I agree with you about the "unrolling". I don't recommend adding for-loops when good vectorized expressions work. Keep using them as we will be able to do a better job of parallelizing that code in the future. However, when you can't vectorize and for-loops are all you have, Numba is great.
We have spent some time on optimizing NumPy expressions like you describe and some array expressions can be translated down to equivalent LLVM loops. But most of the effort in that direction has been happening in the Blaze project which is generalizing NumPy so it can deal with much larger data but still produce optimized code from those vector expressions. This actually requires a re-thinking of how NumPy is implemented from the ground up.
Note that the array-expressions previously in numbapro only have been moved to numba (our plan for all premium products is to move features into open source as we get funds to support that).