Reductions are quite slow. Without the final reduction I get a 100x speed
up.
Why is Y.sum(axis=1) so slow? I think that if each core handled a single
row it'd be 10 times faster for matrices with many rows like in this case.
Theano is probably using an O(logn) algorithm which is only useful when one
needs to reduce a single but long vector.
Can you confirm?
On Tuesday, February 7, 2017 at 12:37:02 AM UTC+1, Kiuhnm Mnhuik wrote:
>
> I tried the following code:
>
> def test_speed():
> print('Computing X and X2...', end='', flush=True)
> X_np = np.random.uniform(0, 100, size=(10000, 1000)).astype(floatX)
> X2_np = np.random.uniform(0, 100, size=(10000,
> 1000)).astype(floatX)
> print('done!', flush=True)
>
> print('Moving X and X2 to the GPU...', end='', flush=True)
> X = theano.shared(X_np)
> X2 = theano.shared(X2_np)
> print('done!', flush=True)
>
> print('Building the graph...', end='', flush=True)
> Y = X
> for _ in range(100):
> # Y = Y * (Y <= X2)
> Y = Y * (Y - X2)
> Y.sum(axis=1)
> print('done!', flush=True)
>
> print('compiling...', end='', flush=True)
> f = theano.function([], Y)
> print('done!', flush=True)
>
> import time
> t = time.clock()
> f()
> print(time.clock() - t)
>
> Note that there is a line with '<=' and another with '-' in the loop.
> They're exclusive. Here are the timings in seconds:
>
> CPU GPU
> '-' 0.21 0.016
> <= 0.39 0.019
>
> I'd say I don't need to worry about using comparisons.
>
> On Monday, February 6, 2017 at 1:20:13 PM UTC+1, Kiuhnm Mnhuik wrote:
>>
>> I'm using Theano 0.9.0b1 with the new back-end.
>> Should I use float32 for everything (even for bool masks) for maximum
>> speed on GPU (GTX 970)?
>>
>
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