Hi Fred,
I'm talking about the GPU. With a 10000x1000 matrix X, X.sum(axis=1) is 10
times slower than X + Y, where Y is another matrix of the same shape,
according to my tests.
I suspect that you're reducing each row using some O(logn) algorithm which
makes sense when one needs to reduce a single long vector. But in this
case, shouldn't we assign each row to a single core of the GPU and reduce
the row as we would do on the CPU? The parallelism would result from having
so many rows.
Of course, if the matrix had just 10 rows this algorithm would be very
slow, but with 10000 rows it should be faster than what you're doing right
now. It might be almost as fast as doing X + Y.
I'm speculating since I've never looked into CUDA programming (it's on my
TODO list!).
On Tuesday, February 7, 2017 at 10:49:47 PM UTC+1, nouiz wrote:
>
> Hi,
>
> for the gpu, int are only supported in the new gpu back-end
> (device=cuda*). In the old back-end, they would end up being on CPU. This
> is why at many places it is told to not use int on the GPU. But it isn't
> true with the new back-end.
>
> For the reduction being slow, we didn't parallelize it on the CPU. It
> wasn't a bottleneck on the CPU and we don't have much time to optimize the
> CPU. So I would recommand to time your real model on the CPU before
> spending much time thinking about the parallel reduction on CPU as it is
> probably not a problem.
>
>
> Fred
>
> On Mon, Feb 6, 2017 at 8:11 PM Kiuhnm Mnhuik <kiuhn...@gmail.com
> <javascript:>> wrote:
>
>> 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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>>
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