using odlcuda about odlcuda HOT 12 OPEN

If I run ccmake, then I only have ODL_CUDA_COMPUTE. Is that what you mean?
With this changed to 35 (omg is my GPU so old???) the above example works well! Many thanks!

from odlcuda.

Here is now my example with timings. I hope this motivates you and shows that you are doing an incredible job!

domain_cpu = odl.uniform_discr([0, 0, 0], [1, 1, 1], [4000, 250, 350], impl='numpy')
domain_gpu = odl.uniform_discr([0, 0, 0], [1, 1, 1], [4000, 250, 350], impl='cuda')

x_cpu = np.e * domain_cpu.one()
x_gpu = np.e * domain_gpu.one()

% time y = x_cpu.ufuncs.log()
% time y = x_gpu.ufuncs.log()

CPU times: user 7.28 s, sys: 340 ms, total: 7.62 s
Wall time: 7.63 s
CPU times: user 1.36 ms, sys: 338 µs, total: 1.7 ms
Wall time: 1.64 ms

It looks I can finally run my code on the real PET data :D

from odlcuda.

The order matters. I first tried ...

Very interesting observation. With that said, you should as a user never explicitly import odlcuda.

domain_cpu = odl.uniform_discr([0], [1], [3e+8], impl='numpy')

This is not supposed to work, and the error message is quite explicit on why. 3e+8 is a floating point number and we require shape to be a integer, we're also cautious on casting since it opens up errors. We've had problems with people using shape = 1.5 or something like that, which is then cast to shape = 1, causing confusion.

The solution is to simply cast it to an integer yourself domain_cpu = odl.uniform_discr([0], [1], [int(3e+8)], impl='numpy'), or use powers domain_cpu = odl.uniform_discr([0], [1], [3 * 10 ** 8], impl='numpy')

... cudaFuncGetAttributes ...

This is actually covered in the readme, and the recommended solution is to change CUDA_COMPUTE to a version supported by your GPU. I need to know what GPU you have to fix that, or you can look it up yourself.

from odlcuda.

To then use CUDA for everything I do, I need to rewrite some functionals that I wrote in ODL. This causes me some trouble.
My strategy was to replace all numpy functionality with ufuncs, i.e. np.log(x) = x.ufunc.log(). Is this a good strategy? But when it comes to indices, I am struggling a little:

import odl
import numpy as np
domain_gpu = odl.uniform_discr([0, 0, 0], [1, 1, 1], [4000, 250, 350], dtype='float32', impl='cuda')
x_gpu = np.e * domain_gpu.one()
i_gpu = x_gpu.ufuncs.greater(0)
x_gpu[i_gpu]

results in

Traceback (most recent call last):

  File "<ipython-input-2-fd24fbf6952c>", line 4, in <module>
    x_gpu[i_gpu]

  File "/mhome/damtp/s/me404/store/repositories/git_ODL/odl/discr/discretization.py", line 314, in __getitem__
    return self.ntuple.__getitem__(indices)

  File "/home/me404/.local/lib/python2.7/site-packages/odlcuda-0.5.0-py2.7.egg/odlcuda/cu_ntuples.py", line 419, in __getitem__
    return self.data.__getitem__(indices)

ArgumentError: Python argument types in
    CudaVectorFloat32.__getitem__(CudaVectorFloat32, DiscreteLpElement)
did not match C++ signature:
    __getitem__(CudaVectorImpl<float> {lvalue}, long)

I kind of see what I am doing wrong, but not how to resolve this. Any ideas?

from odlcuda.

Basically the problem here is that I've not implemented comparison between vectors and longs in odlcuda. The workaround for now is to compare to the zero vector, but if this is a performance hog for you i could get it fixed.

from odlcuda.

I thought the problem here is the indexing, as I am indexing with an odl element and not with a "long". If I do your proposed fix, nothing changes.

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Also in the numpy case I am not sure what kind of indexing is necessary and what is not. Does

i = np.int32(data.ufuncs.greater(0).asarray().ravel())
log_data = data[i].ufuncs.log()

make any sense to you?

from odlcuda.

Now I see what you are aiming at here. Now that would be complicated (mostly given that we don't support advanced indexing). I'd probably try some smooth approximation of the log function if I was you. You could also do something like:

pos = data.ufuncs.greater(epsilon * data.space.one())
log_data = (data * pos).ufunc.log()

Another option would be for you to manually add whatever function you have as raw cuda, modifying odlcuda shouldn't be too complicated.

With that said, the primary solution here is frankly to wait for holger to finalize the tensor branch and we'll get a really good backend for this stuff.

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@adler-j, am I correct in assuming that @kohr-h's tensor branch isn't in yet and that the above "problem" still exists?

I tried to look into odlcuda a little but could not get my head around it. Is it possible to just get a pointer to the data on the gpu device so that one could use any gpu code without needing to understand your structures in odlcuda?

from odlcuda.

I think I found the answer to my question in cu_ntuples.py. Now I start to understand how odl is actually implemented :).

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I would like to consult something(Please excuse me for not being good at English) , after I installed according to the official document:(https://odlgroup.github.io/odl/getting_started/installing_extensions.html) , the CPU version can work well, but CUDA can not, please help to analyze it, the installation process is as follows:
git clone https://github.com/odlgroup/odlcuda.git
cd odlcuda
conda install conda-build
git checkout conda-build
conda build ./conda CUDA_ROOT=/usr/lss/cudatoolkit-10.1.243-h74a9793_0 CUDA_COMPUTE=60
conda install --use-local odlcuda
python -c "import odl; odl.rn(3, impl='cuda').element()"

from odlcuda.

@wangshuaikun did you install it?

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