PyOpenCL April 2014

pyopencl@tiker.net

10 participants
17 discussions

Invalid command Queue when using big data sets on nVidia

by Justin Heinermann

Dear all, we are trying to implement a K nearest neighbor search on GPUs with PyOpenCL. The goal of the algorithm is: For a given target point, find the nearest points from a given set (training data). The distance between two points is computed by the squared euclidean distance. One of our implementations is a brute force approach, which aims at processing big data sets in parallel, e.g. 1 million training data and some millions of targets (test data). For every target point one kernel instance is created which finds the k nearest points out of the training points. Our problem is the following. Everything works fine for small data sets and the results are as expected on both GPU (GeForce GTX 650 with nVidia Driver 313.09.) and CPU(Intel Core i5-3450 with AMD APP SDK) running Ubuntu 12.10, PyOpenCL 2013.1-py2.7-linux-x86_64. But if we increase the size of the data sets, the GPU version crashes with the following error: > File "brutegpu.py", line 65, in query > cl.enqueue_copy(self.queue, d_min, self.d_min_buf).wait() > File "/usr/local/lib/python2.7/dist-packages/ > pyopencl-2013.1-py2.7-linux-x86_64.egg/pyopencl/__init__.py", > line 935, in enqueue_copy > return _cl._enqueue_read_buffer(queue, src, dest, **kwargs) > pyopencl.LogicError: clEnqueueReadBuffer failed: invalid command queue The CPU-Version still works fine with 1 million training points and 1 million of test points. Attached you can find the corresponding source code as working minimal example, which consists of on Host-Python-File and one OpenCL-Kernel-File. We would highly apprecriate any help - maybe we made a mistake which is already known to you. So the big question for us is: Why is it working on CPU and why isn't it working on the GPU? Are there nVidia-specific pitfalls for such big data sets? The compiler says: > ptxas info : Compiling entry function 'find_knn' for 'sm_30' > ptxas info : Function properties for find_knn > 0 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads > ptxas info : Used 17 registers, 336 bytes cmem[0], 4 bytes cmem[3] Or are there any rules for using a kernel for big data sets such as setting the work group sizes or maximum memory usage? The error message "invalid command queue" is confusing and I wasn't able to find any helpful information (except that oftentimes "invalid command queue" means segfault, but i could not find any wrong array adress yet.) Maybe one of you could have a look at our code and finds some stupid mistake. We would be very grateful for every hint. Best regards, Justin Heinermann, University Oldenburg

6 years, 3 months

Very specific bug: round(float) fails on Ti780 with python only

by Kai Ruhl

I have a *very* specific bug in pyopencl: When I use round(88.9f) with pyopencl from git (2014-04-08) on a Nvidia Ti780, it will give me 88.0 instead of 89.0. - If I change the gfx card to my older GTX590, it will work. - If I round doubles instead of floats, it works on both cards. - If I write a test in C++, it works on both cards. Can anyone with a Ti780 confirm this bug? Side info: I have compiled pyopencl with ENABLE_GL=True and CL_PRETEND_VERSION="1.1" due to missing clCreateSubDevices in nvidia OpenCL. OS is Ubuntu 14.04. My test is: import pyopencl as cl import numpy as np ctx = cl.create_some_context() que = cl.CommandQueue(ctx) cl_prg = cl.Program(ctx, "__kernel void doit(__global float *a) { a[0] = round(88.9f); }").build() a = np.zeros(1, dtype=np.float32); A = cl.Buffer(ctx, cl.mem_flags.READ_WRITE | cl.mem_flags.COPY_HOST_PTR, hostbuf=a) cl_prg.doit(que, [1], None, A); que.finish() cl.enqueue_copy(que, a, A) print a[0]

6 years, 6 months

Re: [PyOpenCL] pip install pyopencl on OSX, with gl interop

by Andreas Kloeckner

"Keith R. Brafford" <keith.brafford(a)gmail.com> writes: > Wait...it was my fault! I forgot I had commented out the context > acquisition line and replaced it with my own before I realized I had a > wrongly compiled pyopencl: > > #context = cl.Context(properties=[(cl.context_properties.PLATFORM, > platform)] + get_gl_sharing_context_properties()) > context = cl.Context(properties=[(cl.context_properties.PLATFORM, > platform)] ) > > I returned it to its correct state and it works fine now. > > Any idea why the pip thing doesn't do it for me though? Hmm, I did have a typo in the file name. It should be ".aksetup-defaults.py". Andreas

6 years, 7 months

Re: [PyOpenCL] pip install pyopencl on OSX, with gl interop

by Keith R. Brafford

Wait...it was my fault! I forgot I had commented out the context acquisition line and replaced it with my own before I realized I had a wrongly compiled pyopencl: #context = cl.Context(properties=[(cl.context_properties.PLATFORM, platform)] + get_gl_sharing_context_properties()) context = cl.Context(properties=[(cl.context_properties.PLATFORM, platform)] ) I returned it to its correct state and it works fine now. Any idea why the pip thing doesn't do it for me though? --Keith Brafford On Tue, Apr 29, 2014 at 10:24 PM, Keith R. Brafford < keith.brafford(a)gmail.com> wrote: > I tried that, and no luck: > > >>> pyopencl.have_gl() > > False > > Then I did: > > pip uninstall pyopencl > > followed by: > > git clone http://git.tiker.net/trees/pyopencl.git > > cd pyopencl > > git submodule init > > git submodule update > > python configure.py --cl-enable-gl > > python setup.py build > > make > > python setup.py install > > And now I get this: > > >>> pyopencl.have_gl() > > True > > > So...Yay! I can run gl_interop_demo.py and it works well, but when I try > to run the particle demo I get a "Segfault 11" error. > > Segmentation fault: 11 > > with a "Python quit unexpectedly" message box popping up. > > Any ideas? > > --Keith Brafford > > > On Tue, Apr 29, 2014 at 10:00 PM, Andreas Kloeckner < > lists(a)informa.tiker.net> wrote: > >> "Keith R. Brafford" <keith.brafford(a)gmail.com> writes: >> >> > I am trying to use pip to install pyopencl on OSX, but I can't get >> > pyopencl.have_gl() to return True. >> > >> > How can I tell pip to compile the module such that I can get gl >> > interoperability? >> >> Create a $HOME/.aksetup-defualts.py with one line: >> >> CL_HAVE_GL = True >> >> Then run the pip install. That should do it. If not, come back and yell >> at me. >> >> Andreas >> > >

6 years, 7 months

Re: [PyOpenCL] pip install pyopencl on OSX, with gl interop

by Andreas Kloeckner

"Keith R. Brafford" <keith.brafford(a)gmail.com> writes: > I am trying to use pip to install pyopencl on OSX, but I can't get > pyopencl.have_gl() to return True. > > How can I tell pip to compile the module such that I can get gl > interoperability? Create a $HOME/.aksetup-defualts.py with one line: CL_HAVE_GL = True Then run the pip install. That should do it. If not, come back and yell at me. Andreas

6 years, 7 months

pip install pyopencl on OSX, with gl interop

by Keith R. Brafford

I am trying to use pip to install pyopencl on OSX, but I can't get pyopencl.have_gl() to return True. How can I tell pip to compile the module such that I can get gl interoperability? Thanks, --Keith Brafford

6 years, 7 months

IPython integration

by Andreas Kloeckner

Hi all, IPython integration for PyOpenCL has just landed in git. Documentation and an expample here: http://documen.tician.de/pyopencl/misc.html#ipython-integration The idea is that you can define CL kernels in a special type of cell in the IPython notebook, and when you evaluate that, you automatically get all the kernels in your cell as objects to play with in your IPython notebook. Got comments? suggestions? Don't hold back! Andreas

6 years, 7 months

Compute large integers with the kernel and python

by Cellier

Hello, I'm knew to PyOpenCL After searching for a while, I couldn't find some bignum implementation for OpenCL in C. But fortunately, I'm more experienced with python where the only limit for the size of numbers is the whole RAM. How I may use PyOpenCL to get CPUs intensive computations done on the GPU by OpenCL? regards.

6 years, 7 months

Compute large integers with the kernel and python

by Cellier

On 27/04/2014 19:02, William Shipman wrote: > I'm not aware of any arbitrary precision arithmetic libraries either, and I > do wonder if they would be worthwhile on a GPU. But there are alternatives. > > You can use double-double (128 bit) and quad-double (256 bit) precision. > Every number gets represented using two or four double precision numbers, > roughly doubling (or quadrupling) the number of accurate digits. Every > operation then takes these two (four) components for every number. You > should have a look at http://crd-legacy.lbl.gov/~dhbailey/mpdist/ for C++ > and Fortran implementations of double-double and quad-double precision > arithmetic. Of course, you will have to turn this into OpenCL code. > Please check the license documentation that is posted at this link to make > sure that the license is compatible with your intended uses. > > > > On 27 April 2014 19:00, Cellier <lcellier(a)lycee-joliverie.fr> wrote: > >> I don't need the basic operations (such as logab + - ) to be done in >> parallel. I just want to perform operations on them. It doesn't matter if >> it is done serially. >> I need to parallelize things at an higher level : see >> https://fr.wikipedia.org/wiki/Wikipédia:Oracle/semaine_17_ >> 2014#Math.C3.A9matiques_:_Trouver_des_nombres_premiers_ >> qui_loge_sur_512_Ko_en_binaire. (sorry for the language). >> >> >> _______________________________________________ >> PyOpenCL mailing list >> PyOpenCL(a)tiker.net >> http://lists.tiker.net/listinfo/pyopencl >> > 256 bit is far from 512288 bits. I expect performance by doing thousands miller-rabin test in parallel, so it doesn't matter if that's done serially. I'm also asking how I can use python integers (which don't are only limited by the size of the RAM) within the kernel, with PyOpenCL.

6 years, 7 months

Compute large integers with the kernel and python

by lcellier

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6 years, 7 months

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PyOpenCL April 2014