PyOpenCL April 2013

pyopencl@tiker.net

9 participants
19 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, 11 months

Segmentation fault in pyopencl.image_from_array

by Jerome Kieffer

Dear Python/OpenCL community, I am pretty new (py)opencl and encountered a problem, maybe it a lack of understanding of openCL, but I found strange python seg-faults: test program: #!/usr/bin/python import numpy, pyopencl ctx = pyopencl.create_some_context() data=numpy.random.random((1024,1024)).astype(numpy.float32) img = pyopencl.image_from_array(ctx, ary=data, mode="r", norm_int=False, num_channels=1) print img System: debian sid: pyopencl2012.1 (the same code works with debian stable and v2011.2) Here is the backtrace obtained with GDB: 0x0000000000000000 in ?? () (gdb) bt #0 0x0000000000000000 in ?? () #1 0x00007ffff340c253 in pyopencl::create_image_from_desc(pyopencl::context const&, unsigned long, _cl_image_format const&, _cl_image_desc&, boost::python::api::object) () from /usr/lib/python2.7/dist-packages/pyopencl/_cl.so #2 0x00007ffff342de36 in _object* boost::python::detail::invoke<boost::python::detail::install_holder<pyopencl::image*>, pyopencl::image* (*)(pyopencl::context const&, unsigned long, _cl_image_format const&, _cl_image_desc&, boost::python::api::object), boost::python::arg_from_python<pyopencl::context const&>, boost::python::arg_from_python<unsigned long>, boost::python::arg_from_python<_cl_image_format const&>, boost::python::arg_from_python<_cl_image_desc&>, boost::python::arg_from_python<boost::python::api::object> >(boost::python::detail::invoke_tag_<false, false>, boost::python::detail::install_holder<pyopencl::image*> const&, pyopencl::image* (*&)(pyopencl::context const&, unsigned long, _cl_image_format const&, _cl_image_desc&, boost::python::api::object), boost::python::arg_from_python<pyopencl::context const&>&, boost::python::arg_from_python<unsigned long>&, boost::python::arg_from_python<_cl_image_format const&>&, boost::python::arg_from_python<_cl_image_desc&>&, boost::python::arg_from_python<boost::python::api::object>&) () from /usr/lib/python2.7/dist-packages/pyopencl/_cl.so #3 0x00007ffff342e06f in boost::python::detail::caller_arity<5u>::impl<pyopencl::image* (*)(pyopencl::context const&, unsigned long, _cl_image_format const&, _cl_image_desc&, boost::python::api::object), boost::python::detail::constructor_policy<boost::python::default_call_policies>, boost::mpl::vector6<pyopencl::image*, pyopencl::context const&, unsigned long, _cl_image_format const&, _cl_image_desc&, boost::python::api::object> >::operator()(_object*, _object*) () from /usr/lib/python2.7/dist-packages/pyopencl/_cl.so #4 0x00007ffff311715b in boost::python::objects::function::call(_object*, _object*) const () from /usr/lib/libboost_python-py27.so.1.49.0 #5 0x00007ffff3117378 in ?? () from /usr/lib/libboost_python-py27.so.1.49.0 #6 0x00007ffff3120593 in boost::python::detail::exception_handler::operator()(boost::function0<void> const&) const () from /usr/lib/libboost_python-py27.so.1.49.0 #7 0x00007ffff3445983 in boost::detail::function::function_obj_invoker2<boost::_bi::bind_t<bool, boost::python::detail::translate_exception<pyopencl::error, void (*)(pyopencl::error const&)>, boost::_bi::list3<boost::arg<1>, boost::arg<2>, boost::_bi::value<void (*)(pyopencl::error const&)> > >, bool, boost::python::detail::exception_handler const&, boost::function0<void> const&>::invoke(boost::detail::function::function_buffer&, boost::python::detail::exception_handler const&, boost::function0<void> const&) () from /usr/lib/python2.7/dist-packages/pyopencl/_cl.so #8 0x00007ffff3120373 in boost::python::handle_exception_impl(boost::function0<void>) () from /usr/lib/libboost_python-py27.so.1.49.0 #9 0x00007ffff3115635 in ?? () from /usr/lib/libboost_python-py27.so.1.49.0 Thanks for your help. If you are not able to reproduce this bug, I should mention it to debian. Cheers, -- Jérôme Kieffer Data analysis unit - ESRF

7 years, 5 months

create Event from cl_event

by Gregor Thalhammer

Dear Andreas, I am currently working on a cython based wrapper for the OpenCL FFT library from AMD: https://github.com/geggo/gpyfft For this I need to create a pyopencl Event instance from a cl_event returned by the library. I attached a patch against recent pyopencl that adds this possibility, similar to the from_cl_mem_as_int() method of the MemoryObject class. Could you please add this to pyopencl. Thanks for your help Gregor

8 years

New version and prefix scan problems

by Tomasz Rybak

Sorry if there are two copies of this message. I have sent it to the list but received no confirmation (nor any error) and checked that archive does not show any message from January. I can see that there is already new version (2013.1) in docs, marked "in development". I would like for it not to be released before fixing problems with parallel prefix scan. Problems with scan are only visible on APU Loveland. They do not occur on ION, nor on GTX 460. I do not have access to machine with NVIDIA CC 3.x so I cannot test prefix scan there. I first encountered it in August, and mentioned them in email to the list from 2012-08-08 ("Python3 test failures"). Only recently I had some time and eagerness to look closer into them. Tests still fail on recent git version c31944d1e81a. Failing tests are now in test_algorithm.py, in third group (marked scan-related, starting in line 418). I'll describe my observations of test_scan function. My APU has 2 Computing Units. GenericScanKernel chooses k_group_size to be 4096, max_scan_wg_size to be 256, and max_intervals to 6. The first error occurs when there is enough work to fill two Computing Units - in my case 2**12+5. It looks like there is problem with passing partial result from computations occurring on fist CU to the second one. Prefix sum is computed correctly on the second half of the array but starting with the wrong point. I have printed interval_results array and I have observed that error (difference between the correct value of the interval's first element and actual one) is not the value of any of the elements of interval_results, nor it is difference between interval_results elements. On the other hand difference between real and wanted value is similar (i.e. in the same range) to the difference between interval_results[4] and interval_results[3]. In the test I have run just now the error is 10724571 and the difference is 10719275; I am not sure if this is relevant though. Errors are not repeatable - sometimes they occur for small arrays (e.g. for 2**12+5) sometimes for larger ones (test I have run right now failed for ExclusiveScan of size 2**24+5). The tests' failures also depend on order of tests - after changing order of elements of array scan_test_counts I got failures for different sizes, but always for sizes larger than 2**12. It might be some race condition, but I do not understand new scan fully and cannot point my finger at one place. If there is any additional test I can perform please let me know. I'll try to investigate it further but I am not sure whether it'll work. Best regards. -- Tomasz Rybak GPG/PGP key ID: 2AD5 9860 Fingerprint A481 824E 7DD3 9C0E C40A 488E C654 FB33 2AD5 9860 http://member.acm.org/~tomaszrybak

8 years, 3 months

Re: [PyOpenCL] New version and prefix scan problems

by Andreas Kloeckner

Hi Jonny, Jonathan Hunt <hunt(a)braincorporation.com> writes: > I'd be happy to test for you on Apple (10.8.3 with NVIDIA GeForce GT 650M > 1024 MB / i7). However, I'm not quite sure how to run the tests (I did > look, but I missed any existing documentation). > > I install the latest pyopencl in a virtualenv but running test_algorithm.py > spits out: > "platform darwin -- Python 2.7.2 -- pytest-2.3.4 > collected 46 items > > test_algorithm.py . > " > and then appears to hang for quite some time (with no CPU load). It seems > be the same for the other test files. > > Pyopencl works fine for me in normal operation on this machine so I'm > probably doing something wrong. Thanks for offering. I've known that something fishy is going on with PyOpenCL's tests on OS X for a while, but since I don't have access to a Mac, I've never been able to debug it. I'll try to get access (hopefully lasting enough to integrate into my continuous integration setup) and figure out what's going on. Nonetheless, I think (hope?) that issue is separate from the AMD issue I mentioned in my original email... Andreas

8 years, 3 months

OpenCL e

by Andreas Kloeckner

Hi all, I was wondering something about OpenCL's execution model. Here's a quote from AMD's documentation [1], page 132: Execution of kernel dispatches can overlap if there are no dependencies between them and if there are resources available in the GPU. This is critical when writing benchmarks it is important that the measurements are accurate and that “false dependencies” do not cause unnecessary slowdowns. An example of false dependency is: a. Application creates a kernel “foo”. b. Application creates input and output buffers. c. Application binds input and output buffers to kernel “foo”. d. Application repeatedly dispatches “foo” with the same parameters. If the output data is the same each time, then this is a false dependency because there is no reason to stall concurrent execution of dispatches. To avoid stalls, use multiple output buffers. The number of buffers required to get peak performance depends on the kernel. Now, I thought OpenCL would only look at events passed to wait_for when determining what kernel is allowed to run concurrently with what other kernel. This sounds like some dependency information is also conveyed by what mem objects are used, especially that two kernels aren't allowed to write to the same one at the same time. Is that AMD-specific, or is that part of the spec? I'd be grateful for any clues. Thanks! Andreas [1] http://developer.amd.com/download/AMD_Accelerated_Parallel_Processing_OpenC…

8 years, 3 months

PyOpenCL Python version support: drop 2.4 and 2.5?

by Andreas Kloeckner

Hi all, I'd like a show of hands of the number of people who are using or have somewhat recently used PyOpenCL on Python 2.4 or 2.5. I'm not dropping support for these versions just yet, but might at some point. For now, I'm just looking for data. Thanks! Andreas

8 years, 3 months

Scan vs wait_for

by Andreas Kloeckner

Hi all, if you've used any of the things in pyopencl.algorithm (from git, has not yet been in a release), here's an important heads-up: I've made an incompatible change to these interfaces to make sure they support wait_for arguments and return events. In some cases, this means that result tuples have a different length now. (Yes, those should be namedtuple()s, I know. Watch for a related post.) If this affects you, I'm sorry. Andreas

8 years, 3 months

Re: [PyOpenCL] PyOpenCl with Nvidia graphic card

by Tomasz Rybak

That's strange. I attach full list of packages installed on my machine, running OpenCL on NVIDIA GTX 460. Some packages are for CUDA (I've installed CUDA SDK packages from experimental), some for AMD OpenCL (run on CPU). I know it is possible to run OpenCL (and PyOpenCL) on Debian (with contrib and non-free enabled) without needing to install non-packaged software. To have working OpenCL you need: 1. ICD management library - it loads OpenCL implementations. I recommend ocl-icd-libopencl1 which is open source and works without problems with various OpenCL implementations. I had some problems with NVIDIA ICD loader. 2. OpenCL implementation - for you it is nvidia-opencl-icd Its version depends on installed driver, so if you are using driver from experimental you should install nvidia-opencl-icd from experimental, and so on. 3. As you are writing on this list - PyOpenCL ;-) You can try to install packaged pyopencl (python-pyopencl or python3-pyopencl, depending on your needs). It should write about any missing dependencies. My advice - install packaged software. Mixing .debs with official NVIDIA drivers can cause problems. I know that Debian contains not-so-recent PyOpenCL, but we are in freeze now so no new versions of packages are accepted. Regarding lack of symbols - I also do not have any symbols in libnvidia-opencl.so. As for AMD: $ nm /usr/lib/x86_64-linux-gnu/libamdocl64.so nm: /usr/lib/x86_64-linux-gnu/libamdocl64.so: no symbols and clinfo shows both platforms. Best regards. -- Tomasz Rybak GPG/PGP key ID: 2AD5 9860 Fingerprint A481 824E 7DD3 9C0E C40A 488E C654 FB33 2AD5 9860 http://member.acm.org/~tomaszrybak

8 years, 3 months

Re: [PyOpenCL] PyOpenCl with Nvidia graphic card

by Andreas Kloeckner

Hi James, James Bergstra <james.bergstra(a)gmail.com> writes: > I've come across a similar issue I think: I'm on debian unstable and I > haven't seen my GTX 280 show up in the platforms or devices yet. I've tried > all of these I think: > > nvidia-opencl-dev - NVIDIA OpenCL development files > nvidia-libopencl1 - NVIDIA OpenCL library > nvidia-libopencl1-ia32 - please switch to multiarch nvidia-libopencl1:i386 > nvidia-opencl-common - NVIDIA OpenCL driver > nvidia-opencl-icd - NVIDIA OpenCL ICD > nvidia-opencl-icd-ia32 - please switch to multiarch nvidia-opencl-icd:i386 > nvidia-libopencl1-dev - NVIDIA OpenCL development files > > I've also tried installing drivers from the official cuda 5 distribution > (the one not using .deb files from nvidia's website). > > I feel like I'm stumped on what filename should I put into the nvidia.icd > file in /etc/Opencl/vendors. It's either just the file name of the .so (which is then searched using /etc/ld.so.conf and LD_LIBRARY_PATH) or the full absolute path of the .so. > The nvidia-opencl-common package sets up nvidia.icd to point to: > libnvidia-opencl.so.1 > > The only matching library I can find is: > /usr/lib/x86_64-linux-gnu/libnvidia-opencl.so.1 > > This library has no symbols: > $ nm /usr/lib/x86_64-linux-gnu/libnvidia-opencl.so.304.88 > 0000000000a5a190 b cudbgApiClientPid > 0000000000a5a194 b cudbgApiClientRevision > 0000000000130eb0 t cudbgApiInit > 0000000000a3b744 b cudbgDebuggerInitialized > 0000000000a5a164 b cudbgIpcFlag > 0000000000a5a170 b cudbgRpcEnabled > 0000000000a5a160 b cudbgSessionId That's normal, I think. $ nm /usr/lib/x86_64-linux-gnu/libamdocl64.so nm: /usr/lib/x86_64-linux-gnu/libamdocl64.so: no symbols But: $ nm -D /usr/lib/x86_64-linux-gnu/libamdocl64.so | egrep T 00000000002d5d80 T clBuildProgram 00000000002cdde0 T clCreateBuffer 00000000002bee60 T clCreateCommandQueue 00000000002bfa60 T clCreateContext 00000000002bfda0 T clCreateContextFromType (snip) > Perhaps unsurprisingly, no nvidia platform appears when I run clinfo. When > I install the amd opencl package, the icd file points to a library that has > cl* symbols, and the amd reveals my CPU as a device. All good. > > What do I have to do to get my nvidia card to be visible as an opencl > device? Do you have access to /dev/nvi*? Run you code under strace and see if it open()s the library and/or the /dev/nvi* device files. HTH, Andreas

8 years, 3 months

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PyOpenCL April 2013