PyOpenCL July 2015

pyopencl@tiker.net

9 participants
8 discussions

[request] Upcoming version: testing/feedback

by Andreas Kloeckner

Hi all, thanks to the hard work of Marko Bencun and Yichao Yu, the next version of PyOpenCL will be substantially different internally from the previous one. In particular, the wrapper will no longer be built using Boost.Python but instead using cffi 1.0's ahead-of-time mode. One main consequence of this is that PyOpenCL now works on Pypy. This new code is now on the git master branch. (It used to live on the 'cffi' branch. The old Boost wrapper is now on the 'deprecated-boost-python' branch.) From a user's perspective, nothing should have changed--on all machines I have access to, PyOpenCL passes the same tests as before, on any Python version more recent than 2.6, including Pypy. Nonetheless, before I go ahead and release a new PyOpenCL based on this code, I'd like to get as many of you as I can to try it and report back. If you package PyOpenCL, or if you have a Mac or a Windows machine, I'd especially like to hear from you. Thanks! Andreas

5 years

[POLL: INCOMPATIBLE CHANGE] PyOpenCL complex support

by Andreas Kloeckner

Hi all, I'm writing about PyOpenCL's support for complex numbers. As of right now, PyOpenCL's complex numbers are typedefs of float2 and double2, which makes complex+complex addition and real*complex addition match the desired semantics, but lots of other operators silently do the wrong thing, such as - complex*complex - real+complex I've come to regard this as a major flaw, and I can't count the number of times I've had to hunt bugs related to this, and so I'd like to get rid of it. I've thought about ways of doing this in a backward-compatible manner, and they all strike me as flawed, and so I'd prefer to move to a simple struct (supporting both .real and .imag as well as the old .x and .y members) in one big change. If you have code depending on PyOpenCL's complex number support and are opposed to this change, please speak up now. I'll make the change in git today to give you a preview. What do you think? Andreas

5 years

Re: [PyOpenCL] [request] Upcoming version: testing/feedback

by Gregor Thalhammer

> Am 02.07.2015 um 09:39 schrieb Bogdan Opanchuk <bogdan(a)opanchuk.net>: > > (did not CC to the mail list by mistake) > > Hi Andreas, > > I tried to compile & install it on OSX 10.10.4, default clang (from Xcode 6.4) and Pythons 2.7.9, 3.4.3, and pypy-2.6.0 (installed via pyenv). Strangely enough, I have not encountered the problem Gregor reported — PyOpenCL compiles successfully and seems to work fine with my programs. > Hi, as a follow up to my initial report, I could succesfully build recent pyopencl with Python 2.7.6 from python.org <http://python.org/> (now on os 10.10.4), but not with Anaconda Python 2.7. I opened an issue https://github.com/ContinuumIO/anaconda-issues/issues/373 <https://github.com/ContinuumIO/anaconda-issues/issues/373> for anaconda, perhaps there someone knows how to resolve this. Gregor

5 years

PyOpenCL

by Benson Muite

Hi, Has anyone tried OpenCL on Xeon Phi systems (eg. Stampede)? If so, how did you get it to work, in particular what runtime libraries did you use? Benson

5 years, 1 month

PyOpenCL on OS X - weird results

by Justin Fanning

I recently upgraded my MacBook pro, to discover Apple no longer uses Nvidia and has swapped to AMD, meaning I have had to swap from pycuda to pyopencl. I have run several Apple supplied openCL demos, though the code at: http://documen.tician.de/pyopencl/ <http://documen.tician.de/pyopencl/> seems to give odd output, I was hoping someone could validate if this is correct or what steps I should take? I installed PyOpenCL from the instructions bottom of page: http://wiki.tiker.net/PyOpenCL/Installation/Mac <http://wiki.tiker.net/PyOpenCL/Installation/Mac> Warmest regards, Justin $ python ./pyopencl/pyopencl/examples/demo.py Choose platform: [0] <pyopencl.Platform 'Apple' at 0x7fff0000> Choice [0]: Choose device(s): [0] <pyopencl.Device 'Intel(R) Core(TM) i7-4980HQ CPU @ 2.80GHz' on 'Apple' at 0xffffffff> [1] <pyopencl.Device 'Iris Pro' on 'Apple' at 0x1024500> [2] <pyopencl.Device 'AMD Radeon R9 M370X Compute Engine' on 'Apple' at 0x1021c00> Choice, comma-separated [0]:2 Set the environment variable PYOPENCL_CTX=':2' to avoid being asked again. [ 0. 0. 0. ..., 0. 0. 0.] <— Is this correct? 0.0 <— Is this correct? Config is: Model Name: MacBook Pro Model Identifier: MacBookPro11,5 Processor Name: Intel Core i7 Processor Speed: 2.8 GHz Number of Processors: 1 Total Number of Cores: 4 L2 Cache (per Core): 256 KB L3 Cache: 6 MB Memory: 16 GB Boot ROM Version: MBP114.0172.B04 SMC Version (system): 2.30f2 System Version: OS X 10.10.4 (14E46) Kernel Version: Darwin 14.4.0 Boot Volume: Macintosh HD Boot Mode: Normal Secure Virtual Memory: Enabled Time since boot: 4 days 1:41 AMD Radeon R9 M370X: Chipset Model: AMD Radeon R9 M370X Type: GPU Bus: PCIe PCIe Lane Width: x8 VRAM (Total): 2048 MB Vendor: ATI (0x1002) Device ID: 0x6821 Revision ID: 0x0083 ROM Revision: 113-C5670E-777 gMux Version: 4.0.20 [3.2.8] EFI Driver Version: 01.00.777 Displays: Color LCD: Display Type: Retina LCD Resolution: 2880 x 1800 Retina Retina: Yes Pixel Depth: 32-Bit Color (ARGB8888) Main Display: Yes Mirror: Off Online: Yes Built-In: Yes [ end ]

5 years, 1 month

running on Xeon Phi

by Sven Warris

Hi all, We have ported a CUDA implementation to an OpenCL implementation. The CUDA version was running in a python application using pyCUDA, so now I'm looking into pyOpenCL to add this new implementation to our applications. I've managed to have it up and running on a desktop CPU (Intel) and GPU (NVIDIA). The challenge now is to run pyOpenCL on a server (centos linux) with Xeon Phi cards. The host CPU runs the demo.py nicely. However, the Xeon Phi card returns all zeros in the memory. The demo.py does recognize the cards: >>> ctx = cl.create_some_context() Choose platform: [0] <pyopencl.Platform 'Intel(R) OpenCL' at 0x7f9e20> Choice [0]: Choose device(s): [0] <pyopencl.Device 'Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz' on 'Intel(R) OpenCL' at 0x7e76d8> [1] <pyopencl.Device 'Intel(R) Many Integrated Core Acceleration Card' on 'Intel(R) OpenCL' at 0xe07c38> [2] <pyopencl.Device 'Intel(R) Many Integrated Core Acceleration Card' on 'Intel(R) OpenCL' at 0x7da438> [3] <pyopencl.Device 'Intel(R) Many Integrated Core Acceleration Card' on 'Intel(R) OpenCL' at 0xfa8488> [4] <pyopencl.Device 'Intel(R) Many Integrated Core Acceleration Card' on 'Intel(R) OpenCL' at 0xfa9b28> [5] <pyopencl.Device 'Intel(R) Many Integrated Core Acceleration Card' on 'Intel(R) OpenCL' at 0xfab208> [6] <pyopencl.Device 'Intel(R) Many Integrated Core Acceleration Card' on 'Intel(R) OpenCL' at 0xfac8e8> When I print the resulting array using device 0 (host CPU): >>> print(res_np) [ 1.13724446 0.91993028 1.07355368 ..., 0.70078576 1.66417909 1.3580389 ] When I print the resulting array using device 1 (Xeon Phi card): >>> print(res_np) [ 0. 0. 0. ..., 0. 0. 0.] The compiler says: /home/me/.local/lib/python2.7/site-packages/pyopencl/__init__.py:59: CompilerWarning: From-source build succeeded, but resulted in non-empty logs: Build on <pyopencl.Device 'Intel(R) Many Integrated Core Acceleration Card' on 'Intel(R) OpenCL' at 0x1662be8> succeeded, but said: Compilation started Compilation done Linking started Linking done Device build started Device build done Build started Kernel <sum> was successfully vectorized (16) Done. warn(text, CompilerWarning) I'm missing a library? Do I need to install something on the cards related to pyOpenCL? Any help is very much appreciated! Sven

5 years, 2 months

Re: [PyOpenCL] CLMath, matrix_multiply and builds that depend on installed drivers

by Andreas Kloeckner

Hi Lars, Lars.Ericson(a)wellsfargo.com writes: > The reason it doesn't have a Windows installer is that a process of > post-build host-based tuning which was used on clAmdBlas has been > replaced by direct access to driver properties in the compilation of > clBlas. This means that each user of the package has to compile it on > their machine before they can use it, which also means that wrappers > for the package to pyOpenCL have to be built at that time. In > addition, if you have a machine with multiple OpenCL devices (for > example I have AMD and Intel OpenCL on my workstation, with the Intel > CPU chip acting as a separate platform and device), I don't know if > the build is correct and optimal for all devices and platforms on the > machine at build time or only correct and optimal for the AMD device. Thanks for sharing your wrapper. So clBlas does tuning at the actual build time of the library? That seems a little weird, given that the device that the dgemm would target might not even be available until runtime... and since OpenCL makes JIT so easy, I get this even less. Looking at the source, it seems that the library might ship a binary called clblastune that a use could run to redo the hw tuning: https://github.com/clMathLibraries/clBLAS/blob/9731ea2a270509211a47bf6cf9df… If the tuning could be done after the fact, I don't really see the obstacle to a Windows installer. Andreas

5 years, 2 months

CLMath, matrix_multiply and builds that depend on installed drivers

by Lars.Ericson＠wellsfargo.com

Hi, I wrapped AMD's deprecated DGEMM as a form of matrix_multiply in pyOpenCL as follows: python matrix_multiply_setup.py build_ext -inplace where matrix_multiply_setup.py is from distutils.core import setup from distutils.extension import Extension from Cython.Build import cythonize import numpy as np extensions = [ Extension(name = 'matrix_multiply', sources = ['matrix_multiply.pyx'], include_dirs = [ "C:\\Program Files (x86)\\AMD APP SDK\\2.9-1\\include", "C:\\Program Files (x86)\\AMD\\clAmdBlas\\include", np.get_include() ], library_dirs = ["C:\\Program Files (x86)\\AMD APP SDK\\2.9-1\\bin\\x86_64", "c:\\Program Files (x86)\\AMD\\clAmdBlas\\bin64"], libraries=['clAmdBlas', 'OpenCL']) ] extensions = cythonize(extensions) setup( ext_modules = extensions ) and matrix_multiply.pyx is: import numpy as np cimport numpy as np import pyopencl as cl import pyopencl.array as cla import pyopencl.clrandom as clr import pyopencl.clmath as clm from clAmdBlas cimport * def blas_setup(): clAmdBlasSetup() def blas_teardown(): clAmdBlasTeardown() def matrix_multiply(A_g,B_g,C_g,queue): (M,K)=A_g.shape N=B_g.shape[1] cdef cl_event event = NULL cdef intptr_t queue_p = <intptr_t>queue.int_ptr cdef cl_command_queue cq = <cl_command_queue>queue_p cdef intptr_t A_g_p = A_g.data.int_ptr cdef cl_mem bufA = <cl_mem> A_g_p cdef intptr_t B_g_p = B_g.data.int_ptr cdef cl_mem bufB = <cl_mem> B_g_p cdef intptr_t C_g_p = C_g.data.int_ptr cdef cl_mem bufC = <cl_mem> C_g_p err = clAmdBlasDgemm(clAmdBlasRowMajor,clAmdBlasNoTrans,clAmdBlasNoTrans,M,N,K,1.0, bufA,K,bufB,N,0.0,bufC,N,1,&cq,0,NULL,&event) where clAmdBlas.pxd is: from libc.stdint cimport intptr_t, uintptr_t cdef extern from "clAmdBlas.h": enum: CL_SUCCESS = 0 enum clAmdBlasStatus: clAmdBlasSuccess = CL_SUCCESS enum clAmdBlasOrder: clAmdBlasRowMajor = 0 enum clAmdBlasTranspose: clAmdBlasNoTrans = 0 ctypedef unsigned int cl_uint ctypedef double cl_double ctypedef void* cl_mem ctypedef void* cl_command_queue ctypedef void* cl_event ctypedef void* cl_platform_id ctypedef void* cl_device_id ctypedef void* cl_context clAmdBlasStatus clAmdBlasSetup( ) void clAmdBlasTeardown( ) clAmdBlasStatus clAmdBlasDgemm(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue *commandQueues, cl_uint numEventsInWaitList, const cl_event *eventWaitList, cl_event *events) Once matrix_multiply.pyd is created, it can be used in a pure Python program involving PyOpenCL for example as follows, where queue is a pyOpenCL queue: import pyopencl.array as cla import matrix_multiply import numpy as np import pyopencl as cl A = np.ascontiguousarray(np.ones((2,2))) B = np.ascontiguousarray(np.ones(2,2))) bufA = cla.to_device(queue, A) bufB = cla.to_device(queue, B) bufC = cl.array.zeros(queue, shape=((2,2)), dtype=np.float64) matrix_multiply.blas_setup() matrix_multiply.matrix_multiply(bufA, bufB, bufC, queue) matrix_multiply.blas_teardown() Note though that clAmdBlas is deprecated in favor of clBlas on gitub: https://github.com/clMathLibraries/clBLAS, which doesn't have a Windows installer, whereas clAmdBlas did. The reason it doesn't have a Windows installer is that a process of post-build host-based tuning which was used on clAmdBlas has been replaced by direct access to driver properties in the compilation of clBlas. This means that each user of the package has to compile it on their machine before they can use it, which also means that wrappers for the package to pyOpenCL have to be built at that time. In addition, if you have a machine with multiple OpenCL devices (for example I have AMD and Intel OpenCL on my workstation, with the Intel CPU chip acting as a separate platform and device), I don't know if the build is correct and optimal for all devices and platforms on the machine at build time or only correct and optimal for the AMD device. Thanks, Lars Ericson Quantitative Analytics Consultant Market & Institutional Risk Management Wells Fargo Bank, N.A. | 301 S. College St., 4th Floor | Charlotte, NC 28202-6000 MAC D1053-04X Tel 704-410-2219 | Cell 917-891-1639 lars.ericson(a)wellsfargo.com<mailto:lars.ericson@wellsfargo.com>

5 years, 2 months

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PyOpenCL July 2015