PyOpenCL June 2015

pyopencl@tiker.net

6 participants
7 discussions

[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

4 years, 10 months

Re: [PyOpenCL] PyOpenCL wrapper for AMD clBLAS

by Andreas Kloeckner

Eric and Lars, Eric Hunsberger <erichuns(a)gmail.com> writes: > Looking at old mailing list messages, it looked like there had been past > interest in wrapping the AMD OpenCL BLAS library, but that no-one had > gotten around to it yet: > > http://lists.tiker.net/pipermail/pyopencl/2013-August/001573.html > > So I wrote my own wrapper the other day, only to find that Lars Ericson has > just done a very similar thing: > > http://lists.tiker.net/pipermail/pyopencl/2015-June/001890.html > > There are some differences, though. I'm interfacing with the new clBLAS > library as available on GitHub (https://github.com/clMathLibraries/clBLAS). > The interfaces are also different: Lars incorporates a lot more stuff in > the Cython files, so that you can write your own Cython programs to use the > BLAS functions, whereas my approach is just to make basic wrappers for the > functions so that they're easy to call on PyOpenCL Arrays from Python. > > I haven't done a lot of profiling yet, but the performance seems to be > pretty good on my NVIDIA GPU, especially for larger matrices (I haven't > done a comparison with cuBLAS, though). > > My wrapper is available here: > > https://github.com/hunse/pyopencl_blas > > Hopefully it's not too difficult to install; I've only tried on Linux. Thanks for making your wrappers available! I've added links to them from the front page of the PyOpenCL docs: http://documen.tician.de/pyopencl/ Andreas

5 years

PyOpenCL wrapper for AMD clBLAS

by Eric Hunsberger

Hi, Looking at old mailing list messages, it looked like there had been past interest in wrapping the AMD OpenCL BLAS library, but that no-one had gotten around to it yet: http://lists.tiker.net/pipermail/pyopencl/2013-August/001573.html So I wrote my own wrapper the other day, only to find that Lars Ericson has just done a very similar thing: http://lists.tiker.net/pipermail/pyopencl/2015-June/001890.html There are some differences, though. I'm interfacing with the new clBLAS library as available on GitHub (https://github.com/clMathLibraries/clBLAS). The interfaces are also different: Lars incorporates a lot more stuff in the Cython files, so that you can write your own Cython programs to use the BLAS functions, whereas my approach is just to make basic wrappers for the functions so that they're easy to call on PyOpenCL Arrays from Python. I haven't done a lot of profiling yet, but the performance seems to be pretty good on my NVIDIA GPU, especially for larger matrices (I haven't done a comparison with cuBLAS, though). My wrapper is available here: https://github.com/hunse/pyopencl_blas Hopefully it's not too difficult to install; I've only tried on Linux. Best, Eric

5 years

Re: [PyOpenCL] Interfacing PyOpenCL to clAmdBlas in Cython

by Lars.Ericson＠wellsfargo.com

Note: This is an existence proof, not an approach. I noticed some people getting halfway there. This gets all the way, but somebody with expert knowledge of pyOpenCL design would be best to integrate clMath into pyOpenCL. Also AMD told me clAmdBlas is deprecated in favor of open source clMath on github, so clMath is the thing to incorporate. ClMath has a pyopencl wrapper in the distribution, but there is not a soup to nuts example of how to use it in a purely pyOpenCL and pure Python way. Also other features aren't wrapped. Also there is githuib clRnd as well as clFFT mentioned. Not clear to me where wrappers should go so that user in Python (not writing app code in Cython),can see all of these libraries in pyOpenCL. Should wrappers go in each package or in pyOpenCL package?

5 years

Re: [PyOpenCL] Interfacing PyOpenCL to clAmdBlas in Cython

by Jerome Kieffer

Your approach looks pretty symilar to this code for clFFT. https://github.com/geggo/gpyfft Knowing AMD has opened all the C++ code underneath, would it make sense to joint efforts ? Cheers, Jerome

5 years

Interfacing PyOpenCL to clAmdBlas in Cython

by Lars.Ericson＠wellsfargo.com

Hi, I wrote a program which uses pyOpenCL to call clAmdBlas DGEMM . This example is a recoding of example_sgemm.c in the clAmdBlas distribution, changed to use Python, Cython, NumPY, pyOpenCL and DGEMM. The reason for doing this is to show how to access fast matrix multiplication which is also host-tunable, while at the same time be able to mix with other pyOpenCL calls for example for simple addition of matrices and for random number generation. I followed this route for two reasons: 1. It is hard to find self-contained OpenCL kernels on the Internet for fast matrix multiplication for matrices of any dimension. Most examples assume a dimension which happens to fit the gadget. More pragmatic matrix multiplication codes use a C++ program to generate the kernel on the fly based on the dimensions. This is what clAmdBlas does. I didn't find any Python codes that replicate this. 2. I couldn't figure out by reading the pyOpenCL documentation how to multiply two matrices. I guessed I could do it with Reikna, but I wasn't convinced the result would be dimension-optimised. I looked in the code for matrix multiplication in the pyOpenCL distribution and it looked at first glance like an element-wise multiply. I am sharing this with PyOpenCL mailing list in case PyOpenCL folks want to incorporate wrappers for clAmdBlas and clAmdRnd (references below). Here is the Cython header for clAmdBlasDGEMM: from libc.stdint cimport intptr_t, uintptr_t cdef extern from "clAmdBlas.h": enum: CL_SUCCESS = 0 CL_INVALID_VALUE = -30 CL_INVALID_COMMAND_QUEUE = -36 CL_INVALID_CONTEXT = -34 CL_INVALID_MEM_OBJECT = -38 CL_INVALID_DEVICE = -33 CL_INVALID_EVENT_WAIT_LIST = -57 CL_OUT_OF_RESOURCES = -5 CL_OUT_OF_HOST_MEMORY = -6 CL_INVALID_OPERATION = -59 CL_COMPILER_NOT_AVAILABLE = -3 CL_BUILD_PROGRAM_FAILURE = -11 enum clAmdBlasStatus: clAmdBlasSuccess = CL_SUCCESS clAmdBlasInvalidValue = CL_INVALID_VALUE clAmdBlasInvalidCommandQueue = CL_INVALID_COMMAND_QUEUE clAmdBlasInvalidContext = CL_INVALID_CONTEXT clAmdBlasInvalidMemObject = CL_INVALID_MEM_OBJECT clAmdBlasInvalidDevice = CL_INVALID_DEVICE clAmdBlasInvalidEventWaitList = CL_INVALID_EVENT_WAIT_LIST clAmdBlasOutOfResources = CL_OUT_OF_RESOURCES clAmdBlasOutOfHostMemory = CL_OUT_OF_HOST_MEMORY clAmdBlasInvalidOperation = CL_INVALID_OPERATION clAmdBlasCompilerNotAvailable = CL_COMPILER_NOT_AVAILABLE clAmdBlasBuildProgramFailure = CL_BUILD_PROGRAM_FAILURE clAmdBlasNotImplemented = -1024 clAmdBlasNotInitialized = -1023 clAmdBlasInvalidMatA clAmdBlasInvalidMatB clAmdBlasInvalidMatC clAmdBlasInvalidVecX clAmdBlasInvalidVecY clAmdBlasInvalidDim clAmdBlasInvalidLeadDimA clAmdBlasInvalidLeadDimB clAmdBlasInvalidLeadDimC clAmdBlasInvalidIncX clAmdBlasInvalidIncY clAmdBlasInsufficientMemMatA clAmdBlasInsufficientMemMatB clAmdBlasInsufficientMemMatC clAmdBlasInsufficientMemVecX clAmdBlasInsufficientMemVecY enum clAmdBlasOrder: clAmdBlasRowMajor = 0 clAmdBlasColumnMajor = 1 enum clAmdBlasTranspose: clAmdBlasNoTrans = 0 clAmdBlasTrans = 1 clAmdBlasConjTrans = 2 ctypedef unsigned int cl_uint ctypedef signed int cl_int ctypedef float cl_float 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 ctypedef unsigned long cl_ulong ctypedef cl_ulong cl_bitfield ctypedef cl_bitfield cl_device_type cl_device_type CL_DEVICE_TYPE_GPU = (1 << 2) ctypedef cl_bitfield cl_command_queue_properties ctypedef cl_uint cl_bool cl_bool CL_FALSE = 0 cl_bool CL_TRUE = 1 ctypedef cl_bitfield cl_mem_flags cl_mem_flags CL_MEM_READ_ONLY = (1 << 2) cl_mem_flags CL_MEM_READ_WRITE = (1 << 0) cl_int clGetPlatformIDs(cl_uint num_entries, cl_platform_id *platforms, cl_uint *num_platforms) cl_int clGetDeviceIDs(cl_platform_id platform, cl_device_type device_type, cl_uint num_entries, cl_device_id *devices, cl_uint *num_devices) ctypedef intptr_t cl_context_properties cl_context_properties CL_CONTEXT_PLATFORM = 0x1084 cl_context clCreateContext(cl_context_properties *properties, cl_uint ndevices, cl_device_id *devices, void *pfn_notify, void *user_data, cl_int *errcode_ret) cl_command_queue clCreateCommandQueue(cl_context context, cl_device_id device, cl_command_queue_properties properties, cl_int *errcode_ret) cl_int clEnqueueReadBuffer(cl_command_queue command_queue, cl_mem buffer, cl_bool blocking_read, size_t offset, size_t size, void * ptr, cl_uint num_events_in_wait_list, const cl_event * event_wait_list, cl_event * event) cl_int clEnqueueWriteBuffer(cl_command_queue command_queue, cl_mem buffer, cl_bool blocking_write, size_t offset, size_t size, const void * ptr, cl_uint num_events_in_wait_list, const cl_event * event_wait_list, cl_event * event) cl_int clReleaseContext(cl_context context) cl_int clReleaseCommandQueue(cl_command_queue command_queue) cl_mem clCreateBuffer(cl_context context, cl_mem_flags flags, size_t size, void * host_ptr, cl_int * errcode_ret) cl_int clWaitForEvents(cl_uint num_events, const cl_event * event_list) cl_int clReleaseMemObject(cl_mem memobj) 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) Here is the main program: import sys from clAmdBlas cimport * import ctypes import numpy as np cimport numpy as np import pyopencl as cl import pyopencl.array as cl_array cdef class GPU: cdef cl_platform_id platform cdef cl_device_id device cdef cl_event event cdef cl_command_queue queue cdef cl_context ctx cdef cl_int err cdef public py_platform, py_device, py_context, py_queue def __dealloc__(self): clAmdBlasTeardown() print "clean up and shut down gadget" def __cinit__(self): self.event = NULL cdef cl_context_properties props[3] props[:] = [CL_CONTEXT_PLATFORM, 0, 0] self.py_platform = cl.get_platforms()[0] cdef intptr_t platform_p = self.py_platform.int_ptr self.platform = <cl_platform_id>platform_p self.py_device = self.py_platform.get_devices()[0] cdef intptr_t device_p = self.py_device.int_ptr self.device = <cl_device_id>device_p self.py_context = cl.Context(devices=[self.py_device]) cdef intptr_t context_p = self.py_context.int_ptr self.ctx = <cl_context>context_p self.py_queue = cl.CommandQueue(self.py_context, device=self.py_device) cdef intptr_t queue_p = <intptr_t>self.py_queue.int_ptr self.queue = <cl_command_queue>queue_p self.err = clAmdBlasSetup() if self.err != CL_SUCCESS: raise ValueError("clAmdBlasSetup() failed with %d" % self.err) else: print "Blas setup" DTYPE = np.float64 ctypedef np.float64_t DTYPE_t def dgemm_test(): gpu = GPU() cdef size_t M = 4 cdef size_t N = 3 cdef size_t K = 5 cdef size_t A_size = M*K*sizeof(cl_double) a = np.ascontiguousarray([11,12,13,14,15,21,22,23,24,25,31,32,33,34,35,41,42,43,44,45], dtype=DTYPE) a_d = cl_array.to_device(gpu.py_queue, a) cdef intptr_t a_d_p = a_d.data.int_ptr cdef cl_mem bufA = <cl_mem> a_d_p print "A" cdef size_t B_size = K*N*sizeof(cl_double) b = np.ascontiguousarray([11,12,13,21,22,23,31,32,33,41,42,43,51,52,53], dtype=DTYPE) b_d = cl_array.to_device(gpu.py_queue, b) cdef intptr_t b_d_p = b_d.data.int_ptr cdef cl_mem bufB = <cl_mem> b_d_p print "B" cdef size_t C_size = M*N*sizeof(cl_double) c = np.ascontiguousarray([11,12,13,21,22,23,31,32,33,41,42,43], dtype=DTYPE) c_d = cl_array.to_device(gpu.py_queue, c) cdef intptr_t c_d_p = c_d.data.int_ptr cdef cl_mem bufC = <cl_mem> c_d_p print "C" cdef size_t result_size = C_size cdef np.ndarray[DTYPE_t, ndim=1] r = np.zeros(M*N, dtype=DTYPE) cdef DTYPE_t[:] r_view = r cdef cl_double *result = &r_view[0] print "r" cdef cl_double alpha = 10 cdef cl_double beta = 20 err = clAmdBlasDgemm(clAmdBlasRowMajor,clAmdBlasNoTrans,clAmdBlasNoTrans,M,N,K,alpha,bufA,K,bufB,N,beta,bufC,N,1,&gpu.queue,0,NULL,&gpu.event) if err != CL_SUCCESS: return "clAmdBlasDgemm() failed with %d" % err else: print "DGEMMed" err = clWaitForEvents(1, &gpu.event) err = clEnqueueReadBuffer(gpu.queue, bufC, CL_TRUE, 0, result_size, result, 0, NULL, NULL) return r This then gets compiled to a Python module by running python example_dgemm_setup.py build_ext -inplace with this build definition file example_dgemm_setup.py: from distutils.core import setup from distutils.extension import Extension from Cython.Build import cythonize import numpy as np extensions = [ Extension(name = 'example_dgemm', sources = ['example_dgemm.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 ) This can then be imported into a Python script like this one cytest.py: import example_dgemm r = example_dgemm.dgemm_test() print "expecting 21370 22040 22710 37070 38240 39410 52770 54440 56110 68470 70640 72810" print r This can then be run with "python cytest.py", giving this output: python dytest.py Blas setup A B C r DGEMMed clean up and shut down gadget expecting 21370 22040 22710 37070 38240 39410 52770 54440 56110 68470 70640 72810 [ 21370. 22040. 22710. 37070. 38240. 39410. 52770. 54440. 56110. 68470. 70640. 72810.] I was helped by looking at an initial wrapper from Kent Knox in the clMath distribution, plus a couple of other clues mentioned in references. Note that clAmdBlasTune.exe is undocumented but mentioned here and there on the Internet. The code above isn't long, but involves NumPy, OpenCL, pyOpenCL, Cython and MemoryViews, ctypes and libc.stdint.intptr_t, and about 10 cups of coffee. References: 1. NumPy and Ctypes a. https://sage.math.washington.edu:8091/hudson/job/cython-docs/doclinks/1/src… b. https://docs.python.org/2/library/ctypes.html c. http://docs.scipy.org/doc/numpy/user/c-info.python-as-glue.html 2. Cython a. http://cython.org/ b. http://docs.cython.org/src/userguide/extension_types.html 3. DGEMM and clMath and clAmdBlas a. http://www.math.utah.edu/software/lapack/lapack-blas/dgemm.html b. http://developer.amd.com/tools-and-sdks/opencl-zone/amd-accelerated-paralle… c. C:\Program Files (x86)\AMD APP SDK\2.9-1\include\CL\cl.h d. c:/Program Files (x86)/AMD/clAmdBlas/samples/example_sgemm.c e. c:/Program Files (x86)/AMD/clAmdBlas/bin64/clAmdBlas.dll f. c:/Program Files (x86)/AMD/clAmdBlas/bin64/clAmdBlasTune.exe g. C:\Program Files (x86)\AMD\clAmdBlas\include\clAmdBlas.h h. https://github.com/clMathLibraries/clBLAS/tree/master/src/wrappers/python (seems correct but complete example not provided) i. http://lists.tiker.net/pipermail/pyopencl/2014-February/001690.html (incomplete attempt, coredumps maybe due to mixing raw OpenCL and pyOpenCL deallocation) j. http://stackoverflow.com/questions/13396443/pyopencl-difference-between-to-… k. http://diffusionht.blogspot.com/2013/08/clamdblastune-ati-radeon-7970-linux… 4. clRNG (similarly, would be nice to have in pyOpenCL) http://developer.amd.com/community/blog/2015/05/05/amd-releases-clrng-an-op… 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

Bug with intel card/linux driver?

by Riccardo De Maria

Hello, I am just starting with opencl. I have notebook with an Intel HD BroadWell U-Processor GT2 on Intel Gen OCL driver on Linux. The following kernel get and return non-sense as input and output. Please note the cl_khr_fp64: enable. The code runs fine on nvidia tesla in another machine. Everything works fine with float32. Do you have any clue why fp64 is broken? Is it a driver issue? How can I dig further? Many thanks! Riccardo import numpy as np import pyopencl as cl a_np = np.array([1,2,3]).astype('float64') b_np = np.array([4,5,6]).astype('float64') ctx = cl.create_some_context() queue = cl.CommandQueue(ctx) mf = cl.mem_flags a_g = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=a_np) b_g = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=b_np) prg = cl.Program(ctx, r""" #pragma OPENCL EXTENSION cl_khr_fp64 : enable __kernel void sum(__global const double *a_g, __global const double *b_g, __global double *res_g) { int gid = get_global_id(0); res_g[gid] = a_g[gid] + b_g[gid]; } """).build() res_g = cl.Buffer(ctx, mf.WRITE_ONLY, a_np.nbytes) prg.sum(queue, a_np.shape, None, a_g, b_g, res_g) res_np = np.empty_like(a_np) cl.enqueue_copy(queue, res_np, res_g) print res_np print a_np+b_np

5 years, 1 month

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PyOpenCL June 2015