Python pyopencl.Program方法代碼示例

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import Program [as 別名]
def compile(self, kernel_instance):
        """call the OpenCL compiler to compile the kernel, return the device function

        :param kernel_name: The name of the kernel to be compiled, used to lookup the
            function after compilation.
        :type kernel_name: string

        :param kernel_string: The OpenCL kernel code that contains the function `kernel_name`
        :type kernel_string: string

        :returns: An OpenCL kernel that can be called directly.
        :rtype: pyopencl.Kernel
        """
        prg = cl.Program(self.ctx, kernel_instance.kernel_string).build(options=self.compiler_options)
        func = getattr(prg, kernel_instance.name)
        return func 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import Program [as 別名]
def compile(self,type):
        fname = ""
        self.type=type
        if (self.type == 'sha1'):
            fname = os.path.join("Library","sha1.cl")
        elif (self.type == 'sha256'):
            fname = os.path.join("Library","sha256.cl")
        elif (self.type == 'pbkdf2_sha1'):
            fname = os.path.join("Library","pbkdf2_sha1.cl")
        elif (self.type == 'pbkdf2_sha256'):
            fname = os.path.join("Library","pbkdf2_sha256.cl")
        else:
            print('Type: ' + self.type + ' not supported!')
            exit(0)

        with open(fname, "r") as rf:
            src = rf.read()

        # Kernel function instantiation
        self.prg = cl.Program(self.ctx, src).build() 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import Program [as 別名]
def __create_program(self):
        self.__include_code = self.sas.kernelize()
        codes = self.__include_code + '\n'
        # codes = self.__args_codes + '\n' +\
        #         self.__populate_codes + '\n' +\
        #         self.__evaluate_code + '\n' +\
        #         self.__include_code + '\n' +\
        #         self.__fitness_kernel_str

        f = open('ocl_sa.cl', 'r')
        fstr = ''.join(f.readlines())
        f.close()

        if self.__debug_mode:
            fdbg = open('final.cl', 'w')
            fdbg.write(codes + fstr)
            fdbg.close()

        self.__prg = cl.Program(self.__ctx, codes + fstr).build(self.__include_path); 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import Program [as 別名]
def __create_program(self):
        codes = self.__args_codes + '\n' +\
                self.__populate_codes + '\n' +\
                self.__evaluate_code + '\n' +\
                self.__include_code + '\n' +\
                self.__fitness_kernel_str
        kernel_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'kernel')
        f = open(os.path.join(kernel_path, 'ocl_ga.cl'), 'r')
        fstr = ''.join(f.readlines())
        f.close()
        if self.__debug_mode:
            fdbg = open('final.cl', 'w')
            fdbg.write(codes + fstr)
            fdbg.close()

        self.__prg = cl.Program(self.__ctx, codes + fstr).build(self.__include_path); 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import Program [as 別名]
def _compile_kernels(self):
        # TODO: global cache, so every instantiation doesn't necessitate a
        #       full compile
        self.compiled_kernels = {}
        for domain in self.kernels.keys():
            self.compiled_kernels[domain] = {}
            for op_type in self.kernels[domain]:
                kernel = self.kernels[domain][op_type]
                if domain is backend.OpenCLMemory and isinstance(kernel, str):
                    prg = cl.Program(self.context.sub_context, kernel)
                    prg = prg.build()
                    try:
                        built_kernel = prg.all_kernels()[0]
                    except IndexError:
                        log.warning("Failed to build kernel for domain %s and operand types %s" % (domain, op_type))
                        built_kernel = noop
                    self.compiled_kernels[domain][op_type] = built_kernel
                else:
                    self.compiled_kernels[domain][op_type] = kernel 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import Program [as 別名]
def change_params(self, cl_platform_index=None, cl_device_index=None, **kargs):
        BasePeakDetector.change_params(self,  **kargs)
        OpenCL_Helper.initialize_opencl(self, cl_platform_index=cl_platform_index, cl_device_index=cl_device_index)

        if self.chunksize > self.max_wg_size:
            n = int(np.ceil(self.chunksize / self.max_wg_size))
            self.global_size =  (self.max_wg_size * n, )
            self.local_size = (self.max_wg_size,)
        else:
            self.global_size = (self.chunksize, )
            self.local_size = (self.chunksize, )


        chunksize2=self.chunksize+2*self.n_span
        
        self.sum_rectified = np.zeros((self.chunksize), dtype=self.dtype)
        self.peak_mask = np.zeros((self.chunksize), dtype='uint8')
        ring_sum = np.zeros((chunksize2), dtype=self.dtype)
        
        #GPU buffers
        self.sigs_cl = pyopencl.Buffer(self.ctx, mf.READ_WRITE, size=self.nb_channel*self.chunksize*self.dtype.itemsize)
        
        self.ring_sum_cl = pyopencl.Buffer(self.ctx, mf.READ_WRITE| mf.COPY_HOST_PTR, hostbuf=ring_sum)
        self.peak_mask_cl = pyopencl.Buffer(self.ctx, mf.READ_WRITE | mf.COPY_HOST_PTR, hostbuf=self.peak_mask)
        
        kernel = self.kernel%dict(chunksize=self.chunksize, nb_channel=self.nb_channel, n_span=self.n_span,
                    relative_threshold=self.relative_threshold, peak_sign={'+':1, '-':-1}[self.peak_sign])
        
        prg = pyopencl.Program(self.ctx, kernel)
        self.opencl_prg = prg.build(options='-cl-mad-enable')
        
        self.kern_detect_peaks = getattr(self.opencl_prg, 'detect_peaks') 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import Program [as 別名]
def _get_kernels(self):
        """Return KNN and range search OpenCL kernels."""
        kernel_source = open(self.kernel_location).read()
        program = cl.Program(self.context, kernel_source).build()
        kNN_kernel = program.kernelKNNshared
        kNN_kernel.set_scalar_arg_dtypes([None, None, None, np.int32,
                                          np.int32, np.int32, np.int32,
                                          np.int32, None])

        RS_kernel = program.kernelBFRSAllshared
        RS_kernel.set_scalar_arg_dtypes([None, None, None, None,
                                         np.int32, np.int32, np.int32,
                                         np.int32, None])
        return (kNN_kernel, RS_kernel) 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import Program [as 別名]
def kernel_info(self, arg_to_dtype_set=frozenset(), all_kwargs=None):
        kernel = self.get_typed_and_scheduled_kernel(arg_to_dtype_set)

        from loopy.codegen import generate_code_v2
        from loopy.target.execution import get_highlighted_code
        codegen_result = generate_code_v2(kernel)

        dev_code = codegen_result.device_code()

        if self.kernel.options.write_cl:
            output = dev_code
            if self.kernel.options.highlight_cl:
                output = get_highlighted_code(output)

            if self.kernel.options.write_cl is True:
                print(output)
            else:
                with open(self.kernel.options.write_cl, "w") as outf:
                    outf.write(output)

        if self.kernel.options.edit_cl:
            from pytools import invoke_editor
            dev_code = invoke_editor(dev_code, "code.cl")

        import pyopencl as cl

        cl_program = (
                cl.Program(self.context, dev_code)
                .build(options=kernel.options.cl_build_options))

        cl_kernels = _Kernels()
        for dp in codegen_result.device_programs:
            setattr(cl_kernels, dp.name, getattr(cl_program, dp.name))

        return _KernelInfo(
                kernel=kernel,
                cl_kernels=cl_kernels,
                implemented_data_info=codegen_result.implemented_data_info,
                invoker=self.get_invoker(kernel, codegen_result)) 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import Program [as 別名]
def test_gpu_vector_sum(a, b):
    #define the PyOpenCL Context
    platform = cl.get_platforms()[0]
    device = platform.get_devices()[0]
    context = cl.Context([device])
    queue = cl.CommandQueue(context, \
                            properties=cl.command_queue_properties.PROFILING_ENABLE)   
    #prepare the data structure
    a_buffer = cl.Buffer\
               (context, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf=a)
    b_buffer = cl.Buffer\
               (context, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf=b)
    c_buffer = cl.Buffer\
               (context, cl.mem_flags.WRITE_ONLY, b.nbytes)   
    program = cl.Program(context, """
    __kernel void sum(__global const float *a, __global const float *b, __global float *c)
    {
        int i = get_global_id(0);
        int j;
        for(j = 0; j < 10000; j++)
        {
            c[i] = a[i] + b[i];
        }
    }""").build()
    #start the gpu test
    gpu_start_time = time()   
    event = program.sum(queue, a.shape, None, a_buffer, b_buffer, c_buffer)   
    event.wait()   
    elapsed = 1e-9*(event.profile.end - event.profile.start)   
    print("GPU Kernel evaluation Time: {0} s".format(elapsed))   
    c_gpu = np.empty_like(a)  
    cl._enqueue_read_buffer(queue, c_buffer, c_gpu).wait()  
    gpu_end_time = time()  
    print("GPU Time: {0} s".format(gpu_end_time - gpu_start_time))   
    return c_gpu   

#start the test 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import Program [as 別名]
def build_program(ctx, filename):
    prog = None
    try:
        f = open(filename, 'r')
        fstr = ''.join(f.readlines())
        f.close()
        # -Werror : Make all warnings into errors.
        # https://www.khronos.org/registry/OpenCL/sdk/2.0/docs/man/xhtml/clBuildProgram.html
        prog = cl.Program(ctx, fstr).build(options=['-Werror'], cache_dir=None);
    except:
        import traceback
        traceback.print_exc()
    return prog 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import Program [as 別名]
def __create_program(self):
        f = open('ant_tsp.cl', 'r');
        fstr = ''.join(f.readlines())
        f.close()
        ocl_kernel_path = os.path.join(os.path.dirname(os.path.abspath('../../' + __file__)), 'kernel').replace(' ', '\\ ')
        options = [
            '-D', 'ANT_COUNT={}'.format(self.__ants),
            '-D', 'NODE_COUNT={}'.format(self.__node_count),
            '-D', 'ALPHA={}'.format(self.__alpha),
            '-D', 'BETA={}'.format(self.__beta),
            '-D', 'EVAPORATION={}'.format(self.__evaporation),
            '-D', 'Q={}'.format(self.__q),
            '-I', ocl_kernel_path
        ]
        self.__prg = cl.Program(self.__ctx, fstr).build(options); 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import Program [as 別名]
def tile_cl(queue, a, shape, dim=4):
    """Horizontally and vertically tile a GPUArray.

    Parameters
    ----------
    queue
        PyOpenCL queue.
    a : gpuarray
        GPUArray to tile.
    shape : tuple
        Number of vertical and horizontal tiles.

    Returns
    -------
    gpuarray
        Tiled GPUArray.
    """
    m, n = a.shape
    repy, repx = shape
    b = cl_array.empty(queue, (m * repy, n * repx), dtype=float32)

    kernel = cl.Program(queue.context, """
    __kernel void tile_cl(__global float *a, __global float *b, unsigned m, unsigned n, unsigned repx, unsigned repy)
    {
        int idx = get_global_id(0);
        int idy = get_global_id(1);
        int id  = idy * (n * repx) + idx;

        b[id] = a[(idy % m) * n + (idx % n)];
    }
    """).build()

    kernel.tile_cl(queue, (n * repx, m * repy), None, a.data, b.data, uint32(m), uint32(n), uint32(repx), uint32(repy))

    return b 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import Program [as 別名]
def __getitem__(self, index):
        if self.vcl_sub_context.has_program(index):
            program = self.vcl_sub_context.get_program_from_string(index)
            return get_pyopencl_object(program)
        else:
            raise IndexError("Program %s not in context" % index) 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import Program [as 別名]
def __setitem__(self, index, program):
        if self.vcl_sub_context.has_program(index):
            log.warn("Program %s already in context, so overwriting")
            del self[index]
        self.vcl_sub_context.add_program(program.int_ptr, index) 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import Program [as 別名]
def __delitem__(self, index):
        if self.vcl_sub_context.has_program(index):
            self.vcl_sub_context.delete_program(index)
        else:
            raise IndexError("Program %s not in context" % index)