Python pyopencl.get_platforms方法代碼示例

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import get_platforms [as 別名]
def get_opencl_devices():
    """Return a list of available OpenCL devices.

    Raises ImportError if OpenCL is not found.
    Raises IOError if no OpenCL devices are found.
    """
    device_strings = []
    platforms = pyopencl.get_platforms() #@UndefinedVariable
    for i, platform in enumerate(platforms):
        devices = platform.get_devices()
        for j, device in enumerate(devices):
            device_strings.append('[%d-%d] %s' % 
                (i, j, merge_whitespace(device.name)[:25]))
    if len(device_strings) == 0:
        raise IOError
    return device_strings 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import get_platforms [as 別名]
def _get_device(self, gpuid):
        """Return GPU devices, context, and queue."""
        all_platforms = cl.get_platforms()
        platform = next((p for p in all_platforms if
                         p.get_devices(device_type=cl.device_type.GPU) != []),
                        None)
        if platform is None:
            raise RuntimeError('No OpenCL GPU device found.')
        my_gpu_devices = platform.get_devices(device_type=cl.device_type.GPU)
        context = cl.Context(devices=my_gpu_devices)
        if gpuid > len(my_gpu_devices)-1:
            raise RuntimeError(
                'No device with gpuid {0} (available device IDs: {1}).'.format(
                    gpuid, np.arange(len(my_gpu_devices))))
        queue = cl.CommandQueue(context, my_gpu_devices[gpuid])
        if self.settings['debug']:
            print("Selected Device: ", my_gpu_devices[gpuid].name)
        return my_gpu_devices, context, queue 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import get_platforms [as 別名]
def __setstate__(self, state):
        self.atomics_flavor = state["atomics_flavor"]
        self.fortran_abi = state["fortran_abi"]
        self.pyopencl_module_name = state["pyopencl_module_name"]

        dev_id = state["device_id"]
        if dev_id is None:
            self.device = None
        else:
            import pyopencl as cl
            matches = [
                dev
                for plat in cl.get_platforms()
                for dev in plat.get_devices()
                if dev.hashable_model_and_version_identifier == dev_id]

            if matches:
                self.device = matches[0]
            else:
                raise LoopyError(
                        "cannot unpickle device '%s': not found"
                        % dev_id) 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import get_platforms [as 別名]
def query_devices(c_p):
    import pyopencl as cl
    platforms = cl.get_platforms()
    devices = platforms[0].get_devices()

    data = []
    for pidx in range(len(platforms)):
            devices = platforms[pidx].get_devices()
            for didx in range(len(devices)):
                data.append((pidx, didx))
    c_p.send(data)

## OpenCLGAWorker is a spawned process which is supposed to run OpenCLGA on a
#  target device which is decided by OpenCLGAClient.
#  @param platform_index Platform index which is queried and assigned by Client.
#  @param device_index Device index which is queried and assigned by Client.
#  @param ip The IP of server.
#  @param port The listening port of server.
#  @var exit_evt A event to wait in method run(), and will be set when receving
#                'exit' command, or terminating.
#  @var uuid A unique ID for UI to identify the worker.
#  @var running A varialbe shared by client & worker process to identify if worker
#               is running or not. 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import get_platforms [as 別名]
def __init__(self, device=0, platform=0, iterations=7, compiler_options=None):
        """Creates OpenCL device context and reads device properties

        :param device: The ID of the OpenCL device to use for benchmarking
        :type device: int

        :param iterations: The number of iterations to run the kernel during benchmarking, 7 by default.
        :type iterations: int
        """
        if not cl:
            raise ImportError("Error: pyopencl not installed, please install e.g. using 'pip install pyopencl'.")

        self.iterations = iterations
        #setup context and queue
        platforms = cl.get_platforms()
        self.ctx = cl.Context(devices=[platforms[platform].get_devices()[device]])

        self.queue = cl.CommandQueue(self.ctx, properties=cl.command_queue_properties.PROFILING_ENABLE)
        self.mf = cl.mem_flags
        #inspect device properties
        self.max_threads = self.ctx.devices[0].get_info(cl.device_info.MAX_WORK_GROUP_SIZE)
        self.compiler_options = compiler_options or []

        #collect environment information
        dev = self.ctx.devices[0]
        env = dict()
        env["platform_name"] = dev.platform.name
        env["platform_version"] = dev.platform.version
        env["device_name"] = dev.name
        env["device_version"] = dev.version
        env["opencl_c_version"] = dev.opencl_c_version
        env["driver_version"] = dev.driver_version
        env["iterations"] = self.iterations
        env["compiler_options"] = compiler_options
        self.env = env
        self.name = dev.name 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import get_platforms [as 別名]
def initialize_opencl(self, cl_platform_index=None, cl_device_index=None, ctx=None, queue=None):
        assert HAVE_PYOPENCL, 'PyOpenCL is not installed'

        global default_platform_index
        global default_device_index
        
        if ctx is None and queue is None:
            if cl_platform_index is None:
                if default_platform_index is not None and default_device_index is not None:
                    self.cl_platform_index = default_platform_index
                    self.cl_device_index = default_device_index
                    self.devices = [pyopencl.get_platforms()[self.cl_platform_index].get_devices()[self.cl_device_index]]
                    self.ctx = pyopencl.Context(self.devices)
                else:
                    self.ctx = pyopencl.create_some_context(interactive=False)
            else:
                self.cl_platform_index = cl_platform_index
                self.cl_device_index = cl_device_index
                self.devices = [pyopencl.get_platforms()[self.cl_platform_index].get_devices()[self.cl_device_index]]
                self.ctx = pyopencl.Context(self.devices)
            self.queue = pyopencl.CommandQueue(self.ctx)
        else:
            assert cl_platform_index is None and cl_device_index is None
            self.ctx = ctx
            self.queue = queue
        
        self.max_wg_size = self.ctx.devices[0].get_info(pyopencl.device_info.MAX_WORK_GROUP_SIZE) 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import get_platforms [as 別名]
def get_cl_device_list():
    
    device_indexes = []
    for platform_index, platform in enumerate(pyopencl.get_platforms()):
        for device_index,device in enumerate(platform.get_devices()):
            device_indexes.append((device.name, platform_index, device_index))
    
    return device_indexes 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import get_platforms [as 別名]
def has_amd():
	for platform in cl.get_platforms():
		if 'amd' in platform.name.lower():
			return True
	return False 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import get_platforms [as 別名]
def __init__(self, device_index, options):
		super(OpenCLMiner, self).__init__(device_index, options)
		self.output_size = 0x100

		self.device = cl.get_platforms()[options.platform].get_devices()[device_index]
		self.device_name = self.device.name.strip('\r\n \x00\t')
		self.frames = 30

		self.worksize = self.frameSleep= self.rate = self.estimated_rate = 0
		self.vectors = False

		self.adapterIndex = None
		if ADL and 'amd' in self.device.platform.name.lower() and self.device.type == cl.device_type.GPU:
			with adl_lock:
				self.adapterIndex = self.get_adapter_info()[self.device_index].iAdapterIndex 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import get_platforms [as 別名]
def _get_device(gpuid):
    """Return GPU devices, context, and queue."""
    platform = cl.get_platforms()
    platf_idx = find_nonempty(platform)
    my_gpu_devices = platform[platf_idx].get_devices(
                                                device_type=cl.device_type.GPU)
    context = cl.Context(devices=my_gpu_devices)
    queue = cl.CommandQueue(context, my_gpu_devices[gpuid])
    if VERBOSE:
        print(("Selected Device: ", my_gpu_devices[gpuid].name))
    return my_gpu_devices, context, queue 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import get_platforms [as 別名]
def get_all_info(self):
        platforms = pyopencl.get_platforms()
        s = "\n-------- available devices -----------\n"
        for p in platforms:
            s += "platform: \t%s\n"%p.name
            printNames = [["CPU", pyopencl.device_type.CPU],
                          ["GPU", pyopencl.device_type.GPU]]
            for name, identifier in printNames:
                s += "device type: \t%s\n"%name
                try:
                    for d in p.get_devices(identifier):
                        s += "\t%s \n"%d.name
                except:
                    s += "nothing found: \t%s\n"%name

        infoKeys = ['NAME', 'GLOBAL_MEM_SIZE',
                    'GLOBAL_MEM_SIZE', 'MAX_MEM_ALLOC_SIZE',
                    'LOCAL_MEM_SIZE', 'IMAGE2D_MAX_WIDTH',
                    'IMAGE2D_MAX_HEIGHT', 'IMAGE3D_MAX_WIDTH',
                    'IMAGE3D_MAX_HEIGHT', 'IMAGE3D_MAX_DEPTH',
                    'MAX_WORK_GROUP_SIZE', 'MAX_WORK_ITEM_SIZES']

        s += "\n-------- currently used device -------\n"

        for k in infoKeys:
            s += "%s: \t  %s\n"%(k, self.get_info(k))
        return s 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import get_platforms [as 別名]
def printplatforms(self):
        i=0
        for platform in cl.get_platforms():
            print('Platform %d - Name %s, Vendor %s' %(i,platform.name,platform.vendor))
            i+=1 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import get_platforms [as 別名]
def printfullinfo(self):
        print('\n' + '=' * 60 + '\nOpenCL Platforms and Devices')
        i=0
        for platform in cl.get_platforms():
            print('=' * 60)
            print('Platform %d - Name: ' %i + platform.name)
            print('Platform %d - Vendor: ' %i + platform.vendor)
            print('Platform %d - Version: ' %i + platform.version)
            print('Platform %d - Profile: ' %i + platform.profile)

            for device in platform.get_devices():
                print(' ' + '-' * 56)
                print(' Device - Name: ' \
                      + device.name)
                print(' Device - Type: ' \
                      + cl.device_type.to_string(device.type))
                print(' Device - Max Clock Speed: {0} Mhz' \
                      .format(device.max_clock_frequency))
                print(' Device - Compute Units: {0}' \
                      .format(device.max_compute_units))
                print(' Device - Local Memory: {0:.0f} KB' \
                      .format(device.local_mem_size / 1024.0))
                print(' Device - Constant Memory: {0:.0f} KB' \
                      .format(device.max_constant_buffer_size / 1024.0))
                print(' Device - Global Memory: {0:.0f} GB' \
                      .format(device.global_mem_size / 1073741824.0))
                print(' Device - Max Buffer/Image Size: {0:.0f} MB' \
                      .format(device.max_mem_alloc_size / 1048576.0))
                print(' Device - Max Work Group Size: {0:.0f}' \
                      .format(device.max_work_group_size))
                print('\n')
        i+=1 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import get_platforms [as 別名]
def print_device_info() :
    print('\n' + '=' * 60 + '\nOpenCL Platforms and Devices')

    for platform in cl.get_platforms():
        print('=' * 60)
        print('Platform - Name: ' + platform.name)
        print('Platform - Vendor: ' + platform.vendor)
        print('Platform - Version: ' + platform.version)
        print('Platform - Profile: ' + platform.profile)

    for device in platform.get_devices():
        print(' ' + '-' * 56)
        print(' Device - Name: ' \
        + device.name)
        print(' Device - Type: ' \
        + cl.device_type.to_string(device.type))
        print(' Device - Max Clock Speed: {0} Mhz'\
        .format(device.max_clock_frequency))
        print(' Device - Compute Units: {0}'\
        .format(device.max_compute_units))
        print(' Device - Local Memory: {0:.0f} KB'\
        .format(device.local_mem_size/1024.0))
        print(' Device - Constant Memory: {0:.0f} KB'\
        .format(device.max_constant_buffer_size/1024.0))
        print(' Device - Global Memory: {0:.0f} GB'\
        .format(device.global_mem_size/1073741824.0))
        print(' Device - Max Buffer/Image Size: {0:.0f} MB'\
        .format(device.max_mem_alloc_size/1048576.0))
        print(' Device - Max Work Group Size: {0:.0f}'\
        .format(device.max_work_group_size))
        print('\n') 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import get_platforms [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