import pycuda
import pycuda.driver as drv
drv.init()

print ('CUDA device query (PyCUDA version) \n')

print ('Detected {} CUDA Capable device(s) \n'.format(drv.Device.count()))

for i in range(drv.Device.count()):
    
    gpu_device = drv.Device(i)
    print ('Device {}: {}'.format( i, gpu_device.name() ) )
    compute_capability = float( '%d.%d' % gpu_device.compute_capability() )
    print ('\t Compute Capability: {}'.format(compute_capability))
    print ('\t Total Memory: {} megabytes'.format(gpu_device.total_memory()//(1024**2)))
    
    # The following will give us all remaining device attributes as seen 
    # in the original deviceQuery.
    # We set up a dictionary as such so that we can easily index
    # the values using a string descriptor.
    
    device_attributes_tuples = gpu_device.get_attributes().items() 
    device_attributes = {}
    
    for k, v in device_attributes_tuples:
        device_attributes[str(k)] = v
    
    num_mp = device_attributes['MULTIPROCESSOR_COUNT']
    
    # Cores per multiprocessor is not reported by the GPU!  
    # We must use a lookup table based on compute capability.
    # See the following:
    # http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capabilities
    
    cuda_cores_per_mp = { 3.0 : 192, 3.2 : 192, 3.5 : 192, 3.7 : 192, 5.0 : 128, 5.1 : 128, 5.2 : 128, 5.3 : 128, 6.0 : 64, 6.1 : 128,\
         6.2 : 128, 7.0 : 64, 7.1 : 64, 7.2 : 64, 7.3 : 64, 7.4 : 64, 7.5 : 64}[compute_capability]
    
    print ('\t ({}) Multiprocessors, ({}) CUDA Cores / Multiprocessor: {} CUDA Cores'.format(num_mp, cuda_cores_per_mp, num_mp*cuda_cores_per_mp))
    
    device_attributes.pop('MULTIPROCESSOR_COUNT')
    
    for k in device_attributes.keys():
        print ('\t {}: {}'.format(k, device_attributes[k]))