Create deviceQuery.py

Chapter03/deviceQuery.py

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+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 = { 5.0 : 128, 5.1 : 128, 5.2 : 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]))