new_contents: import numpy as np
  from numba import cuda
  from numba.cuda.testing import CUDATestCase, skip_on_cudasim
  from numba.tests.support import override_config
  from numba.core.errors import NumbaPerformanceWarning
  import warnings
  
  
  def numba_dist_cuda(a, b, dist):
      len = a.shape[0]
      for i in range(len):
          dist[i] = a[i] * b[i]
  
  
  def numba_dist_cuda2(a, b, dist):
      len = a.shape[0]
      len2 = a.shape[1]
      for i in range(len):
          for j in range(len2):
              dist[i, j] = a[i, j] * b[i, j]
  
  
  @skip_on_cudasim('Large data set causes slow execution in the simulator')
  class TestCUDAWarnings(CUDATestCase):
      def test_inefficient_kernel(self):
          a = np.random.rand(1024 * 1024 * 32).astype('float32')
          b = np.random.rand(1024 * 1024 * 32).astype('float32')
          dist = np.zeros(a.shape[0]).astype('float32')
  
          sig = 'void(float32[:], float32[:], float32[:])'
          with override_config('CUDA_LOW_OCCUPANCY_WARNINGS', 1):
              with warnings.catch_warnings(record=True) as w:
                  warnings.simplefilter('always', NumbaPerformanceWarning)
                  cuda_func = cuda.jit(sig)(numba_dist_cuda)
                  cuda_func[1,1](a, b, dist)
                  self.assertEqual(w[0].category, NumbaPerformanceWarning)
                  self.assertIn('Grid size', str(w[0].message))
                  self.assertIn('2 * SM count', str(w[0].message))
  
      def test_efficient_kernel(self):
          a = np.random.rand(1024 * 1024 * 128).astype('float32').\
              reshape((1024 * 1024, 128))
          b = np.random.rand(1024 * 1024 * 128).astype('float32').\
              reshape((1024 * 1024, 128))
          dist = np.zeros_like(a)
  
          sig = 'void(float32[:, :], float32[:, :], float32[:, :])'
  
          with override_config('CUDA_LOW_OCCUPANCY_WARNINGS', 1):
              with warnings.catch_warnings(record=True) as w:
                  warnings.simplefilter('always', NumbaPerformanceWarning)
                  cuda_func = cuda.jit(sig)(numba_dist_cuda2)
                  cuda_func[256,256](a, b, dist)
                  self.assertEqual(len(w), 0)
  
subject: Add new file to test kernel efficiency warnings