diff --git a/pystencils_tests/test_vectorization_specific.py b/pystencils_tests/test_vectorization_specific.py
index 9b9d34694ccd63a4ab3a5aa4ce4f7df23b10f897..065958624df55d79c79643a91aa1cce70fedad4f 100644
--- a/pystencils_tests/test_vectorization_specific.py
+++ b/pystencils_tests/test_vectorization_specific.py
@@ -61,6 +61,7 @@ def test_vectorized_abs(instruction_set, dtype):
 @pytest.mark.parametrize('dtype', ('float', 'double'))
 @pytest.mark.parametrize('instruction_set', supported_instruction_sets)
 def test_strided(instruction_set, dtype):
+    npdtype = np.float64 if dtype == 'double' else np.float32
     type_string = "float64" if dtype == 'double' else "float32"
 
     f, g = ps.fields(f"f, g : {type_string}[2D]")
@@ -77,30 +78,22 @@ def test_strided(instruction_set, dtype):
                                                           default_number_float=type_string)
             ast = ps.create_kernel(update_rule, config=config)
             assert len(warn) == 0
-            
-    ps.show_code(ast)
+    # ps.show_code(ast)
     func = ast.compile()
-    ref_config = pystencils.config.CreateKernelConfig(default_number_float=type_string)
-    ref_func = ps.create_kernel(update_rule, config=ref_config).compile()
+    ref_func = ps.create_kernel(update_rule).compile()
 
-    # For some reason other array creations fail on the emulated ppc pipeline
-    size = (25, 19)
-    arr = np.zeros(size).astype(type_string)
-    for i in range(size[0]):
-        for j in range(size[1]):
-            arr[i, j] = i * j
-
-
-    dst = np.zeros_like(arr, dtype=type_string)
-    ref = np.zeros_like(arr, dtype=type_string)
+    arr = np.random.random((23 + 2, 17 + 2)).astype(npdtype)
+    # print("sum arr: ", np.sum(arr))
+    # print("arr type:  ", arr.dtype)
+    dst = np.zeros_like(arr, dtype=npdtype)
+    ref = np.zeros_like(arr, dtype=npdtype)
 
     func(g=dst, f=arr)
     ref_func(g=ref, f=arr)
-
-    print("dst: ", dst)
-    print("np array: ", arr)
-
-    np.testing.assert_almost_equal(dst[1:-1, 1:-1], ref[1:-1, 1:-1], 13 if dtype == 'double' else 5)
+    
+    # print("dst sum:  ", np.sum(dst))
+    # print("reference sum:  ", np.sum(ref))
+    np.testing.assert_almost_equal(dst, ref, 13 if dtype == 'double' else 5)
 
 
 @pytest.mark.parametrize('dtype', ('float', 'double'))