Added benchmarks for numpy-like array indexing

bench.py

@@ -0,0 +1,98 @@
+import numpy as np
+import mcl
+import pytest
+import time
+from mcl.machine_types import i32, i64, intp, memref
+from mcl.vm import Type
+from mcl.ndarray import Array, DType, Int32
+
+def to_custom_indices(val):
+    if not isinstance(val, np.ndarray):
+        return val
+
+    res_val = Array(dtype=DType(Int32), data=memref.alloc(tuple(map(intp, val.shape)), i32))
+    for idx in np.ndindex(val.shape):
+        res_val[idx] = i32(int(val[idx]))
+    return res_val
+
+@pytest.fixture(scope='module')
+def setup_arrays():
+    np_array = np.random.rand(1000, 1000)
+    return np_array, to_custom_indices(np_array)
+
+def benchmark_function(func):
+    start_time = time.time()
+    func()
+    end_time = time.time()
+    return end_time - start_time
+
+lambda_getitem_func = lambda arr, indices: arr[*indices]
+
+@pytest.mark.parametrize("operation", [
+    ("getitem_single_element", (500, 500)),
+    ("getitem_row", (500, slice(None))),
+    ("getitem_column", (slice(None), 500)),
+    ("getitem_simple_slice", (slice(100, 200), slice(100, 200))),
+    ("getitem_fancy_indexing_single_array", (np.random.randint(100, size=(5)),)),
+    # TODO: Fix this error, seems to be an issue with indexing, needs it's test too
+    # ("getitem_fancy_indexing_2d_array", (np.random.randint(100, size=(10, 20)), np.random.randint(200, size=(10, 20)))),
+    # TODO: Fix this error, seems to be an issue with indexing, needs it's test too
+    # ("getitem_fancy_indexing_2d_array", (np.random.randint(100, size=(10, 20)), np.random.randint(200, size=(10, 10, 20)))),
+])
+def test_getitem_indexing_operations(setup_arrays, operation):
+    name, indices = operation
+    np_array, custom_array = setup_arrays
+
+    np_time = benchmark_function(lambda: lambda_getitem_func(np_array, indices))
+    print(f"NumPy - {name}: {np_time:.6f} seconds")
+
+    custom_indices = tuple([to_custom_indices(ary) for ary in indices])
+
+    custom_time = benchmark_function(lambda: lambda_getitem_func(custom_array, custom_indices))
+    print(f"CustomArray - {name}: {custom_time:.6f} seconds")
+
+    if np_time > 0:
+        factor = custom_time / np_time
+        print(f"Factor for {name} (CustomArray / NumPy): {factor:.2f}")
+    else:
+        print(f"NumPy result for {name} is zero, cannot compute performance factor.")
+
+    assert np_time > 0
+    assert custom_time > 0
+
+def lambda_setitem_func(arr, indices, val):
+    arr[*indices] = val
+
+@pytest.mark.parametrize("operation", [
+    ("setitem_single_element", (500, 500), 10),
+    ("setitem_row", (500, slice(None)), 10),
+    ("setitem_column", (slice(None), 500), 10),
+    ("setitem_simple_slice", (slice(100, 200), slice(100, 200)), 10),
+
+    # TODO: Array setitem is not supported in mcl right now
+    # Once it's supported, need benchmarks with array values
+    # ("setitem_fancy_indexing_single_array", (np.random.randint(100, size=(5)),), 10),
+    # ("setitem_fancy_indexing_2d_array", (np.random.randint(100, size=(10, 20)), np.random.randint(200, size=(10, 20)))),
+    # ("setitem_fancy_indexing_2d_array", (np.random.randint(100, size=(10, 20)), np.random.randint(200, size=(10, 10, 20)))),
+])
+def test_setitem_indexing_operations(setup_arrays, operation):
+    name, indices, val = operation
+    np_array, custom_array = setup_arrays
+
+    np_time = benchmark_function(lambda: lambda_setitem_func(np_array, indices, val))
+    print(f"NumPy - {name}: {np_time:.6f} seconds")
+
+    custom_indices = tuple([to_custom_indices(ary) for ary in indices])
+    custom_val = i32(val)
+
+    custom_time = benchmark_function(lambda: lambda_setitem_func(custom_array, custom_indices, custom_val))
+    print(f"CustomArray - {name}: {custom_time:.6f} seconds")
+
+    if np_time > 0:
+        factor = custom_time / np_time
+        print(f"Factor for {name} (CustomArray / NumPy): {factor:.2f}")
+    else:
+        print(f"NumPy result for {name} is zero, cannot compute performance factor.")
+
+    assert np_time > 0
+    assert custom_time > 0