⚡️ Speed up function _gridmake2 by 1,039%

[codeflash-ai]

📄 1,039% (10.39x) speedup for _gridmake2 in code_to_optimize/discrete_riccati.py

⏱️ Runtime : 1.06 milliseconds → 93.3 microseconds (best of 96 runs)

📝 Explanation and details

The optimized code achieves a 10x speedup (1038%) by replacing NumPy's high-level array operations with JIT-compiled explicit loops via Numba's @njit decorator.

Key Optimizations

1. Numba JIT Compilation with @njit(cache=True)

• Eliminates Python interpreter overhead by compiling to machine code
• The cache=True flag stores compiled code between runs, avoiding recompilation cost
• Particularly effective for loops, which NumPy operations like tile, repeat, and column_stack use internally but with Python overhead

2. Preallocated Output Arrays with Explicit Loops

• Original approach: np.column_stack([np.tile(x1, x2.shape[0]), np.repeat(x2, x1.shape[0])]) creates three temporary arrays (tile result, repeat result, then column_stack result)
• Optimized approach: Pre-allocates a single output array with exact size (x1.shape[0] * x2.shape[0], 2) and fills it directly via nested loops
• Eliminates intermediate array allocations and memory copies

3. Direct Memory Access

• Line profiler shows the original code spends 77.9% of time in np.column_stack and related operations
• The optimized version replaces these with direct index assignments (out[idx, 0] = x1[i]), which Numba compiles to efficient memory writes

Performance Context

From function_references, _gridmake2 is called recursively within gridmake() when building cartesian products of multiple arrays. For d > 2 dimensions, the function is called d-1 times in a loop. This means:

• Hot path impact: The 10x speedup compounds across multiple calls when expanding 3+ dimensional grids
• Memory efficiency: For large input arrays, avoiding temporary allocations becomes increasingly important

Test Case Suitability

The optimization excels when:

• Building cartesian products of moderately-sized vectors (e.g., 100-1000 elements each)
• Called repeatedly in loops (as in the recursive gridmake case)
• Input arrays have consistent dtypes (Numba's type specialization works best here)

The line profiler confirms the bottleneck was NumPy's high-level operations, which this optimization directly addresses through low-level compiled code.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 31 Passed
🌀 Generated Regression Tests	🔘 None Found
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Click to see Existing Unit Tests

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_gridmake2.py::TestGridmake2EdgeCases.test_both_empty_arrays	64.3μs	2.12μs	2927%✅
test_gridmake2.py::TestGridmake2EdgeCases.test_empty_arrays_raise_or_return_empty	65.0μs	2.42μs	2588%✅
test_gridmake2.py::TestGridmake2EdgeCases.test_float_dtype_preserved	65.0μs	2.04μs	3083%✅
test_gridmake2.py::TestGridmake2EdgeCases.test_integer_dtype_preserved	65.4μs	1.96μs	3239%✅
test_gridmake2.py::TestGridmake2NotImplemented.test_1d_first_2d_second_raises	48.7μs	27.2μs	78.6%✅
test_gridmake2.py::TestGridmake2NotImplemented.test_both_2d_raises	48.9μs	28.0μs	74.4%✅
test_gridmake2.py::TestGridmake2With1DArrays.test_basic_two_element_arrays	69.0μs	3.33μs	1971%✅
test_gridmake2.py::TestGridmake2With1DArrays.test_different_length_arrays	66.1μs	2.25μs	2839%✅
test_gridmake2.py::TestGridmake2With1DArrays.test_float_arrays	65.3μs	2.08μs	3036%✅
test_gridmake2.py::TestGridmake2With1DArrays.test_larger_arrays	65.1μs	2.04μs	3087%✅
test_gridmake2.py::TestGridmake2With1DArrays.test_negative_values	65.1μs	1.96μs	3226%✅
test_gridmake2.py::TestGridmake2With1DArrays.test_result_shape	65.1μs	2.04μs	3089%✅
test_gridmake2.py::TestGridmake2With1DArrays.test_single_element_arrays	38.6μs	2.12μs	1716%✅
test_gridmake2.py::TestGridmake2With1DArrays.test_single_element_with_multi_element	65.7μs	1.88μs	3404%✅
test_gridmake2.py::TestGridmake2With2DFirst.test_2d_first_1d_second	41.4μs	2.42μs	1614%✅
test_gridmake2.py::TestGridmake2With2DFirst.test_2d_multiple_columns	12.5μs	2.00μs	527%✅
test_gridmake2.py::TestGridmake2With2DFirst.test_2d_single_column	41.0μs	2.04μs	1911%✅
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_2d_and_1d_matches_numpy	43.1μs	2.71μs	1492%✅
test_gridmake2_torch.py::TestGridmake2TorchCPU.test_both_1d_matches_numpy	66.8μs	2.58μs	2486%✅

To edit these changes git checkout codeflash/optimize-_gridmake2-mjq1m0q5 and push.