|
f = np.vectorize(lambda x: x + 1) |
Current implementation:
f = np.vectorize(lambda x: x + 1)
Recommended replacement:
np.vectorize is often misunderstood as a performance optimization tool, but it does not provide true vectorization. Internally, it wraps a Python function in an element-wise loop, dispatching a separate Python function call for each item in the array. This approach introduces significant runtime overhead, especially for large arrays, and does not benefit from NumPy's optimized C-level operations.
In contrast, using direct NumPy expressions like x + 1 leverages low-level vectorized execution, memory-efficient loops, and SIMD acceleration. These operations are orders of magnitude faster and consume less memory.
Since the lambda function here performs a simple addition, using x + 1 directly is both semantically equivalent and far more performant. Benchmark tests consistently show 10× to 100× speedup with native NumPy expressions compared to np.vectorize.
ece2cmor3/ece2cmor3/lpjg2cmor.py
Line 746 in 7ad5618
Current implementation:
f = np.vectorize(lambda x: x + 1)Recommended replacement:
np.vectorize is often misunderstood as a performance optimization tool, but it does not provide true vectorization. Internally, it wraps a Python function in an element-wise loop, dispatching a separate Python function call for each item in the array. This approach introduces significant runtime overhead, especially for large arrays, and does not benefit from NumPy's optimized C-level operations.
In contrast, using direct NumPy expressions like x + 1 leverages low-level vectorized execution, memory-efficient loops, and SIMD acceleration. These operations are orders of magnitude faster and consume less memory.
Since the lambda function here performs a simple addition, using x + 1 directly is both semantically equivalent and far more performant. Benchmark tests consistently show 10× to 100× speedup with native NumPy expressions compared to np.vectorize.