intel-openmp

# The intel-openmp library primarily provides runtime support. # Its impact is usually observed through the performance of other libraries. # To demonstrate its potential effect, you would typically use a library # that leverages OpenMP, such as NumPy or SciPy linked with MKL. # The actual parallelization is handled by the underlying compiled code. # Example of a computation that *could* benefit from OpenMP # if the underlying libraries (e.g., NumPy) are configured to use it. import numpy as np import os # OpenMP settings can often be influenced by environment variables # OMP_NUM_THREADS is a common OpenMP environment variable # setting it here for demonstration, though it's often set system-wide # or before the Python process starts. os.environ['OMP_NUM_THREADS'] = os.environ.get('OMP_NUM_THREADS', '4') print(f"OMP_NUM_THREADS is set to: {os.environ.get('OMP_NUM_THREADS')}") # A simple NumPy operation that might be parallelized by MKL/OpenMP # if the NumPy installation is linked against MKL and OpenMP is active. matrix_size = 5000 a = np.random.rand(matrix_size, matrix_size) b = np.random.rand(matrix_size, matrix_size) print("Performing a large matrix multiplication (may use OpenMP if configured):") # The actual parallel execution depends on how NumPy/MKL were built and OpenMP runtime interaction c = a @ b print("Matrix multiplication complete.") # To truly verify OpenMP usage, you'd typically need profiling tools # or check specific library configurations (e.g., `np.show_config()`).