run.py

import numpy as np
import time

def numpy_benchmark():
    results = {}

    # Matrix multiplication (BLAS-backed, uses multi-threading internally)
    A = np.random.rand(1000, 1000)
    B = np.random.rand(1000, 1000)
    start = time.time()
    C = np.dot(A, B)
    results["Matrix Multiplication (1000x1000)"] = round(time.time() - start, 2)

    # Element-wise math (vectorized ops)
    x = np.random.rand(10_000_000)
    start = time.time()
    y = np.sin(x) + np.log(x + 1)
    results["Element-wise Computation (10M)"] = round(time.time() - start, 2)

    # Summation (uses reduction across array)
    start = time.time()
    total = np.sum(x)
    results["Sum Reduction (10M)"] = round(time.time() - start, 2)

    return results

if __name__ == "__main__":
    results = numpy_benchmark()
    print("\n🧪 NumPy Benchmark Results")
    for task, t in results.items():
        print(f"{task}: {t} seconds")