For computational physics stuff it can.

You throw out pythons jit compiler, use llvm’s one instead, avoid pitfalls like for loops in python and use the iterate function from numpy instead.

You’re basically calling mostly c-libraries plus it gets compiled to machine code :).

Quick example i drummed up with AI:

1️⃣ Parallel C Version with OpenMP

Here’s how you could modify the previous C program:

include

include

include

int main() { const long N = 10000000; const int T = 10; const double dt = 0.01; const double g = -9.81;

double *x = (double*)malloc(N * sizeof(double));
double *v = (double*)malloc(N * sizeof(double));

// Initialize arrays
for(long i = 0; i < N; i++) {
    x[i] = 0.0;
    v[i] = (double)rand() / RAND_MAX;
}

double start = omp_get_wtime();

for(int t = 0; t < T; t++) {
    #pragma omp parallel for
    for(long i = 0; i < N; i++) {
        v[i] += g * dt;
        x[i] += v[i] * dt;
    }
}

double end = omp_get_wtime();
printf("Parallel C (OpenMP) simulation done in %.3f seconds\n", end - start);

free(x);
free(v);
return 0;

}

Compile with:

gcc -O3 -fopenmp -march=native particle_sim.c -o particle_sim

⸻

2️⃣ Comparison to Python + Numba

Version Notes Expected Timing (10M particles, 10 steps) C (gcc -O3, single-thread) Serial ~1.0 s C + OpenMP Parallelized ~0.2–0.4 s (8–16 cores) Python + Numba (parallel=True) Multi-threaded ~0.25–0.5 s

Observations: 1. Both C + OpenMP and Numba parallel can fully utilize multiple cores. 2. Numba parallel loops (prange) are very similar to OpenMP in effect: • The inner loop is distributed across threads. • Automatic thread management is done by Numba. 3. Performance of Python + Numba can actually match parallel C for simple loops. 4. Slight differences may occur due to: • Thread scheduling • Memory alignment / false sharing • CPU vectorization efficiency

⸻

✅ Key Takeaways • For single-threaded, C is faster than Python + Numba (~20–50% difference). • For multi-threaded, Python + Numba can be almost as fast as parallel C for large array computations. • For massively parallel tasks (millions of particles), GPU acceleration (CuPy, Numba CUDA) can outperform even parallel C on CPU.

⸻

If you want, I can make a benchmark Python script that runs: 1. Python + Numba (parallel) 2. Python + GPU (CuPy) 3. C + OpenMP (via subprocess)

and prints all timings side by side. This would give a full realistic comparison.

Do you want me to do that?