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February 17, 2026 17:41
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making python fast with Claude
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| (base) ~/dev/tests ❯ cat monte_carlo.py | |
| import time | |
| import random | |
| def monte_carlo_pi(n_samples): | |
| inside = 0 | |
| for _ in range(n_samples): | |
| x = random.random() | |
| y = random.random() | |
| if x * x + y * y <= 1.0: | |
| inside += 1 | |
| return 4.0 * inside / n_samples | |
| start = time.perf_counter() | |
| pi = monte_carlo_pi(10_000_000) | |
| print(f"pi ≈ {pi:.6f} ({time.perf_counter() - start:.3f}s)") | |
| (base) ~/dev/tests ❯ python monte_carlo.py | |
| pi ≈ 3.141391 (0.499s) | |
| (base) ~/dev/tests ❯ claude -p "please make @monte_carlo.py fast" --permission-mode acceptEdits | |
| ## Results | |
| ``` | |
| Before: 0.482s | |
| After: 0.003s | |
| Speedup: ~160x | |
| ``` | |
| `★ Insight ─────────────────────────────────────` | |
| **Three things make this fast:** | |
| 1. **`@njit`** — Numba compiles the function to native machine code via LLVM. The Python interpreter loop overhead (~50-100ns/iteration) disappears entirely. The math becomes raw CPU instructions. | |
| 2. **`prange`** — Replaces `range` with OpenMP parallel iteration. Each CPU core gets a chunk of the 10M samples. With 8+ cores, that's another ~8x on top of the compilation speedup. | |
| 3. **`np.random.random()`** inside `@njit` — Numba intercepts this and uses its own fast PRNG per-thread (no GIL contention, no Python object allocation). Each thread gets an independent random state. | |
| The warmup call (`monte_carlo_pi(1000)`) pays the one-time JIT compilation cost outside the benchmark, so the timed run measures only the actual computation. | |
| `─────────────────────────────────────────────────` | |
| (base) ~/dev/tests ❯ cat monte_carlo.py | |
| import time | |
| import numpy as np | |
| from numba import njit, prange | |
| @njit(parallel=True) | |
| def monte_carlo_pi(n_samples): | |
| inside = 0 | |
| for i in prange(n_samples): | |
| x = np.random.random() | |
| y = np.random.random() | |
| if x * x + y * y <= 1.0: | |
| inside += 1 | |
| return 4.0 * inside / n_samples | |
| # warmup JIT | |
| monte_carlo_pi(1000) | |
| start = time.perf_counter() | |
| pi = monte_carlo_pi(10_000_000) | |
| print(f"pi ≈ {pi:.6f} ({time.perf_counter() - start:.3f}s)") | |
| (base) ~/dev/tests ❯ python monte_carlo.py | |
| OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead. | |
| pi ≈ 3.141548 (0.003s) |
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