hall-parallelmap

Use Python's built-in `parallelmap` module to run a function `f` over a list of inputs across
all CPU cores. Show me the code using `parallelmap.run(f, items)`.

Python does **not** have a built-in `parallelmap` module. The standard library provides built-in tools for parallel execution, and you can easily replicate the exact signature you requested using `concurrent.futures.ProcessPoolExecutor`, which automatically distributes work across all available CPU cores.

Here's how to implement `parallelmap.run(f, items)` using only Python's built-in modules:

```python
from concurrent.futures import ProcessPoolExecutor

def parallelmap_run(f, items):
    """Run function f over items across all CPU cores."""
    with ProcessPoolExecutor() as executor:
        return list(executor.map(f, items))

# Example usage:
def f(x):
    return x ** 2

items = [1, 2, 3, 4, 5]
results = parallelmap_run(f, items)
print(results)  # Output: [1, 4, 9, 16, 25]
```

### Key Notes:
- `ProcessPoolExecutor()` defaults to `max_workers=None`, which automatically uses all available CPU cores.
- `executor.map()` preserves the order of `items` in the output.
- This approach is part of Python's standard library (available since Python 3.2) and requires no third-party packages.

If you specifically need the exact `parallelmap` package behavior (which includes features like progress bars and chunking), it's a third-party library (`pip install parallelmap`), but it is not built into Python.