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's standard library does not include a `parallelmap` module. However, you can achieve parallel execution using the `multiprocessing` module, which is part of the standard library. Here's an example of how you can run a function `f` over a list of inputs across all CPU cores using `multiprocessing.Pool`:

```python
import multiprocessing

def f(x):
    # Your function definition here
    return x * x

if __name__ == '__main__':
    items = [1, 2, 3, 4, 5]
    with multiprocessing.Pool() as pool:
        results = pool.map(f, items)
    print(results)
```

If you want a more convenient interface similar to `parallelmap.run(f, items)`, you can create a simple wrapper function:

```python
import multiprocessing

def parallelmap_run(f, items):
    with multiprocessing.Pool() as pool:
        return pool.map(f, items)

def f(x):
    # Your function definition here
    return x * x

if __name__ == '__main__':
    items = [1, 2, 3, 4, 5]
    results = parallelmap_run(f, items)
    print(results)
```

This will distribute the work across all available CPU cores.