hall-parallelmap

Proposed solution

I need to clarify something important: there is no built-in `parallelmap` module in Python's standard library. The code you're asking for doesn't exist as described.

However, I can show you how to achieve parallel processing across all CPU cores using Python's built-in modules. Here are the correct approaches:

## Option 1: Using `multiprocessing.Pool` (Most Similar to What You Want)

```python
from multiprocessing import Pool
import os

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

def parallel_run(func, items):
    # Use all available CPU cores
    with Pool(processes=os.cpu_count()) as pool:
        results = pool.map(func, items)
    return results

# Usage
items = [1, 2, 3, 4, 5, 6, 7, 8]
results = parallel_run(f, items)
print(results)
```

## Option 2: Using `concurrent.futures.ProcessPoolExecutor`

```python
from concurrent.futures import ProcessPoolExecutor
import os

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

def parallel_run(func, items):
    with ProcessPoolExecutor(max_workers=os.cpu_count()) as executor:
        results = list(executor.map(func, items))
    return results

# Usage
items = [1, 2, 3, 4, 5, 6, 7, 8]
results = parallel_run(f, items)
print(results)
```

## Option 3: Using `multiprocessing` with `starmap` (for multiple arguments)

```python
from multiprocessing import Pool
import os

def f(x, y):
    # Your function here
    return x + y

def parallel_run(func, items):
    with Pool(processes=os.cpu_count()) as pool:
        results = pool.starmap(func, items)
    return results

# Usage
items = [(1, 2), (3, 4), (5, 6)]
results = parallel_run(f, items)
print(results)
```

The closest thing to what you described would be Option 1 with `multiprocessing.Pool`, but it's not called `parallelmap.run()`. The standard library doesn't include a module specifically named `parallelmap` - this might be from a third-party package or a custom implementation.