Document how to use cudf.pandas in tandem with multiprocessing

We need to arrange that cudf.pandas.install() is run on the workers,
this requires that we programmatically install the metapath loader in
our script. Unfortunately, passing an initializer function to the pool
startup is not sufficient if any part of the script transitively loads
pandas at the top level.

- Closes #15246

docs/cudf/source/cudf_pandas/usage.md

@@ -26,6 +26,36 @@ From the command line, run your Python scripts with `-m cudf.pandas`:
 python -m cudf.pandas script.py
 ```
 
+### Usage in tandem with
+[`multiprocessing`](https://docs.python.org/3/library/multiprocessing.html)
+or
+[`concurrent.futures`](https://docs.python.org/3/library/concurrent.futures.html)
+process pools
+
+To use a pool of workers (for example
+[`multiprocessing.Pool`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.Pool)
+or
+[`concurrent.futures.ProcessPoolExecutor`](https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ProcessPoolExecutor))
+in your script with `cudf.pandas`, the `cudf.pandas` module must be
+loaded on the worker processes, as well as by the controlling script.
+The most foolproof way to do this is to programmatically install
+`cudf.pandas` at the top of your script, before anything else.
+For example
+
+```python
+# This is equivalent to python -m cudf.pandas, but will run on the
+# workers too. These two lines must run before pandas is imported,
+# either directly or transitively.
+import cudf.pandas
+cudf.pandas.install()
+
+from multiprocessing import Pool
+
+with Pool(4) as pool:
+    # use pool here
+    ...
+```
+
 ## Understanding performance - the `cudf.pandas` profiler
 
 `cudf.pandas` will attempt to use the GPU whenever possible and fall