According to the ThreadPoolExecutor documentation [1], the default
number of maximum workers min(32, os.cpu_count() + 4). The problem is
that we execute many pipelines from nano, on which os.cpu_count()
reports 4, so we can only have up to 8 concurrent workers by default.
This leads to the fact that on pipelines with many UEs (e.g.,
AW2S-AmariUE), we don't ping/iperf on all UEs at the same time.

Fix this by increasing the number of workers to 64. This should be fine,
as there is almost no real work being done in the Workers (only opening
a shell and execute programs such as ping/iperf).

[1] https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor