Let's consider a hypothetical algorithm which has . Assume that there is an inner loop that involves 1,000 bytes of Python code. When processing 10,000 objects, we're executing 100 billion Python operations. This is the essential processing budget. We can try to allocate as many processes and threads as we feel might be helpful, but the processing budget can't change.

The individual CPython bytecode doesn't have a simple execution timing. However, a long-term average on a Mac OS X laptop shows that we can expect about 60 MB of code to be executed per second. This means that our 100 billion bytecode operation will take about 1,666 seconds, or 28 minutes.

If we have a dual processor, four-core computer, then we might cut the elapsed time to 25 percent of the original total: 7 minutes. This presumes that we can partition the work into four (or more) independent OS processes.

The important consideration here is that our budget of 100 billion bytecodes can't be changed. Parallelism...