Python Concurrency with asyncio

6 Handling CPU-bound work

This chapter covers

• The multiprocessing library
• Creating process pools to handle CPU-bound work
• Using async and await to manage CPU-bound work
• Using MapReduce to solve a CPU-intensive problem with asyncio
• Handling shared data between multiple processes with locks
• Improving the performance of work with both CPU- and I/O-bound operations

Until now, we’ve been focused on performance gains we can get with asyncio when running I/O-bound work concurrently. Running I/O-bound work is asyncio’s bread and butter, and with the way we’ve written code so far, we need to be careful not to run any CPU-bound code in our coroutines. This seems like it severely limits asyncio, but the library is more versatile than just handling I/O-bound ...