Advanced Techniques for Sharing Lists and Dictionaries Between Processes in Python Parallel Computing
Parallel processing can drastically speed up tasks by distributing workloads across multiple CPU cores. Python’s multiprocessing module makes parallel processing straightforward, but one challenge it presents is sharing data like lists and dictionaries between processes.
By default, each process in Python has its own memory space, so simply creating and modifying shared data structures like lists or dictionaries across processes is not as simple as in single-threaded applications. However, Python provides several methods to share data between processes. In this blog, we’ll explore these methods in detail.
Why is Data Sharing Between Processes Challenging?
In parallel processing, each process operates in its own address space. This means that variables and data structures in one process are independent of others. While threads in Python share the same memory, processes do not. This isolation prevents processes from directly sharing data, making parallel programming more complex.
Methods to Share Data Between Processes
- Using
multiprocessing.Queue - Using
multiprocessing.Pipe