Differences Between Python Parallel Threads and Processes
Below, are the Differences between Python Parallel Threads and Processes in table format.
Features | Parallel Threads | Parallel Processes |
|---|---|---|
Memory Usage | Share the same memory space | Each has its memory space |
Isolation | Low | High |
Context Switching | Faster | Slower |
Communication | Easy | Complex |
CPU-bound Tasks | Not suitable (GIL limitation) | Suitable |
I/O-bound Tasks | Suitable | Suitable |
| Resource Consumption | Less | More |
Concurrency | Lightweight | Heavier |
| Synchronization | Simpler | More complex |
Conclusion
In conclusion, parallel threads and processes offer concurrent execution but differ in the their memory usage, isolation, context switching and suitability for the different types of the tasks. Threads are lightweight and suitable for the I/O-bound tasks in while processes provide the better isolation and are suitable for the CPU-bound tasks.
Differences between Python Parallel Threads and Processes
In Python, parallelism is a powerful concept used to execute multiple tasks concurrently by improving performance and efficiency. The Two common approaches to parallelism in Python are parallel threads and parallel processes. While both achieve concurrent execution they have distinct characteristics and are suitable for the different use cases.