Description
Adds interactive parallel computing support for Python, especially in IPython and Jupyter workflows. It helps distribute work across multiple processes or machines while keeping a notebook or shell-driven workflow.
Use it for experiments, teaching, and research jobs that benefit from parallel execution. Parallel code can consume significant CPU, memory, and network resources, so configure worker counts carefully.