Nowadays software written in python finds applications in broad range of the categories including business logic, data analysis and scientific calculations.

Cross-architecture portability and interoperability (x86, x86-64, etc.).

Cross-platform portability and interoperability (Windows, Linux, Unix, Mac OS X).

SHA based authentication for network connections.

Dynamic allocation of computational resources (consequence of auto-discovery and fault-tolerance).

Auto-discovery of computational resources.

Fault-tolerance (if one of the nodes fails tasks are rescheduled on others).

Dynamic load balancing (jobs are distributed between processors at runtime).

Low overhead for subsequent jobs with the same function (transparent caching is implemented to decrease the overhead).

Dynamic processors allocation (number of worker processes can be changed at runtime).

Automatic detection of the optimal configuration (by default the number of worker processes is set to the number of effective processors).

Easy to understand and implement job-based parallelization technique (easy to convert serial application in parallel).Parallel execution of python code on SMP and clusters.Parallel Python is an open source and cross-platform module written in pure python It is light, easy to install and integrate with other python software. Parallel Python is a python module which provides mechanism for parallel execution of python code on SMP (systems with multiple processors or cores) and clusters (computers connected via network).

Python emulator for mac