PP Easy and efficient way to create parallel-enabled applications for SMP computers and clusters
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develop python development cluster development smp development cluster smp

PP Description

Easy and efficient way to create parallel-enabled applications for SMP computers and clusters PP 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).PP is light, easy to install and integrate with other Python software. PP is an open source and cross-platform module written in pure Python.PP provides an easy and efficient way to create parallel-enabled applications for SMP computers and clusters. PP module features cross-platform portability and dynamic load balancing. Thus applications written with PP will parallelize efficiently even on multi-platform and heterogeneous clusters (including clusters running other application with variable CPU loads).NOTE: PP is developed, provided and licensed under the terms of the BSD License. Here are some key features of "PP": · Parallel execution of python code on SMP and clusters · Easy to understand and implement job-based parallelization technique (easy to convert serial application in parallel) · Automatic detection of the optimal configuration (by default the number of worker processes is set to the number of effective processors) · Dynamic processors allocation (number of worker processes can be changed at runtime) · Low overhead for subsequent jobs with the same function (transparent caching is implemented to decrease the overhead) · Dynamic load balancing (jobs are distributed between processors at runtime) · Fault-tolerance (if one of the nodes fails tasks are rescheduled on others) · Auto-discovery of computational resources · Dynamic allocation of computational resources (consequence of auto-discovery and fault-tolerance) · SHA based authentication for network connections · Cross-platform portability and interoperability (Windows, Linux, Unix, Mac OS X) · Cross-architecture portability and interoperability (x86, x86-64, etc.) · Open source Requirements: · Python 2.3 or later

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