Python 分散式計算模組 Parallel
特性:
- 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.)
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Example #1:
sum_primes.py
#!/usr/bin/python
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File: sum_primes.py
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Author: VItalii Vanovschi
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Desc: This program demonstrates parallel computations with pp module
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It calculates the sum of prime numbers below a given integer in parallel
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Parallel Python Software: http://www.parallelpython.com
import
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