May 2017
Intermediate to advanced
270 pages
6h 18m
English
Content preview from Python High Performance - Second Edition
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Automatic parallelism
As we mentioned earlier, normal Python programs have trouble achieving thread parallelism because of the GIL. So far, we worked around this problem using separate processes; starting a process, however, takes significantly more time and memory than starting a thread.
We also saw that sidestepping the Python environment allowed us to achieve a 2x speedup on an already fast Cython code. This strategy allowed us to achieve lightweight parallelism but required a separate compilation step. In this section, we will further explore this strategy using special libraries that are capable of automatically translating our code into a parallel version for efficient execution.
Examples of packages that implement automatic parallelism ...
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