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The parallel running function in PyCharm allows running code blocks concurrently, improving development and testing efficiency. By enabling this feature and setting the number of processes, you can: Speed up the development and debugging process. Reduce test suite run time. Take full advantage of multi-core processors. Simplify the structure and maintenance of complex code.
Parallel running in PyCharm
PyCharm is a powerful Python integrated development environment (IDE). This includes a feature called "Parallel Run". It allows you to split your code into multiple chunks that run concurrently, making development and testing more efficient.
How to Enable Parallel Running
Enabling parallel running in PyCharm is very simple:
Benefits of parallel running
Using parallel running provides the following benefits:
Usage Example
To use parallel execution, you can use ThreadPoolExecutor
from the concurrent.futures
module kind. Here is an example that creates a thread pool and uses it to run three functions in parallel:
<code class="python">from concurrent.futures import ThreadPoolExecutor def task(n): return n * n with ThreadPoolExecutor() as executor: results = executor.map(task, range(1, 4)) for result in results: print(result)</code>
In this example, ThreadPoolExecutor
uses three threads to execute the task in parallel
function and store the results in the results
list.
Notes
When using parallel running, you need to consider the following considerations:
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