What does pycharm mean when running in parallel?

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:

1. Open PyCharm and load your project.
2. Navigate to the Run menu.
3. Click on the "Edit Configuration" option.
4. In the "Run/Debug Configuration" dialog box, select "Python" as the runner type.
5. In the "Parallel" tab, check the "Enable parallel runs" checkbox.
6. Set the number of processes you want to run in parallel.

Benefits of parallel running

Using parallel running provides the following benefits:

• Increased development speed: By running multiple parts of your code simultaneously, you can speed up the development and debugging process.
• Improve testing efficiency: Parallelizing your test suites can significantly reduce run times, allowing you to identify bugs faster.
• Take full advantage of multi-core processors: Modern computers often have multiple cores, and running in parallel can take advantage of these cores to improve performance.
• Simplify complex code: Splitting code into chunks that run in parallel simplifies the structure and maintenance of complex applications.

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:

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)

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:

• Shared resources: Parallel When resources are shared between running blocks, synchronization may be required to avoid race conditions.
• Debugging Difficulty: Debugging parallel code can be more complex than debugging code that runs sequentially.
• Potential Overhead: Creating and managing threads for parallel runs may introduce some overhead, especially for small tasks.

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