Python uses GeekConcurrent to implement quantitative programming

In modern software development, concurrent programming is the key to improving program performance and responsiveness. The Python language is loved by programmers for its concise and easy-to-read syntax and powerful library support, but Python also faces some challenges when dealing with high concurrency tasks. This article will introduce coroutine concurrent programming in Python and how to use the GeekConcurrent library to implement vector-oriented programming, helping readers get started quickly through examples and code.

1. The basics of Python coroutine concurrency

1. The concept of coroutines

Coroutine is a method used in Python to handle concurrent operations, which is particularly suitable for handling I/O-bound and event-driven problems, such as network requests, file I/O, user input, etc. Compared with threads, coroutines are single-threaded. The operating system does not know its existence. They are controlled by the Python interpreter. The advantage of coroutines is that they avoid the overhead of multi-threaded locks and multiple threads, and can better utilize Python's global interpreter locks (GILs).

The basic operations of coroutines include start, yield and termination. Python coroutines evolved from the generator foundation. Since the introduction of the asyncio module in Python 3.4, coroutines have gradually become one of the main ways for Python to handle concurrency. Python 3.5 began to introduce the async/await keyword, making the writing of coroutines more concise and clear.

2. Basic use of coroutines

Here is a simple asyncio coroutine example:

import asyncio
 
async def main():
    print('Hello')
    await (1)
    print('World')
 
(main())

In this example, main is a coroutine function defined using asyncdef. In coroutine functions, you can use the await expression to suspend the execution of the coroutine and wait for an operation that may take some time to complete, such as an I/O operation. In this example, we use await (1) to simulate a time-consuming operation. Note that await expression cannot be used in ordinary functions, it can only be used in coroutine functions. Finally, we use (main()) to run this coroutine.

3. Generator coroutine

Generators in Python were initially designed for iterative operations, but later they were enhanced so that they could be used as coroutines. This is achieved by using yield expressions (rather than yield statements) and send methods in the generator.

Here is an example of a simple generator coroutine:

def simple_coroutine():
    print('Coroutine started')
    x = yield
    print('Coroutine received:', x)
 
my_coro = simple_coroutine()
next(my_coro)  # Start coroutinemy_coro.send(42)  # Send value to coroutine

In this example, simple_coroutine is a generator function that pauses execution at the yield expression. We first create a generator object my_coro, and then use the next function to start the coroutine. Then we use the send method to send the value back to the coroutine. The coroutine resumes execution from the paused place, and the value of the yield expression is the parameter of the send method.

2. GeekConcurrent vector-oriented programming

1. Introduction to GeekConcurrent

GeekConcurrent is a high-performance parallel library that provides a more concise and easy-to-understand way to build parallelized code. It allows developers to easily implement multi-threaded and concurrent programming through similar methods, while supporting a variety of high-concurrency scenarios. GeekConcurrent provides a set of easy-to-use classes and methods, allowing developers to easily implement multi-threaded, asynchronous tasks, etc.

2. Install and import GeekConcurrent

Before using GeekConcurrent, you need to install it first. You can use pip for installation:

pip install geekconcurrent

After the installation is complete, you can import GeekConcurrent in the Python code:

from geekconcurrent import ForkThread, FutureSet

3. Implement parallel code using GeekConcurrent

Here is an example of using GeekConcurrent to create multithreaded tasks and manage asynchronous results:

from geekconcurrent import ForkThread, FutureSet
import time
 
def my_function(num):
    (2)
    return f"Result from function {num}"
 
# Create multi-threaded tasksmulti_thread = ForkThread(lambda: my_function(6))
print("\nMulti-threaded task completion:", multi_thread.result())

In this example, ForkThread creates a new task and executes on different threads. Use the result method to get the results of the task.

4. Use FutureSet to manage asynchronous results

FutureSet is used to manage the results of multiple asynchronous tasks, which can improve the maintainability and stability of your code. Here is an example using FutureSet:

from geekconcurrent import Future, FutureSet
 
def compute_a():
    a = 3
    return 10 * a
 
def compute_b():
    b = 4
    return 20 * b
 
futureset = FutureSet(Future())
with futureset:
    result_a = compute_a().result()
    result_b = compute_b().result()
 
print(f"Calculation completed:{result_a + result_b}")

In this example, the two tasks compute_a and compute_b will be executed in parallel. With FutureSet, we can synchronize the results of multiple tasks and update the output results immediately after any task is completed.

5. Complex example: Processing video files

When creating video clips, it is often necessary to process multiple video clips simultaneously. Creating multithreaded tasks with GeekConcurrent can quickly complete these parallel tasks, thereby improving overall production efficiency. Here is an example of processing video files:

from geekconcurrent import ForkThread, FutureSet
import time
 
def process_video_chunk(chunk):
    # Simulate and process video clips    (1)
    return f"Processed chunk {chunk}"
 
def process_video():
    signal = Future()
    chunks = [1, 2, 3, 4]  # Video clip list    for chunk in chunks:
        (chunk)  # Put the fragment into the signal queue    
    results = []
    while not signal.is_empty():
        chunk = next(signal)
        result = process_video_chunk(chunk)
        (result)
        (0.5)  # Simulate the waiting time for subsequent tasks    
    return results
 
future_set = FutureSet(Future)
with future_set:
    video1 = ForkThread(lambda: process_video())
    video2 = ForkThread(lambda: process_video())
 
for video in future_set.values():
    print(f"Video processing is completed: {()}")

In this example, we define a process_video_chunk function to simulate processing video clips. The process_video function uses the Future object to manage the processing flow of video clips. We then created two parallel tasks using ForkThread to process two video files. Finally, we use FutureSet to manage the results of these asynchronous tasks.

3. The combination of coroutines and GeekConcurrent

While GeekConcurrent provides powerful multi-threading and concurrent programming support, coroutines are still a very good choice when dealing with I/O-intensive tasks. We can use coroutines in conjunction with GeekConcurrent for more efficient concurrent programming.

Here is an example using a combination of coroutines and GeekConcurrent:

import asyncio
from geekconcurrent import ForkThread, FutureSet
 
async def fetch_data(url):
    # Simulate network requests    await (2)
    return f"Data from {url}"
 
def process_urls(urls):
    tasks = [fetch_data(url) for url in urls]
    loop = asyncio.get_event_loop()
    results = loop.run_until_complete((*tasks))
    return results
 
urls = ["/1", "/2", "/3"]
 
future_set = FutureSet(Future)
with future_set:
    # Use ForkThread to process URLs in parallel    data_task = ForkThread(lambda: process_urls(urls))
 
# Print processing resultsprint(f"Data acquisition is completed: {data_task.result()}")

In this example, we define a fetch_data coroutine function to simulate network requests. Then, we use the process_urls function to process multiple URLs in parallel. In the process_urls function, we use it to perform multiple coroutine tasks concurrently. Finally, we use ForkThread to process these URLs in parallel and use FutureSet to manage the results of asynchronous tasks.

4. Summary

This article introduces coroutine concurrent programming in Python and how to use the GeekConcurrent library to implement vector-oriented programming. Through examples and code, we show how to create and manage coroutine tasks, how to use GeekConcurrent to handle multiple tasks in parallel, and how to use coroutines and GeekConcurrent to achieve more efficient concurrent programming.

Coroutines are very efficient in handling I/O-intensive tasks, while GeekConcurrent provides a simple and easy-to-understand way to implement multi-threaded and concurrent programming. By using both, we can write more efficient and reliable concurrent programs.

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