如何在Python中有效地并行化复杂的数据处理，以提高程序的性能？我有大量的数据需要使用特定的函数process_data（）进行处理。由于处理是计算密集型的，我想使用多处理和线程来并行化它，以有效地利用多个CPU内核。
下面是我的代码的一个简化示例：

import numpy as np
import multiprocessing as mp
import threading

def process_data(data_chunk):
    # Implement your complex data processing logic for a data chunk here
    processed_data = data_chunk * 2  # Just an example, replace this with your own logic
    return processed_data

def parallel_processing_with_multiprocessing(data, num_processes):
    pool = mp.Pool(num_processes)
    processed_data = pool.map(process_data, data)
    pool.close()
    pool.join()
    return processed_data

def parallel_processing_with_threading(data, num_threads):
    results = []
    threads = []
    chunk_size = len(data) // num_threads

    for i in range(num_threads):
        start_idx = i * chunk_size
        end_idx = start_idx + chunk_size if i < num_threads - 1 else len(data)
        thread = threading.Thread(target=lambda idx_range: results.extend(process_data(data[idx_range[0]:idx_range[1]])), args=((start_idx, end_idx),))
        threads.append(thread)

    for thread in threads:
        thread.start()

    for thread in threads:
        thread.join()

    return results

if __name__ == "__main__":
    data = np.random.rand(1000000)  # Sample data: A large amount of data
    num_processes = mp.cpu_count()  # Use as many processes as available CPU cores
    num_threads = 4  # Use 4 threads for parallel processing

    # Compare the execution times for parallel processing using multiprocessing and threading
    processed_data_multiprocessing = parallel_processing_with_multiprocessing(data, num_processes)
    processed_data_threading = parallel_processing_with_threading(data, num_threads)

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我想知道这种并行数据处理方法是否有效，以及如何进一步优化它。有没有更好的策略或其他Python库可以用来解决这类问题？提前感谢您的帮助！