distributed processing

简明释义

分布式处理

英英释义

例句

1.The company implemented distributed processing to enhance the speed of data analysis.

公司实施了分布式处理以提高数据分析的速度。

2.Cloud computing relies heavily on distributed processing for scalable applications.

云计算在可扩展应用中严重依赖于分布式处理。

3.By utilizing distributed processing, we can manage larger datasets more efficiently.

通过利用分布式处理，我们可以更高效地管理更大的数据集。

4.The research team used distributed processing to simulate complex models across multiple servers.

研究团队使用分布式处理在多个服务器上模拟复杂模型。

5.With distributed processing, tasks can be completed in parallel, reducing overall computation time.

通过分布式处理，任务可以并行完成，从而减少总体计算时间。

作文

In the modern era of technology, the need for efficient data processing has become increasingly important. One of the most effective methods to achieve this is through distributed processing, which refers to the technique of dividing a large task into smaller subtasks that can be processed simultaneously across multiple computing nodes. This approach not only enhances the speed of computation but also improves the overall performance of systems handling vast amounts of data. 分布式处理是指将一个大型任务划分为多个可以同时在多个计算节点上处理的小任务的技术。这种方法不仅提高了计算速度，还改善了处理大量数据的系统的整体性能。The concept of distributed processing is rooted in the idea that no single machine can handle all the computational demands of today's applications. By leveraging the power of multiple machines, we can significantly reduce the time required to process information. For example, in cloud computing environments, data can be distributed across various servers, allowing for parallel processing. This means that while one server is busy handling one part of the data, another server can simultaneously work on a different part, leading to faster results. 分布式处理的概念源于这样一种思想：没有一台机器能够处理当今应用程序的所有计算需求。通过利用多台机器的能力，我们可以显著减少处理信息所需的时间。Moreover, distributed processing is particularly beneficial in scenarios where data is generated at a high velocity and volume. For instance, in big data analytics, the sheer amount of data being collected can overwhelm a single system. By employing distributed processing, organizations can efficiently analyze large datasets by breaking them down into manageable chunks that are processed in parallel. This capability is essential for real-time data analysis, where timely insights can lead to better decision-making. 此外，分布式处理在数据以高速和大容量生成的场景中尤为有益。例如，在大数据分析中，收集的数据量之大可能会使单个系统不堪重负。通过采用分布式处理，组织可以通过将大型数据集分解为可管理的块并并行处理，从而有效地分析大型数据集。这种能力对于实时数据分析至关重要，因为及时的见解可以导致更好的决策。Another significant advantage of distributed processing is its scalability. As the demand for processing power increases, organizations can simply add more nodes to their network to enhance their capabilities. This flexibility allows businesses to adapt to changing workloads without needing to invest in expensive hardware upgrades. Furthermore, distributed processing can also provide fault tolerance; if one node fails, others can take over the tasks, ensuring that the system remains operational. 分布式处理的另一个重要优点是其可扩展性。随着对处理能力的需求增加，组织只需向其网络添加更多节点即可增强其能力。这种灵活性使企业能够适应不断变化的工作负载，而无需投资昂贵的硬件升级。此外，分布式处理还可以提供容错性；如果一个节点失败，其他节点可以接管任务，确保系统保持运行。In conclusion, distributed processing is a transformative approach to data management that offers numerous benefits, including improved speed, efficiency, scalability, and fault tolerance. As technology continues to evolve, the adoption of distributed processing will likely become more widespread, enabling organizations to harness the full potential of their data and drive innovation. In a world where data is king, understanding and implementing distributed processing will be crucial for success. 总之，分布式处理是一种变革性的数据管理方法，提供了许多好处，包括提高速度、效率、可扩展性和容错性。随着技术的不断发展，分布式处理的采用可能会变得更加普遍，使组织能够充分利用其数据的潜力并推动创新。在一个数据为王的世界中，理解和实施分布式处理将对成功至关重要。