parallelize

简明释义

英[/ˈpærəˌlɛlaɪz/]美[/ˈpærəˌlɛlaɪz/]

v. 平行放置；使……平行于……；使程序（适合）进行计算

第 三 人 称 单 数 p a r a l l e l i z e s

现 在 分 词 p a r a l l e l i z i n g

过 去 式 p a r a l l e l i z e d

过 去 分 词 p a r a l l e l i z e d

英英释义

单词用法

同义词

反义词

例句

1.It is difficult to parallelize a subsistent sequential algorithm.

对已有的串行算法进行并行化，是一项很困难的工作。

2.At that time it was a LINQ provider that could automatically parallelize queries.

当时它是一个LINQProvider，可以自动地将查询并行化。

3.These algorithms are very easy to parallelize.

这些算法很容易并行执行。

4.The need to parallelize development activities associated with different streams to the maximum extent possible, while minimizing the cross-stream dependencies.

需要最大程度地同时进行与不同流程相关的开发活动，同时又要尽量减小流程之间的相互依赖。

5.The effect is shown graphically in Figure 3: a traditional program would execute the loop sequentially, whereas the OpenMP implementation creates threads to parallelize the for block.

图3中图形化地显示了其效果：传统的程序将连续地执行循环，而OpenMP实现则创建多个线程，从而并行地执行for块。

6.To increase the CommonStore archiving throughput, it is recommended to parallelize the archiving threads.

为了增加CommonStore归档吞吐量，建议将归档线程并行化。

7.Like the 970, it can use the VMX (AltiVec) vector instructions to parallelize arithmetic operations.

与970类似，它可以使用VMX (AltiVec)向量指令并行执行算术运算。

8.This example shows two simple ways to parallelize operations on file directories.

这个范例示范在档案目录上平行处理作业的两个简单方式。

9.There's a reason all the examples of "look how easy it is to parallelize things" tend to use simple arrays and don't ever have allocations or freeing of the objects.

所有关于“并行化如此容易”的理由都倾向于使用简单数组操作做例子，甚至不包含对象的分配和释放。

10.To speed up the rendering process, we should parallelize 并行处理 the image generation.

为了加快渲染过程，我们应该parallelize 并行处理图像生成。

11.By using advanced algorithms, we can parallelize 并行化 the search for optimal solutions.

通过使用高级算法，我们可以parallelize 并行化最佳解决方案的搜索。

12.We need to parallelize 并行处理 the data processing tasks to improve performance.

我们需要parallelize 并行处理数据处理任务以提高性能。

13.The software can parallelize 并行化 computations across multiple cores.

该软件可以parallelize 并行化多个核心上的计算。

14.The new version of the application allows users to parallelize 并行处理 tasks easily.

该应用程序的新版本允许用户轻松地parallelize 并行处理任务。

作文

In today's fast-paced technological world, the efficiency of computational processes has become increasingly important. One of the key concepts that has emerged in the field of computer science is the ability to parallelize tasks. This method allows multiple processes to be executed simultaneously, significantly reducing the time required for complex computations. In this essay, I will discuss the importance of parallelization in modern computing, its applications, and how it can enhance performance across various domains.The concept of parallelize originates from the need to improve processing speeds by dividing a task into smaller, independent sub-tasks that can be executed at the same time. Traditional sequential processing involves executing one task after another, which can lead to bottlenecks, especially when dealing with large datasets or complex algorithms. By contrast, when we parallelize a task, we can take advantage of multi-core processors and distributed computing environments, allowing for more efficient use of resources.One of the most prominent applications of parallelization is in the realm of data analysis. With the advent of big data, organizations are inundated with vast amounts of information that need to be processed quickly to derive meaningful insights. For instance, companies like Google and Facebook utilize parallelize techniques to analyze user data in real-time, enabling them to deliver personalized experiences and targeted advertisements. By breaking down data processing tasks into smaller chunks, these companies can handle millions of queries simultaneously, leading to faster response times and enhanced user satisfaction.Another area where parallelization plays a critical role is in scientific research. Simulations and modeling often require extensive computations that can take days or even weeks to complete using traditional methods. However, by employing parallelize strategies, researchers can distribute their simulations across multiple processors, drastically reducing the time needed to obtain results. For example, climate modeling, which involves complex equations and vast datasets, benefits immensely from parallelization. This allows scientists to make timely predictions about climate change and its potential impacts on our planet.Moreover, parallelize techniques are not limited to high-performance computing environments; they can also be applied in everyday software development. Modern programming languages and frameworks often provide built-in support for parallelization, making it easier for developers to create applications that can efficiently utilize available hardware. For instance, web servers can parallelize incoming requests to serve multiple users simultaneously, improving overall performance and user experience.Despite its advantages, parallelization does come with challenges. Not all tasks can be easily divided into parallel processes, and some may have dependencies that limit the degree of parallelization achievable. Additionally, managing concurrent processes can introduce complexities, such as race conditions and deadlocks, which require careful design and testing to mitigate.In conclusion, the ability to parallelize tasks is a fundamental aspect of modern computing that has transformed the way we approach problem-solving in various fields. From data analysis to scientific research and software development, parallelization enables us to harness the full potential of our computational resources, leading to faster processing times and improved outcomes. As technology continues to evolve, the importance of parallelizing tasks will only grow, making it an essential skill for future generations of computer scientists and engineers.

在当今快节奏的科技世界中，计算过程的效率变得越来越重要。计算机科学领域出现的一个关键概念是能够并行化任务。这种方法允许多个过程同时执行，从而显著减少复杂计算所需的时间。在本文中，我将讨论并行化在现代计算中的重要性、其应用以及它如何在各个领域提升性能。并行化的概念源于通过将任务分解为较小的独立子任务来提高处理速度，这些子任务可以同时执行。传统的顺序处理涉及一个接一个地执行任务，这可能导致瓶颈，尤其是在处理大型数据集或复杂算法时。相比之下，当我们并行化一个任务时，我们可以利用多核处理器和分布式计算环境，从而更有效地使用资源。并行化的一个突出的应用领域是数据分析。随着大数据的出现，组织面临着需要快速处理的大量信息，以提取有意义的见解。例如，谷歌和脸书等公司利用并行化技术实时分析用户数据，使他们能够提供个性化的体验和针对性的广告。通过将数据处理任务分解为较小的块，这些公司可以同时处理数百万个查询，从而提高响应速度和用户满意度。并行化发挥关键作用的另一个领域是科学研究。模拟和建模通常需要大量计算，使用传统方法可能需要几天甚至几周才能完成。然而，通过采用并行化策略，研究人员可以将他们的模拟分布到多个处理器上，从而大幅减少获得结果所需的时间。例如，气候建模涉及复杂的方程和庞大的数据集，极大地受益于并行化。这使科学家能够及时预测气候变化及其对我们星球潜在影响。此外，并行化技术不仅限于高性能计算环境；它们还可以应用于日常软件开发。现代编程语言和框架通常提供内置的并行化支持，使开发人员更容易创建能够有效利用可用硬件的应用程序。例如，Web服务器可以并行化传入请求，以同时为多个用户提供服务，从而提高整体性能和用户体验。尽管有其优势，并行化确实带来了挑战。并非所有任务都可以轻易分解为并行过程，有些任务可能具有限制可实现的并行化程度的依赖关系。此外，管理并发过程可能引入复杂性，例如竞争条件和死锁，这需要仔细设计和测试来缓解。总之，任务并行化的能力是现代计算的一个基本方面，它改变了我们在各个领域解决问题的方法。从数据分析到科学研究和软件开发，并行化使我们能够充分利用计算资源，从而实现更快的处理时间和更好的结果。随着技术的不断发展，并行化任务的重要性只会增加，这使其成为未来计算机科学家和工程师的核心技能。