computational effort

简明释义

计算工作量

英英释义

例句

1.With parallel processing, we can significantly cut down on computational effort 计算工作量 required for data analysis.

通过并行处理，我们可以显著减少数据分析所需的计算工作量 计算工作量。

2.Reducing the computational effort 计算工作量 is essential for real-time applications.

减少计算工作量 计算工作量对于实时应用至关重要。

3.The new model was designed to achieve better accuracy with less computational effort 计算工作量.

新模型的设计旨在以更少的计算工作量 计算工作量实现更好的准确性。

4.The algorithm requires significant computational effort 计算工作量 to process large datasets efficiently.

该算法需要大量的计算工作量 计算工作量才能有效处理大数据集。

5.The researchers optimized their code to minimize computational effort 计算工作量 during simulations.

研究人员优化了他们的代码，以最小化模拟过程中的计算工作量 计算工作量。

作文

In the age of technology, where data is generated at an unprecedented rate, the term computational effort (计算工作量) has become increasingly significant. This phrase refers to the amount of computational resources and time required to perform a specific task, often in the context of algorithms, simulations, or data processing. Understanding computational effort (计算工作量) is crucial for researchers and practitioners in various fields, including computer science, engineering, and data analysis.One of the key aspects of computational effort (计算工作量) is its relation to algorithm efficiency. Different algorithms can solve the same problem but with varying levels of efficiency. For instance, consider the problem of sorting a list of numbers. A simple algorithm like bubble sort may require a significant amount of computational effort (计算工作量) as it compares each element with every other element, resulting in a time complexity of O(n^2). In contrast, more advanced algorithms like quicksort can sort the same list with much less computational effort (计算工作量), achieving a time complexity of O(n log n). This difference highlights the importance of choosing the right algorithm based on the computational effort (计算工作量) it demands.Moreover, computational effort (计算工作量) is not only about time but also about the resources consumed during computation. For example, when running simulations in scientific research, the computational effort (计算工作量) can be substantial, requiring powerful computers and extensive memory. As a result, researchers must often balance the accuracy of their models with the computational effort (计算工作量) they are willing to invest. This trade-off is particularly evident in fields like climate modeling or molecular dynamics, where high-fidelity simulations can take weeks or even months to complete.In recent years, advancements in technology have introduced new ways to optimize computational effort (计算工作量). The rise of parallel computing and cloud computing allows for distributing tasks across multiple processors or machines, significantly reducing the computational effort (计算工作量) required for large-scale problems. For instance, big data analytics often involves processing vast datasets, and leveraging cloud resources can lead to faster insights while minimizing the local computational effort (计算工作量).Furthermore, machine learning and artificial intelligence have also transformed the landscape of computational effort (计算工作量). Training complex models, such as deep neural networks, demands immense computational effort (计算工作量) due to the sheer volume of data and the complexity of the algorithms involved. Researchers are continuously exploring ways to make these processes more efficient, whether through better algorithms, hardware acceleration, or optimization techniques.In conclusion, the concept of computational effort (计算工作量) plays a vital role in modern computing and research. It encompasses not only the time taken to execute algorithms but also the resources consumed during the process. As technology evolves, understanding and optimizing computational effort (计算工作量) will remain essential for tackling increasingly complex problems across various domains. By recognizing the significance of computational effort (计算工作量), we can make informed decisions that enhance our ability to innovate and solve real-world challenges effectively.

在科技时代，数据以空前的速度生成，术语计算工作量（computational effort）变得越来越重要。这个短语指的是执行特定任务所需的计算资源和时间，通常与算法、模拟或数据处理相关。理解计算工作量（computational effort）对计算机科学、工程和数据分析等各个领域的研究人员和从业者至关重要。计算工作量（computational effort）一个关键方面是它与算法效率的关系。不同的算法可以解决相同的问题，但效率水平各异。例如，考虑对数字列表进行排序的问题。像冒泡排序这样的简单算法可能需要大量的计算工作量（computational effort），因为它将每个元素与其他每个元素进行比较，导致时间复杂度为O(n^2)。相比之下，更先进的算法如快速排序可以用更少的计算工作量（computational effort）来排序同一列表，达到O(n log n)的时间复杂度。这种差异突显了根据所需的计算工作量（computational effort）选择合适算法的重要性。此外，计算工作量（computational effort）不仅涉及时间，还涉及计算过程中消耗的资源。例如，在科学研究中运行模拟时，计算工作量（computational effort）可能非常庞大，需要强大的计算机和大量内存。因此，研究人员通常必须在模型的准确性和他们愿意投入的计算工作量（computational effort）之间取得平衡。这种权衡在气候建模或分子动力学等领域尤为明显，高保真模拟可能需要数周甚至数月才能完成。近年来，技术的进步引入了优化计算工作量（computational effort）的新方法。并行计算和云计算的兴起使得将任务分配到多个处理器或机器上成为可能，大大减少了大型问题所需的计算工作量（computational effort）。例如，大数据分析通常涉及处理庞大的数据集，利用云资源可以更快地获得洞察，同时最小化本地的计算工作量（computational effort）。此外，机器学习和人工智能也改变了计算工作量（computational effort）的格局。训练复杂模型，例如深度神经网络，由于数据量庞大和算法复杂性，需求巨大的计算工作量（computational effort）。研究人员不断探索使这些过程更高效的方法，无论是通过更好的算法、硬件加速还是优化技术。总之，计算工作量（computational effort）的概念在现代计算和研究中发挥着至关重要的作用。它不仅包括执行算法所需的时间，还包括在此过程中消耗的资源。随着技术的发展，理解和优化计算工作量（computational effort）将继续是应对各个领域日益复杂问题的重要组成部分。通过认识到计算工作量（computational effort）的重要性，我们可以做出明智的决策，从而有效提升我们创新和解决现实世界挑战的能力。