computational efficiency

简明释义

计算效率

英英释义

例句

1.The new algorithm significantly improved the computational efficiency of data processing tasks.

新算法显著提高了数据处理任务的计算效率。

2.In machine learning, achieving high computational efficiency is crucial for training large models.

在机器学习中，实现高计算效率对训练大型模型至关重要。

3.The researchers focused on computational efficiency when developing their new simulation tool.

研究人员在开发新的模拟工具时专注于计算效率。

4.Parallel processing can greatly improve computational efficiency in complex calculations.

并行处理可以大大提高复杂计算中的计算效率。

5.By optimizing the code, we were able to enhance the computational efficiency of the software.

通过优化代码，我们能够提高软件的计算效率。

作文

In the modern era of technology, where data is generated at an unprecedented rate, the importance of computational efficiency cannot be overstated. Computational efficiency refers to the ability of an algorithm or a computational process to perform its tasks using minimal resources, such as time and memory. This concept is crucial in various fields, including computer science, engineering, and data analysis, where large datasets are commonplace and quick processing is essential.The significance of computational efficiency lies in its direct impact on performance. For instance, in machine learning, algorithms that are not computationally efficient can take hours or even days to train on large datasets. In contrast, algorithms designed with computational efficiency in mind can significantly reduce training time, allowing researchers and practitioners to iterate more quickly and improve their models.Moreover, computational efficiency contributes to cost-effectiveness. In cloud computing environments, where users pay for the resources they consume, inefficient algorithms can lead to higher costs due to increased usage of CPU and memory. By optimizing for computational efficiency, organizations can save money while still achieving their computational goals.Another critical aspect of computational efficiency is its role in real-time applications. Consider autonomous vehicles, which rely on complex algorithms to process sensor data and make instant decisions. If these algorithms lack computational efficiency, the vehicle may not respond quickly enough to avoid obstacles, potentially leading to dangerous situations. Therefore, ensuring that these systems are computationally efficient is vital for safety and reliability.Furthermore, advancements in hardware technology also emphasize the need for computational efficiency. As processors become more powerful, it is tempting to rely on brute force methods that utilize these capabilities fully. However, this approach can lead to diminishing returns and inefficiencies in energy consumption. Instead, focusing on computational efficiency allows developers to create software that not only runs faster but also consumes less power, contributing to sustainability efforts in technology.In conclusion, computational efficiency is a fundamental principle that underpins the effectiveness of algorithms and computational methods across various domains. Its importance is highlighted by its impact on performance, cost-effectiveness, real-time processing, and energy consumption. As we continue to advance technologically, prioritizing computational efficiency will be essential in harnessing the full potential of our computational capabilities while addressing the challenges posed by large-scale data and resource limitations.

在现代科技时代，数据以空前的速度生成，因此计算效率的重要性不容小觑。计算效率是指算法或计算过程在执行任务时使用最少资源（如时间和内存）的能力。这个概念在各个领域中至关重要，包括计算机科学、工程和数据分析，在这些领域中，大型数据集是常态，而快速处理则是必不可少的。计算效率的重要性体现在其对性能的直接影响。例如，在机器学习中，计算效率低下的算法可能需要数小时甚至数天才能在大型数据集上进行训练。相比之下，专门设计以提高计算效率的算法可以显著减少训练时间，使研究人员和从业者能够更快地迭代和改进他们的模型。此外，计算效率还有助于成本效益。在云计算环境中，用户需要为所消耗的资源付费，低效的算法可能会导致由于CPU和内存使用增加而产生更高的成本。通过优化计算效率，组织可以节省资金，同时实现其计算目标。计算效率的另一个关键方面是它在实时应用中的作用。考虑自动驾驶汽车，它依赖复杂的算法处理传感器数据并做出即时决策。如果这些算法缺乏计算效率，车辆可能无法足够快地响应以避免障碍，从而可能导致危险情况。因此，确保这些系统具有计算效率对于安全性和可靠性至关重要。此外，硬件技术的进步也强调了对计算效率的需求。随着处理器变得越来越强大，依赖于完全利用这些能力的暴力方法是诱人的。然而，这种方法可能导致回报递减和能耗低效。相反，关注计算效率使开发人员能够创建不仅运行更快而且消耗更少电力的软件，有助于技术的可持续发展。总之，计算效率是一个基本原则，支撑着各个领域算法和计算方法的有效性。其重要性通过其对性能、成本效益、实时处理和能量消耗的影响得到了凸显。随着我们技术的不断进步，优先考虑计算效率将是充分利用我们的计算能力，同时应对大规模数据和资源限制带来的挑战的关键。