parallel search

简明释义

平行搜索

英英释义

例句

1.Researchers used a parallel search method to analyze multiple datasets simultaneously.

研究人员使用并行搜索方法同时分析多个数据集。

2.The software utilizes a parallel search technique to enhance its performance.

该软件利用并行搜索技术来提升其性能。

3.A parallel search across different databases can yield more comprehensive results.

在不同数据库之间进行并行搜索可以获得更全面的结果。

4.The algorithm performs a parallel search to quickly find the optimal solution.

该算法执行并行搜索以快速找到最佳解决方案。

5.In data mining, a parallel search can significantly reduce processing time.

在数据挖掘中，并行搜索可以显著减少处理时间。

作文

In the realm of computer science and data processing, the concept of parallel search (并行搜索) has gained significant attention. This technique allows for the simultaneous exploration of multiple paths or solutions, thereby enhancing efficiency and reducing the time required to find answers to complex problems. The fundamental idea behind parallel search is to divide a large problem into smaller sub-problems that can be solved concurrently, leveraging the power of modern multi-core processors and distributed computing systems.One of the most prominent applications of parallel search is in the field of artificial intelligence, particularly in algorithms used for game playing, optimization problems, and machine learning. For instance, when an AI agent is tasked with playing a game like chess, it needs to evaluate numerous potential moves and their outcomes. By employing parallel search, the agent can assess multiple branches of the game tree at once, significantly speeding up the decision-making process.Moreover, parallel search is also utilized in database querying and information retrieval systems. Traditional search methods might take considerable time to sift through vast amounts of data to find relevant information. However, with parallel search, queries can be executed across different segments of the database simultaneously. This not only improves response times but also enhances user experience by providing quicker access to information.In addition to its applications in AI and databases, parallel search plays a crucial role in scientific research. Researchers often deal with massive datasets, and analyzing them can be a daunting task. By applying parallel search techniques, scientists can conduct experiments and simulations more rapidly, leading to faster discoveries and advancements in various fields such as genomics, climate modeling, and physics.Furthermore, the rise of cloud computing has further propelled the effectiveness of parallel search. With the ability to harness the computational power of multiple servers, organizations can perform extensive searches across distributed systems without the constraints of local hardware limitations. This scalability is essential for businesses that require real-time data processing and analytics.However, implementing parallel search is not without challenges. Developers must consider issues such as synchronization, data consistency, and load balancing to ensure that the parallel processes work harmoniously. Additionally, not all problems are suitable for parallelization; some may inherently depend on sequential processing, which can limit the effectiveness of parallel search.In conclusion, the concept of parallel search (并行搜索) represents a significant advancement in the way we approach problem-solving in computing. Its applications span various domains, including artificial intelligence, database management, and scientific research, making it a vital tool in today's data-driven world. As technology continues to evolve, the importance of parallel search will likely increase, paving the way for more efficient and effective solutions to complex challenges.