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Explain the role of parallelism in computational thinking.

Parallelism in computational thinking involves breaking down a problem into smaller parts that can be solved simultaneously.

In more detail, parallelism is a key concept in computational thinking that allows for the efficient solving of complex problems. It involves dividing a problem into sub-problems that can be solved concurrently, rather than sequentially. This is particularly useful in situations where the problem is large or complex, and solving it sequentially would be time-consuming or inefficient.

Parallelism is often used in computer science and programming, where tasks can be divided among multiple processors to speed up the computation process. For example, in a multi-core processor, each core can work on a different part of the problem at the same time, leading to faster results. This is known as parallel computing.

The concept of parallelism also extends to the way we think about and approach problem-solving in general. By breaking down a problem into smaller, manageable parts, we can tackle each part individually and in parallel, which can often lead to more efficient and effective solutions. This is a key aspect of computational thinking, which is all about using strategies and techniques from computer science to solve problems more effectively.

However, it's important to note that not all problems can be solved using parallelism. Some problems require sequential processing, where each step depends on the outcome of the previous step. In these cases, parallelism would not be beneficial and could even lead to incorrect results. Therefore, part of computational thinking involves recognising when parallelism can be used effectively and when it cannot.

In conclusion, parallelism plays a crucial role in computational thinking, allowing for more efficient problem-solving in many cases. However, it's also important to understand its limitations and to recognise when it's appropriate to use.

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