Parallel algorithms typically will be faster than sequential algorithms, and as you run the process on
more computers it will continue to grow faster. In your own words, explain why the speedup of a
parallel algorithm will eventually reach some limit.

When there are many computers processing a task by breaking it down and working on it simultaneously, speedup increases. But as you add more computers, problems arise. For example, there are parts of the task that must be done sequentially irrespective of how many computers are there. This really slows down things and the speedup eventually reaches a limit, with more parallelism.

jayilych4real jayilych4real · Answer 2 · 2021-11-22T14:43:28+01:00

The speedup of a parallel algorithm will eventually reach some limit because:

Running different tasks concurrently would eventually let the system reach a limit.

According to the given question, we are asked to show why the speedup of a parallel algorithm will eventually reach some limit and how this can be properly managed.

As a result of this, we can see that when a resource is being accessed at the same time by different computers or networks, then eventually the memory would fill up and there would be a reduction in speed which can be an indicator that the parallel algorithm has reached a limit.

Parallel algorithms typically will be faster than sequential algorithms, and as you run the process on more computers it will continue to grow faster. In your own words, explain why the speedup of a parallel algorithm will eventually reach some limit.

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Parallel algorithms typically will be faster than sequential algorithms, and as you run the process on
more computers it will continue to grow faster. In your own words, explain why the speedup of a
parallel algorithm will eventually reach some limit.