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Answer: Parallelizing sequential/iterative models is difficult because each iteration depends on the results of the previous one, but techniques like checkpointing and asynchronous updates can help.
Sequential/iterative models, such as gradient-based optimization algorithms or Markov chain Monte Carlo methods, are difficult to parallelize because each iteration depends on the results of the previous one. Strategies to mitigate these challenges include checkpointing, where intermediate results are saved, and asynchronous updates, where different parts of the model are updated independently.
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Discuss the challenges of parallelizing sequential/iterative models. Why can it be difficult to parallelize these models, and what are some strategies to mitigate these challenges? Provide examples of models that are inherently sequential and explain why parallelization is problematic for them.
A
Sequential/iterative models can be easily parallelized without any challenges.
B
Parallelizing sequential/iterative models is difficult because each iteration depends on the results of the previous one, but techniques like checkpointing and asynchronous updates can help.
C
The only challenge in parallelizing sequential/iterative models is the increased computational cost.
D
Sequential/iterative models cannot be parallelized at all.
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