Multi-Paradigm Programming with Modern C++ - Distributing the Work

The video tutorial explores the use of parallel tasks to optimize lengthy operations on large collections. It discusses the performance costs of task creation and the importance of efficient task partitioning. The tutorial provides a practical example of implementing a parallel search algorithm, emphasizing the need to balance task size with available threads. It concludes with a performance analysis, highlighting common issues in task parallelism and suggesting optimization strategies, such as dynamic thread scaling.

Why is it not advisable to schedule a separate task for each element in a large collection?

• Because creating a task is an expensive operation.

• Because it would require too much memory.

• Because it would increase the complexity of the code.

• Because it would lead to data inconsistency.

What is the primary factor to consider when calculating the optimal chunk size for parallel tasks?

• The type of data being processed.

• The number of available threads.

• The processing power of the machine.

• The number of elements in the collection.

In the practical example, what is used to manage tasks efficiently without creating additional vectors?

• Pointers

• Iterators

• Linked lists

• Arrays

What is the expected duration of the task execution in the example, considering the concurrency level?

• 4 seconds

• 8 seconds

• 10 seconds

• 6 seconds

What is a common problem with task parallelism and thread pools?

• Tasks are executed out of order.

• Not all threads participate in the calculation.

• Tasks consume too much memory.

• Tasks are not reusable.

What is one suggested strategy to improve performance in task parallelism?

• Increasing the task size.

• Reducing the number of threads.

• Dynamic scaling of threads.

• Using a single-threaded approach.

What is a potential benefit of implementing a more advanced thread pool?

• It can eliminate the need for task scheduling.

• It can support dynamic scaling of threads.

• It can reduce the number of tasks.

• It can increase the memory usage.