What are the complexities involved in parallel algorithms?

What are the complexities involved in parallel algorithms?

Parallel algorithms often have complexities that depend on factors like the number of processors, communication overhead, and the nature of the problem being solved. Generally, we analyze their performance using time complexity and space complexity.

Can you explain time complexity in parallel algorithms?

Time complexity in parallel algorithms refers to how the execution time of an algorithm scales with the size of the input and the number of processors. It often involves analyzing the work done and the depth of the computation.

What is the difference between work and depth?

Work is the total amount of computational effort, while depth is the longest path from the start to the end of the computation, representing the minimum time required if there were unlimited processors.

How does communication overhead affect performance?

Communication overhead can significantly impact performance, as it involves the time taken for processors to exchange data. If the overhead is high, it can negate the benefits of parallelism.