Chapter: Multicore Application Programming For Windows, Linux, and Oracle Solaris - Identifying Opportunities for Parallelism

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How Parallelism Can Change the Choice of Algorithms

Algorithms have characteristics that make them more or less appropriate for a multi-threaded implementation.

How Parallelism Can Change the Choice of Algorithms

Algorithms have characteristics that make them more or less appropriate for a multi-threaded implementation. For example, suppose you have a deck of playing cards that are in a random order but you would like to sort them in order. One way to do this would be to hold the unsorted cards in one hand and place each card into its appropriate place in the other hand. There are N cards, and a binary search is needed to locate each card into its proper place. So, going back to the earlier discussion on algorithmic complexity, this is an O(nlog(n)) algorithm.

 

However, suppose you have someone to help, and you each decide to sort half the pack. If you did that, you would end up with two piles of sorted cards, which you would then have to combine. To combine them, you could each start with a pile of cards, and then whoever had the next card could place it onto the single sorted stack. The com-plexity of the sort part of this algorithm would be O(nlog(n)) (for a value of n that was half the original), and the combination would be O(n). So although we have increased the number of “threads,” we do not guarantee a doubling of performance.

 

An alternative way of doing this would be to take advantage of the fact that playing cards have an existing and easily discernible order. If instead of sorting the cards, you just place them at the correct place on a grid. The grid could have the “value” of the card as the x-axis and the “suit” of the card as the y-axis. This would be an O(n) operation since the time it takes to place a single card does not depend on the number of cards that are present in the deck. This method is likely to be slightly slower than keeping the cards in your hands because you will have to physically reach to place the cards into the appropriate places in the grid. However, if you have the benefit of another person helping, then the deck can again be split into two, and each person would have to sort only half the cards. Assuming you don’t obstruct each other, you should be able to attain a near doubling of performance. So, comparing the two algorithms, using the grid method might be slower for a single person but would scale better with multiple people.

 

The point here is to demonstrate that the best algorithm for a single thread may not necessarily correspond to the best parallel algorithm. Further, the best parallel algorithm may be slower in the serial case than the best serial algorithm.

 

Proving the complexity of a parallel algorithm is hard in the general case and is typi-cally handled using approximations. The most common approximation to parallel performance is Amdahl’s law.


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