Sorting

1. Sorting Parallel Computing Spring 2010

2. Sorting Algorithm • Rearranging a list of numbers into increasing (strictly nondecreasing) order.

3. Potential Speedup • O(nlogn) optimal for any sequential sorting algorithm without using special properties of the numbers. • Best we can expect based upon a sequential sorting algorithm using n processors is Optimal parallel time complexity = O(n logn)/n= O(logn) • Has been obtained but the constant hidden in the order notation extremely large.

4. Compare-and-Exchange Sorting Algorithms: Compare and Exchange • Form the basis of several, if not most, classical sequential sorting algorithms. • Two numbers, say A and B, are compared. If A > B, A and B are exchanged, i.e.: if (A > B) { temp = A; A = B; B = temp; }

5. Message Passing Method For P1 to send A to P2 and P2 to send B to P1. Then both processes perform compare operations. P1 keeps the larger of A and B and P2 keeps the smaller of A and B:

6. Merging Two Sublists

7. Bubble Sort • First, largest number moved to the end of list by a series ofcompares and exchanges, starting at the opposite end. • Actions repeated with subsequent numbers, stopping just before the previously positioned number. • In this way, the larger numbers move (“bubble”) toward one end,

8. Bubble Sort

9. Time Complexity • Number of compare and exchange operations • Indicates a time complexity of O(n^2) given that a single compare-and-exchange operation has a constant complexity, O(1).

10. Parallel Bubble Sort Iteration could start before previous iteration finished if doesnot overtake previous bubbling action:

11. Odd-Even (Transposition) Sort Variation of bubble sort. Operates in two alternating phases, even phase and odd phase. Even phase Even-numbered processes exchange numbers with their right neighbor. Odd phase Odd-numbered processes exchange numbers with their right neighbor.

12. Sequential Odd-Even Transposition Sort

13. Parallel Odd-Even Transposition SortSorting eight numbers

14. Parallel Odd-Even Transposition Sort • Consider the one item per processor case. • There are n iterations; in each iteration, each processor does one compare-exchange –which can all be done in parallel. • The parallel run time of this formulation is Θ(n). • This is cost optimal with respect to the base serial algorithm but not the optimal serial algorithm.

15. Parallel Odd-Even Transposition Sort

16. Parallel Odd-Even Transposition Sort • Consider a block of n/p elements per processor. • The first step is a local sort. • In each subsequent step, the compare exchange operation is replaced by the compare split operation. • There are p phases with each phase performing Θ(n/p) compares and Θ(n/p) communication. • The parallel run time of the formulation is • The parallel formulation is cost-optimal for p= O(logn).

17. End Thank you!