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Parallel Selection - Implementation and Analysis


The parallel algorithm for selection can now be presented, and makes use of the Dynamic Data Redistribution algorithm given in Section 4. The following is run on processor j:


The analysis of the parallel selection algorithm is as follows. For , in step 1, we solve the problem sequentially in linear time. For larger n, dynamic data redistribution algorithm is called in step 2 to ensure that there are elements on processors 0 through p-2, and processor p-1 holds the remaining elements. At least half of the medians found in step 3 are less than or equal to the splitter. Thus, at least half of the p groups contribute at least elements that are less than the splitter, except for the last group and the group containing the splitter element. Therefore, the total number of elements less than or equal to the splitter is at least

Similarly, the number of elements that are greater than the splitter is at least . Thus, in the worst case, the selection algorithm is called recursively on at most


Using the complexity of the communication primitives as stated in Section 3, it is easy to to derive the recurrence equations for the parallel complexity of our algorithm. Solving these recurrences yields the following complexity:


where m is defined in Eq. (6) to be , the maximum number of elements initially on any of the processors. For fixed p, the communication time increases linearly with m and logarithmically with n, while the computation time grows linearly with both m and n.

Figure 6: Performance of Median Algorithm

Figure 7: Performance of Median Algorithm on the SP-2

The running time of the median algorithm on the TMC CM-5 using both methods of dynamic data redistribution is given in Figure 6. Similar results are given in Figure 7 for the IBM SP-2. In all data sets, initial data is balanced.

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Next: Data Sets Up: Parallel Selection Previous: Parallel Selection

David A. Bader