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Sorting – More quicksort related

This document discusses various advanced parallel QuickSort methods, emphasizing the efficiency improvements achievable through different configurations and approaches. The techniques outlined include a two-processor model that allows tasks to be split from both ends, maximizing speedup, and an innovative Hyper QuickSort strategy which utilizes local sorting and mean calculations to optimize data distribution. Additionally, it addresses limitations on processing capabilities in heavy workloads, providing insights into sampling methods for enhanced parallel sorting performance.

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Sorting – More quicksort related

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  1. Sorting – More quicksort related Jie Liu, Ph.D. Professor Department of Computer Science Western Oregon University USA liuj@wou.edu

  2. Parallel Quick sort • 1. The simplest , two processor, one from the left, one from the right • Speedup of two – max • 2. Push every thing on to a stack, the free processor pick from the top • When the work is heavy, not many processors can participate • Speedup has an upper bound of 6 to 8

  3. Hyper quick sort • Sort its local array • The designated processor find out the mean and send to all others. • Others split the values to two sets, the upper half keep the high set and send out the low set to the lower processor, • Lower half does the opposite • Iterate steps 1 to 4 until the cubes has a size of 1 • Modified (on the mean) not better

  4. Parallel Sorting by Regular Sampling • Sort the local • Select and ship the sample • ** one processor sorts the sample, selects p -1 pivots, and sends it all others • All other divide the sorted list according to the pivots of step 3, • Each processor sends out sub arrays and collect data from others • Each sort locally

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