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| P. Heidelberger, A. Norton, J.T. Robinson, "Parallel Quicksort Using Fetch-And-Add," IEEE Transactions on Computers, vol. 39, no. 1, pp. 133-138, January, 1990. | |||
| BibTex | x | ||
| @article{ 10.1109/12.46289, author = {P. Heidelberger and A. Norton and J.T. Robinson}, title = {Parallel Quicksort Using Fetch-And-Add}, journal ={IEEE Transactions on Computers}, volume = {39}, number = {1}, issn = {0018-9340}, year = {1990}, pages = {133-138}, doi = {http://doi.ieeecomputersociety.org/10.1109/12.46289}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Computers TI - Parallel Quicksort Using Fetch-And-Add IS - 1 SN - 0018-9340 SP133 EP138 EPD - 133-138 A1 - P. Heidelberger, A1 - A. Norton, A1 - J.T. Robinson, PY - 1990 KW - fetch-and-add; parallelization; Quicksort algorithm; shared memory multiprocessor; partitioning phase; parallel algorithm; sorting; scheduling; N-processor PRAM; simulations; parallel algorithms; sorting. VL - 39 JA - IEEE Transactions on Computers ER - | |||
A parallelization of the Quicksort algorithm that is suitable for execution on a shared memory multiprocessor with an efficient implementation of the fetch-and-add operation is presented. The partitioning phase of Quicksort, which has been considered a serial bottleneck, is cooperatively executed in parallel by many processors through the use of fetch-and-add. The parallel algorithm maintains the in-place nature of Quicksort, thereby allowing internal sorting of large arrays. A class of fetch-and-add-based algorithms for dynamically scheduling processors to subproblems is presented. Adaptive scheduling algorithms in this class have low overhead and achieve effective processor load balancing. The basic algorithm is shown to execute in an average of O(log(N)) time on an N-processor PRAM (parallel random-access machine) assuming a constant-time fetch-and-add. Estimated speedups, based on simulations, are also presented for cases when the number of items to be sorted is much greater than the number of processors.
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