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DETERMINISTIC SAMPLE SORT FOR GPUS

机译:用于GPU的确定性样本排序

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摘要

We demonstrate that parallel deterministic sample sort for many-core GPUs (GPU Bucket Sort) is not only considerably faster than the best comparison-based sorting algorithm for GPUs (Thrust Merge [Satish et.al., Proc. IPDPS 2009]) but also as fast as randomized sample sort for GPUs (GPU Sample Sort [Leischner et.al., Proc. IPDPS 2010]). However, deterministic sample sort has the advantage that bucket sizes are guaranteed and therefore its running time does not have the input data dependent fluctuations that can occur for randomized sample sort.
机译:我们证明,针对多核GPU的并行确定性样本排序(GPU桶排序)不仅比基于GPU的最佳基于比较的最佳排序算法(Thrust Merge [Satish等人,Proc。IPDPS 2009])要快得多。与GPU的随机样本排序一样快(GPU样本排序[Leischner等人,Proc。IPDPS 2010])。但是,确定性样本分类的优点是可以保证存储桶的大小,因此其运行时间不会因随机样本分类而发生与输入数据有关的波动。

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