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CPRIC: Collaborative Parallelism for Randomized Incremental Constructions

机译:CPRIC:随机增量结构的协作并行性

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Randomized algorithms often outperform their deterministic counterparts in terms of simplicity and efficiency. In this paper, we consider Randomized Incremental Constructions (RICs) that are very popular, in particular in combinatorial optimization and computational geometry. Our contribution is Collaborative Parallel RIC (CPRIC) –a novel approach to parallelizing RIC for modern parallel architectures like vector processors and GPUs. We show that our approach based on a work-stealing mechanism avoids the control-flow divergence of parallel threads, thus improving the performance of parallel implementation. Our extensive experiments on CPU and GPU demonstrate the advantages of our CPRIC approach that achieves an average speedup between 4× and 5× compared to the naively parallelized RIC.
机译:随机算法在简单性和效率方面经常优于其确定性对应物。 在本文中,我们考虑了非常流行的随机增量结构(RIC),特别是在组合优化和计算几何中。 我们的贡献是协作平行的RIC(CPRIC)-A对像矢量处理器和GPU等现代平行架构并行化RIC的新方法。 我们表明我们的方法基于工作窃取机制避免了并行线程的控制流动分歧,从而提高了并行实现的性能。 我们对CPU和GPU的广泛实验证明了我们CPRIC方法的优势,与天真平行化的RIC相比,实现了4×和5×之间的平均速度。

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