随机增量结构的协同并行

Florian Fey, S. Gorlatch
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引用次数: 0

摘要

随机算法在简单性和效率方面往往优于确定性算法。在本文中,我们考虑随机增量结构(RICs)是非常流行的,特别是在组合优化和计算几何。我们的贡献是协作并行RIC (cpricc)——一种为现代并行架构(如矢量处理器和gpu)并行化RIC的新方法。我们证明了基于工作窃取机制的方法避免了并行线程的控制流发散,从而提高了并行实现的性能。我们在CPU和GPU上的大量实验证明了CPRIC方法的优势,与单纯并行化的RIC相比,CPRIC方法的平均加速速度在4到5倍之间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CPRIC: Collaborative Parallelism for Randomized Incremental Constructions
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.
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