Smart data structures: an online machine learning approach to multicore data structures
Smart data structures: an online machine learning approach to multicore data structures
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智能数据结构：多核数据结构的在线机器学习方法
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
A. Agarwal
中科院分区：
文献类型：
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作者：
J. Eastep;David Wingate;A. Agarwal
随着多核技术的普及，编程的复杂性也在急剧上升。一个主要的困难是通过共享数据结构有效地协调线程之间的协作。不幸的是，选择和手动调优数据结构算法以在各种机器和输入上获得良好的性能是一项艰巨的任务，增加了获得正确并行程序的基本困难。为了帮助减轻这些复杂性，这项工作开发了一类新的并行数据结构，称为智能数据结构，利用在线机器学习自动适应。我们的原型和评估一个开源的智能数据结构库的共同并行编程的需求，并证明了显着的改进，现有的最佳算法在各种条件下。我们的研究结果表明，学习是一种很有前途的技术，用于平衡和适应复杂的、随时间变化的权衡，并实现最佳性能。
As multicores become prevalent, the complexity of programming is skyrocketing. One major difficulty is efficiently orchestrating collaboration among threads through shared data structures. Unfortunately, choosing and hand-tuning data structure algorithms to get good performance across a variety of machines and inputs is a herculean task to add to the fundamental difficulty of getting a parallel program correct. To help mitigate these complexities, this work develops a new class of parallel data structures called Smart Data Structures that leverage online machine learning to adapt automatically. We prototype and evaluate an open source library of Smart Data Structures for common parallel programming needs and demonstrate significant improvements over the best existing algorithms under a variety of conditions. Our results indicate that learning is a promising technique for balancing and adapting to complex, time-varying tradeoffs and achieving the best performance available.