Machine Learning Algorithm Performance on the Lucata Computer

P. Springer, Thomas Schibler, Géraud Krawezik, J. Lightholder, P. Kogge
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引用次数: 2

Abstract

A new parallel computing paradigm has recently become available, one that combines a PIM (processor in memory) architecture with the use of many lightweight threads, where each thread migrates automatically to the memory used by that thread. Our effort focuses on producing performance gains on this architecture for a key machine learning algorithm, Random Forest, that are at least linear in proportion to the number of cores. Beyond that, we show that a data distribution that groups test samples and trees by feature improves run times by a factor more than double the number of cores in the machine.
机器学习算法在Lucata计算机上的性能
最近出现了一种新的并行计算范例,它将PIM(内存中的处理器)体系结构与使用许多轻量级线程相结合,其中每个线程自动迁移到该线程使用的内存中。我们的工作重点是在这个架构上为一个关键的机器学习算法随机森林(Random Forest)提供性能提升,这至少与核心数量成线性关系。除此之外,我们还表明,按特征对测试样本和树进行分组的数据分布可以将运行时间提高一倍以上,这是机器中核心数量的两倍多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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