Machine Learning Algorithm Performance on the Lucata Computer
Machine Learning Algorithm Performance on the Lucata Computer
复制标题
Lucata 计算机上的机器学习算法性能
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
P. Kogge
中科院分区：
文献类型：
--
作者：
P. Springer;Thomas Schibler;G. Krawezik;J. Lightholder;P. Kogge
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）架构与许多轻量级线程的使用相结合，其中每个线程自动迁移到该线程使用的内存。我们的工作重点是在这种架构上为关键的机器学习算法随机森林（Random Forest）产生性能增益，至少与核心数量成线性比例。除此之外，我们还表明，按功能对测试样本和树进行分组的数据分布将运行时间提高了一倍以上。
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.