论文标题
学习在异构记忆中对基于图的应用对象进行排名
Learning to Rank Graph-based Application Objects on Heterogeneous Memories
论文作者
论文摘要
与DRAM相比，持续的内存（PMEM），也称为非易失性存储器（NVM），可以提供更高的密度和较低的每位成本。它的主要缺点是它通常比DRAM慢。另一方面，由于其成本和能耗，DRAM具有可伸缩性问题。很快，PMEM可能会与计算机系统中的DRAM共存，但最大的挑战是知道在每种内存中分配哪些数据。本文介绍了一种使用Intel Optane DC持久内存对应用程序性能影响最大的应用程序对象的方法。在我们工作的第一部分中，我们构建了一个工具，该工具可以自动化应用对象的分析和分析。在第二部分中，我们构建了一个机器学习模型，以预测基于图形的大规模应用程序中最关键的对象。我们的结果表明，与使用精心选择的功能相比，使用隔离功能不会带来相同的好处。通过使用我们的预测模型执行数据放置，我们可以将执行时间降低减少12 \％（平均）和30 \％（最大），与基准的方法相比，基于LLC的方法是LISSIDES USISES指标。
Persistent Memory (PMEM), also known as Non-Volatile Memory (NVM), can deliver higher density and lower cost per bit when compared with DRAM. Its main drawback is that it is typically slower than DRAM. On the other hand, DRAM has scalability problems due to its cost and energy consumption. Soon, PMEM will likely coexist with DRAM in computer systems but the biggest challenge is to know which data to allocate on each type of memory. This paper describes a methodology for identifying and characterizing application objects that have the most influence on the application's performance using Intel Optane DC Persistent Memory. In the first part of our work, we built a tool that automates the profiling and analysis of application objects. In the second part, we build a machine learning model to predict the most critical object within large-scale graph-based applications. Our results show that using isolated features does not bring the same benefit compared to using a carefully chosen set of features. By performing data placement using our predictive model, we can reduce the execution time degradation by 12\% (average) and 30\% (max) when compared to the baseline's approach based on LLC misses indicator.
