Performance Modeling and Evaluation of a Production Disaggregated Memory System
Performance Modeling and Evaluation of a Production Disaggregated Memory System
复制标题
DOI：
10.1145/3422575.3422795
复制
发表时间：
2020-09
期刊：
影响因子：
--
通讯作者：
Ning Zhang;Xian-He Sun
中科院分区：
文献类型：
--
作者：
Ning Zhang;Xian-He Sun
高性能计算机依赖大容量内存来缓存数据并提升性能。然而，管理内存层次结构中不断增多的层级变得愈发困难。近年来，为了实现更好的内存利用，分布式内存系统（DMS）架构应运而生。DMS是位于本地内存与存储之间的一个全局内存池。为了充分利用DMS，我们需要更深入地了解其性能以及如何充分挖掘其潜力。在本研究中，我们首先提出一个用于性能评估与分析的DMS性能模型。接下来，我们进行全面的性能评估，以明确不同系统配置下应用程序与DMS的特性。性能评估实验在阿贡国家实验室可用的一种DMS实现——内存区域网络（RAN）上开展。随后，我们展示性能实验结果，并分析RAN - DMS设计与实现的优缺点。我们从代码层面分析K - means应用程序出人意料的性能结果，以阐释DMS的性能。最后，基于研究发现，我们对未来DMS设计及其在人工智能应用中的潜力展开一些探讨。
High performance computers rely on large memories to cache data and improve performance. However, managing the ever-increasing number of levels in the memory hierarchy becomes increasingly difficult. The Disaggregated Memory System (DMS) architecture was introduced in recent years for better memory utilization. DMS is a global memory pool between the local memories and storage. To leverage DMS, we need a better understanding of its performance and how to exploit its full potential. In this study, we first present a DMS performance model for performance evaluation and analysis. We next conduct a thorough performance evaluation to identify application-DMS characteristics under different system configurations. Experimental tests are conducted on the RAM Area Network (RAN), a DMS implementation available at the Argonne National Laboratory, for performance evaluation. Then, the results of performance experiments are presented along with an analysis of the pros and cons of the RAN-DMS design and implementation. The counterintuitive performance results for the K-means application are analyzed at code-level to illustrate DMS performance. Finally, based on our findings, we present some discussions on future DMS design and its potential on AI applications.