Multi-terabyte large memory systems are emerging. They are often characterized with more than two memory tiers for large memory capacity and high performance. Those tiers include slow and fast memories with different latencies and bandwidths. Making effective, transparent use of the multi-tiered large memory system requires a page management system, based on which the application can make the best use of fast memories for high performance and slow memories for large capacity. However, applying existing solutions to multi-tiered large memory systems has a fundamental limitation because of non-scalable, low-quality memory profiling mechanisms and unawareness of rich memory tiers in page migration policies. We develop HM-Keeper, an application-transparent page management system that supports the efficient use of multi-tiered large memory. HM-Keeper is based on two design principles: (1) The memory profiling mechanism must be adaptive based on spatial and temporal variation of memory access patterns. (2) The page migration must employ a holistic design principle, such that any slow memory tier has equal opportunities to directly use the fastest memory. We evaluate HM-Keeper using common big-data applications with large working sets (hundreds of GB to one TB). HM-Keeper largely outperforms seven existing solutions by 15%-78%
翻译:多地球体大型记忆系统正在出现,它们的特点往往是,对于巨大的记忆容量和高性能而言,有超过两层的记忆层,它们往往具有较大的记忆能力和高性能。这些层包括缓慢和快速的记忆,有不同的迟滞和带宽。要有效、透明地使用多层大型记忆系统,需要有一个页面管理系统,根据该系统,应用程序可以最佳地利用快速的记忆,以高性能为目的,而对于大型记忆系统则以缓慢的记忆为目的。然而,对多层大型记忆系统应用现有解决方案,由于无法伸缩、低质量的记忆特征分析机制以及页面迁移政策中丰富的记忆层的不为人所知,因此具有根本性的局限性。我们开发了HM-保管器,这是一个支持高效使用多层大型记忆的应用透明页面管理系统。HM-L-L-LOD基于两个设计原则:(1) 记忆特征分析机制必须基于记忆访问模式的时空变化进行适应。(2) 页面迁移必须采用一个整体的设计原则,这样,任何缓慢的记忆层都有机会直接使用最快的记忆层。我们用通用的大数据应用程序评价高数据应用程序,使用大的工作数据集(主要是为15个基调的基调调调制)