自适应记忆融合:迈向持久记忆的透明、敏捷集成

Dongliang Xue, Chao Li, Linpeng Huang, Chentao Wu, Tianyou Li
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引用次数: 11

摘要

内存计算的巨大前景激励工程师以及时有效的方式扩展其主内存子系统。今天的新一代持久内存(PM)模块以接近dram的速度提供了极大的扩展容量,无疑是系统升级的理想选择。然而,在当前的企业系统中集成可与dram相媲美的pm面临着巨大的障碍,因为软件兼容性和复杂的运行时支持需要进行大量的系统修改。此外,非常大的PM容量不可避免地导致大量元数据,这将带来显著的性能和能源开销。当内存系统达到其容量限制或应用程序需要分配大量内存空间时,低效率问题变得更加严重。在本文中,我们提出了一种新的PM集成方案——自适应记忆融合(AMF),它共同解决了上述问题。我们没有通过修改整个软件栈来努力适应PM的持久性,而是专注于开发新兴PM模块的高容量特性。AMF被设计成对用户应用程序完全透明,它小心地隐藏PM设备,并以类似dram的方式管理可用的PM空间。为了进一步提高性能,我们设计了整体优化方案,使系统能够有效地利用系统资源。具体来说,AMF能够根据内存压力状态自适应地释放PM,巧妙地回收PM页面,并通过直接PM传递实现快速空间扩展。我们将AMF作为内核子系统在Linux中实现。与传统方法相比,AMF可以将高驻留设置基准的页面错误数量减少67.8%,平均减少46.1%。使用实际的内存数据库,我们发现AMF在SQLite上比现有解决方案高出57.7%,在Redis上高出21.8%。总的来说,AMF代表了一种更轻量级的设计方法,它将极大地鼓励在不久的将来快速和灵活地采用PM。
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Adaptive Memory Fusion: Towards Transparent, Agile Integration of Persistent Memory
The great promise of in-memory computing inspires en-gineers to scale their main memory subsystems in a timely and efficient manner. Offering greatly expanded capacity at near-DRAM speed, today’s new-generation persistent memory (PM) module is no doubt an ideal candidate for system upgrade. However, integrating DRAM-comparable PMs in current enterprise systems faces big barriers in terms of huge system modifications for software compati-bility and complex runtime support. In addition, the very large PM capacity unavoidably results in massive metadata, which introduces significant performance and energy overhead. The inefficiency issue becomes even acute when the memory system reaches its capacity limit or the application requires large memory space allocation. In this paper we propose adaptive memory fusion (AMF), a novel PM integration scheme that jointly solves the above issues. Rather than struggle to adapt to the persistence property of PM through modifying the full software stack, we focus on exploiting the high capacity feature of emerging PM modules. AMF is designed to be totally transparent to user applications by carefully hiding PM devices and managing the available PM space in a DRAM-like way. To further improve the performance, we devise holistic optimization scheme that allows the system to efficiently utilize system resources. Specifically, AMF is able to adaptively release PM based on memory pressure status, smartly reclaim PM pages, and enable fast space expansion with direct PM pass-through. We implement AMF as a kernel subsystem in Linux. Compared to traditional approaches, AMF could decrease the page faults number of high-resident-set benchmarks by up to 67.8% with an average of 46.1%. Using realistic in-memory database, we show that AMF outperforms existing solutions by 57.7% on SQLite and 21.8% on Redis. Overall, AMF represents a more lightweight design approach and it would greatly encourage rapid and flexible adoption of PM in the near future.
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