论文标题
停用:架构感知的面料附加内存系统的虚拟内存支持
DeACT: Architecture-Aware Virtual Memory Support for Fabric Attached Memory Systems
论文作者
论文摘要
数据的指数增长促使技术提供商开发新协议,例如高速缓存相干互连和内存语义织物,以帮助用户和设施利用内存技术的进步来满足这些不断增长的内存和存储需求。使用这些新协议,可以将织物连接的记忆(FAM)直接连接到系统互连,并可以轻松地与各种处理元素(PES)集成。此外,支持FAM的系统可以平稳升级,并允许多个PES使用定义明确的协议共享FAM内存池。 PES之间的FAM共享可以有效地共享,改善内存利用率,通过允许从多家供应商的不同PES和内存模块的灵活集成来降低成本,并使升级系统更容易。 FAMS的一个有希望的用例是在高性能计算(HPC)系统中,其中存储器不足是一个主要的挑战。但是,在HPC系统中采用FAM会带来新的挑战。除了成本,灵活性和效率外,需要重新思考的一个特定问题是对安全性和性能的虚拟内存支持。为了应对这些挑战,本文介绍了脱钩的访问控制和地址翻译(DEACT),这是一种支持配备FAM的HPC系统的新型虚拟内存实现。与最先进的两级翻译方法相比,停用的速度达到4.59倍(平均1.8倍)而不会损害安全性。
The exponential growth of data has driven technology providers to develop new protocols, such as cache coherent interconnects and memory semantic fabrics, to help users and facilities leverage advances in memory technologies to satisfy these growing memory and storage demands. Using these new protocols, fabric-attached memories (FAM) can be directly attached to a system interconnect and be easily integrated with a variety of processing elements (PEs). Moreover, systems that support FAM can be smoothly upgraded and allow multiple PEs to share the FAM memory pools using well-defined protocols. The sharing of FAM between PEs allows efficient data sharing, improves memory utilization, reduces cost by allowing flexible integration of different PEs and memory modules from several vendors, and makes it easier to upgrade the system. One promising use-case for FAMs is in High-Performance Compute (HPC) systems, where the underutilization of memory is a major challenge. However, adopting FAMs in HPC systems brings new challenges. In addition to cost, flexibility, and efficiency, one particular problem that requires rethinking is virtual memory support for security and performance. To address these challenges, this paper presents decoupled access control and address translation (DeACT), a novel virtual memory implementation that supports HPC systems equipped with FAM. Compared to the state-of-the-art two-level translation approach, DeACT achieves speedup of up to 4.59x (1.8x on average) without compromising security.