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Large-reach memory management unit caches: Coalesced and shared memory management unit caches to accelerate TLB miss handling

机译:大范围内存管理单元高速缓存:合并和共享内存管理单元高速缓存可加快TLB未命中处理

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Within the ever-important memory hierarchy, little research is devoted to Memory Management Unit (MMU) caches, implemented in modern processors to accelerate Translation Lookaside Buffer (TLB) misses. MMU caches play a critical role in determining system performance. This paper presents a measurement study quantifying the size of that role, and describes two novel optimizations to improve the performance of this structure on a range of sequential and parallel big-data workloads. The first is a software/hardware optimization that requires modest operating system (OS) and hardware support. In this approach, the OS allocates page table pages in ways that make them amenable for coalescing in MMU caches, increasing their hit rates. The second is a readily-implementable hardware-only approach, replacing standard per-core MMU caches with a single shared MMU cache of the same total area. Despite its additional access latencies, reduced miss rates greatly improve performance. The approaches are orthogonal; together, they achieve performance close to ideal MMU caches. Overall, this paper addresses the paucity of research on MMU caches. Our insights will assist the development of high-performance address translation support for systems running big-data applications.
机译:在极为重要的内存层次结构中,很少有研究致力于内存管理单元(MMU)缓存,该缓存在现代处理器中实施以加速转换后备缓冲区(TLB)遗漏。 MMU缓存在确定系统性能方面起着至关重要的作用。本文提出了一项量化该角色规模的度量研究,并描述了两种新颖的优化措施,以改善此结构在一系列顺序和并行大数据工作负载上的性能。首先是软件/硬件优化,需要适度的操作系统(OS)和硬件支持。在这种方法中,操作系统以使其适合MMU缓存中合并的方式分配页表页面,从而提高了命中率。第二种是易于实现的纯硬件方法,用总面积相同的单个共享MMU缓存代替标准的每核MMU缓存。尽管具有额外的访问延迟,但降低的未命中率可以极大地提高性能。这些方法是正交的。它们共同实现了接近理想MMU缓存的性能。总体而言,本文解决了有关MMU缓存的研究不足的问题。我们的见解将有助于为运行大数据应用程序的系统开发高性能地址转换支持。

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