首页> 外文会议>Annual IEEE/ACM International Symposium on Microarchitecture >Large-reach memory management unit caches: Coalesced and shared memory management unit caches to accelerate TLB miss handling
【24h】

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

机译:大型内存管理单元缓存:合并和共享内存管理单元缓存,以加速TLB Miss Handling

获取原文

摘要

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:在重要的内存层次结构中,小型研究致力于内存管理单元(MMU)缓存,在现代处理器中实现,以加速翻译Lookaside缓冲区(TLB)未命中。 MMU缓存在确定系统性能方面发挥着关键作用。本文介绍了量化该角色大小的测量研究,并描述了两种新颖的优化,以提高该结构在一系列顺序和平行的大数据工作负载上的性能。首先是一种软件/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,OS以途径分配页面页面,使其成为在MMU高速缓存中聚结的方式,增加了他们的命中率。第二种是一种易于实现的硬件唯一的方法,用同一总区的单个共享MMU高速缓存替换标准的每核MMU缓存。尽管其额外的访问延迟,但减少了最小的速度大大提高了性能。这种方法是正交的;他们一起实现性能接近理想的MMU缓存。总体而言,本文涉及MMU缓存研究的缺乏。我们的见解将协助开发高性能地址转换支持对运行大数据应用的系统。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号