CSR: Medium: WasteNot: Streamlining Virtual Memory for Modern Systems
CSR: Medium: WasteNot: Streamlining Virtual Memory for Modern Systems
批准号:
1302260
负责人:
Michael Swift
金额:
$74.91万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-09-01 至 2016-08-31
中文摘要
该建议解决了虚拟内存中的浪费问题。虚拟内存是在资源稀缺的时代(例如,低于mb的内存)发明的,但现在被部署到一个具有巨大内存(例如,GB到TB)的变化世界中,而程序通常不需要。利用虚拟内存的通用性(例如,web服务器的大小不能交换)。同时,虚拟内存会浪费大量的执行时间(在翻译暂置缓冲区(TLB)丢失时)和功耗(在每个内存引用上访问TLB)。此外,支持通用图形处理单元(gp - gpu)和虚拟机(VMM)的系统扩展对虚拟内存的挑战更大。这个建议的中心假设是内存过于虚拟:大多数工作负载在大多数情况下并不需要虚拟内存的所有好处。为此,我们寻求通过多种转换机制来实现虚拟内存的非虚拟化,这些转换机制以更少的功能提供更好的性能,以便应用程序为它们所需的功能(性能、电源/能源、空间等)付费,而不是为基于页面的虚拟内存付费。S总是全面性的。这项工作减少了计算机系统的浪费,使其成本和性能得以持续改进。这种改进促进了应用程序的创新,例如大数据工作负载的更高性能,包括图形分析,以及降低功耗带来的环境效益。除了技术建议之外,这项工作还开发和传播了利用其他NSF投资的工具,例如开源的?计算机系统仿真系统。
英文摘要
This proposal attacks waste in virtual memory. Virtual memory was invented in a time of scarcity (e.g., sub-MB memory) but is now deployed to a changed world with vast memory (e.g., GB to TB) where programs often don?t exploit virtual memories generality (e.g., web servers sized to not swap). At the same time, virtual memory can waste substantial execution time (on translation look aside buffer (TLB) misses) and power (accessing TLBs on each memory reference). Moreover, system extensions to support general-purpose graphics processing units (GP-GPUs) and virtual machine (VMM) challenge virtual memory more. The central hypothesis of this proposal is that memory is too virtual: most workloads do not need all of the benefits of virtual memory most of the time. To this end, we seek to de-virtualize virtual memory with multiple translation mechanisms that offer better performance with less functionality so that applications pay (performance, power/energy, space, etc.) for the features they require instead of for page-based virtual memory?s full generality always. This work reduces waste in computer systems to enable continued improvements in thier cost and performance. This improvement facilitates application innovations like higher performance for big-data workloads, including graph analysis, and environmental benefits from reduced power consumption. Beyond technical advices, this work develops and disseminates tools that leverage other NSF investments, such as the open-source ?gem5? computer-system simulation system.
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