CSR:Small:System Support for Petabyte Memories

计算机长期以来一直将工作记忆与永久存储分开。内存通过固定大小的页面（如一本书中的一页）进行管理，而存储则通过大型可变大小的文件（如一整本书）进行管理。特别是对于提供在线服务的计算机，内存正在增长到猛犸的大小。同样重要的是，新技术将允许记忆像存储一样永久。 这个项目包含这些变化，并将开发技术来管理内存更像文件，以加快访问巨大的数据集在一个猛犸量的内存。新技术将由新工具、数据和分析驱动，所有这些都是公开的。这个项目有五个重点。首先，该项目将通过虚拟化和特定于应用程序的压缩来构建和分发用于模拟具有猛犸内存的计算机的新工具。第二，该项目将开发“仅文件内存”，以支持对组成猛犸内存的任意大区域的有效管理。第三，该项目将分析和减轻困扰新旧内存管理解决方案的内存碎片。第四，该项目将开发一个完整的软件-硬件堆栈，用于有效扩展到猛犸的内存大小的地址转换。第五，该项目将使本科生参与计算机科学的教学，并作为研究生研究人员的助手参与拟议的研究。这个项目承诺更高效的计算机与猛犸内存，导致更高的性能和更少的浪费资源。通过仿真工具的开发和发布，这项工作将允许快速有效地研究猛犸的记忆，目前还不可行。对碎片化的研究将促进对新的内存管理技术的进一步研究，以专门解决猛犸内存的碎片化问题。拟议的系统改进将使海量存储系统能够降低成本，提高效率，并简化大型数据集的管理。最后，调查人员将发布所有工作负载，工具和仿真平台作为开源。项目资料库可从研究小组主页（http：//research.cs.wisc.edu/multifacet/）以及研究者主页上查阅。仿真工具将发布到源代码库，允许协作开发。 数据集将在位于麦迪逊的威斯康星州大学的网站上全文公布，或通过查询界面提供给大型数据集。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Computers have long separated working memory from permanent storage. Memory has been managed via fixed-sized pages (like a page in a book) while storage gets managed with large, variable-sized files (like a whole book). Especially for computers that provide online services, memory is growing to mammoth size. Equally important, new technologies will allow memory to be permanent like storage. This project embraces these changes and will develop techniques to manage memory more like files to speed access to huge data sets in a mammoth amount of memory. The new techniques will be driven with new tools, data, and analysis, all made publicly available. This project has five thrusts. First, the project will build and distribute new tools for emulating computers with mammoth memories via virtualization and application-specific compression. Second, the project will develop "file-only memory" to support efficient management of the arbitrarily large regions that compose a mammoth memory. Third, the project will analyze and mitigate memory fragmentation that plagues old and new solutions to memory management. Fourth, the project will development a full software-hardware stack for address translation that scales efficiently to mammoth memory sizes. Fifth, the project will engage undergraduates in both teaching of computer science generally, and in the proposed research as assistants to graduate student researchers. This project promises more efficient computers with mammoth memories, leading to higher performance and fewer wasted resources. Through the development and release of emulation tools, this work will allow rapid efficient studies of mammoth memories not currently feasible. The study of fragmentation will spur further research on new memory management techniques to specifically address fragmentation of mammoth memories. The proposed system enhancements will enable mammoth-memory systems at reduced cost and increased efficiency and simplify management of large data sets. Finally, the investigators will release all workloads, tools, and emulation platforms as open source. The project repository will be available from the research group home page (http://research.cs.wisc.edu/multifacet/) as well as the investigators' home web pages. Emulation tools will be published to source code repositories allowing collaborative development. Data sets will be published in full on a web site hosted at the University of Wisconsin at Madison or be provided via a query interface to large data sets. Papers and articles will also be available on the research group web page.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.