CSR:Small:System Support for Petabyte Memories

长期以来，计算机将工作记忆与永久存储分开。内存是通过固定大小的页面(就像一本书中的一页)来管理的，而存储则是通过大的、可变大小的文件(就像一整本书)来管理的。特别是对于提供在线服务的计算机来说，内存正在增长到巨大的规模。同样重要的是，新技术将使记忆像存储一样成为永久性的。该项目接受了这些变化，并将开发更像文件一样管理内存的技术，以加快对海量内存中的海量数据集的访问。新技术将由新的工具、数据和分析驱动，所有这些都是公开的。这个项目有五个推力。首先，该项目将构建和分发新的工具，通过虚拟化和特定于应用程序的压缩来模拟具有海量内存的计算机。其次，该项目将开发“仅文件内存”，以支持对构成巨大内存的任意大小区域的有效管理。第三，该项目将分析和缓解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硬件堆栈，用于地址转换，可以有效地扩展到巨大的内存大小。第五，该项目将使本科生参与计算机科学的一般教学，并作为研究生研究人员的助理参与拟议的研究。这个项目承诺使用更高效的计算机和巨大的内存，从而带来更高的性能和更少的资源浪费。通过开发和发布仿真工具，这项工作将使目前尚不可行的猛犸象记忆的快速有效研究成为可能。碎片的研究将推动对新的存储器管理技术的进一步研究，以专门解决猛犸象存储器的碎片问题。拟议的系统改进将使巨型存储系统能够以更低的成本和更高的效率实现，并简化大型数据集的管理。最后，调查人员将以开源形式发布所有工作负载、工具和仿真平台。项目库将从研究小组主页(http://research.cs.wisc.edu/multifacet/))以及调查人员的主页上获得。仿真工具将发布到源代码存储库，以便进行协作开发。数据集将在威斯康星大学麦迪逊分校托管的网站上全文公布，或通过大型数据集的查询界面提供。论文和文章也将出现在研究小组的网页上。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.