SHF: Small: Taming Huge Page Problems for Memory Bulk Operations Using a Hardware/Software Co-Design Approach

SHF：小：使用硬件/软件协同设计方法解决内存批量操作的大页面问题

• 批准号: 2400014
• 负责人: Jun Wang
• 金额: $ 40万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2400014&HistoricalAwards=false
• 关键词: SHF; Small; Taming; Huge; Page

Recent advances in non-volatile memory (NVM) technologies promise significantly higher capacities compared to traditional dynamic random-access memory (DRAM). Today’s computers often combine NVM with DRAM to construct a unified main memory architecture that meets the extremely high demand of many emerging big data and artificial intelligence applications. However, managing ultra-high capacity memories introduces daunting systems challenges. In particular, the massive parallelism of NVM drives adoption of huge two megabytes or even larger memory pages. The successful completion of this project's activity will be a significant step towards accelerating the secure non-volatile memory architectures by orders of magnitude performance improvement. This project will also contribute to society through engaging under-represented groups from a Hispanic Serving Institution and research dissemination for computer science and engineering education and training.The research project tackles these issues using a hardware and software co-design methodology. In this project, a general-purpose, fine­-grained memory management system will be designed, modeled, and implemented to significantly improve both the speed and bandwidth of single-level, DRAM combined with ultra-low latency, non-volatile memory architecture for both central processing unit (CPU) and graphic processing unit (GPU) machines, in particular for huge page configurations. The unused and overprovisioned parts of existing hardware architecture units will be exploited via both non-intrusive and intrusive ways to enable fine-granularity memory management, and achieve high efficiency and effectiveness of the Copy-on-Write mechanism. Several new Copy-on-Write use cases, including the checkpointing and snapshot, and large language model serving will be further studied. Furthermore, the new memory architecture will be integrated with the high-speed processing workflow of big data management, artificial intelligence programs, and hardware security machines.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.

与传统的动态随机存取存储器 (DRAM) 相比，非易失性存储器 (NVM) 技术的最新进展有望提供更高的容量。当今的计算机通常将 NVM 与 DRAM 结合起来，构建统一的主存储器架构，以满足许多新兴市场的极高需求。然而，超高容量存储器带来了困难的系统挑战，特别是，NVM 的大规模并行性推动了巨大的 2 兆字节甚至更大的存储器页面的采用。重要的一步该项目还将通过吸引西班牙裔服务机构中代表性不足的群体以及传播计算机科学和工程教育和培训的研究，加速安全非易失性存储器架构的性能提高。该研究项目致力于解决这一问题。在该项目中，将使用硬件和软件协同设计方法来设计、建模和实现通用的细粒度内存管理系统，以显着提高单级 DRAM 组合的速度和带宽。和适用于中央处理单元 (CPU) 和图形处理单元 (GPU) 机器的超低延迟、非易失性内存架构，特别是对于大页面配置，现有硬件架构单元的未使用和过度配置的部分将通过非非易失性内存架构来利用。 - 侵入式和侵入式方式实现细粒度的内存管理，并实现高效率和有效性的Copy-on-Write机制 几个新的Copy-on-Write用例，包括检查点和快照，以及大容量。此外，新的内存架构将与大数据管理、人工智能程序和硬件安全机器的高速处理工作流程相结合。该奖项体现了 NSF 的法定使命，被认为是当之无愧的。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来提供支持。