CRII: OAC: Towards Efficient Memory Management on Terabyte-Scale CXL-Enabled Tiered Memory Systems

随着现代计算环境的发展，Compute Express Link （CXL）和多层内存系统的出现带来了内存管理方面的重大进步和复杂性。传统的内存管理方法主要是为统一的内存系统设计的，在这些高级的、多层的环境中是不够的。该项目提出了一个操作系统（OS）级别的解决方案，用于支持xml的系统中的内存管理，从而在不需要修改应用程序的情况下最大化各种应用程序的性能。这项研究通过提高技术能力，促进科学进步，并通过改进计算基础设施潜在地影响国家健康和繁荣，从而符合国家利益。该计划深入研究了为tb级、支持cxl的分层内存系统开发健壮的内存管理框架。该研究通过两种主要方法解决了这些挑战：(1)一种先进的页面分配机制，该机制在考虑带宽和延迟的情况下，优化了不同内存类型的数据分布，有效地运行，而无需事先了解应用程序的行为。(2)一种启发式的方法来管理所有内存页面，在整个内存层次结构中以精确的、低开销的基于内存区域的分析为指导。所提出的解决方案值得注意的是，它能够解决多层内存系统中的延迟和带宽问题，超越了当前方法的能力。这项研究的智力价值在于它有可能显著增强操作系统内存管理和对异构内存系统的理解。通过解决大规模管理不同内存类型的复杂性，该项目有望显著提高计算基础设施的可扩展性和效率，从而实现更复杂的数据处理任务。更广泛的影响包括促进更广泛地采用分层内存系统，增强计算基础设施的可伸缩性，并潜在地影响用于大规模管理异构内存系统的未来操作系统设计。该奖项反映了美国国家科学基金会的法定使命，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。

As modern computing environments evolve, the advent of Compute Express Link (CXL) and multitiered memory systems introduces significant advancements and complexities in memory management. Traditional memory management methods, primarily designed for uniform memory systems, fall short in these advanced, multi-tiered environments. This project proposes an Operating System (OS) level solution for memory management in CXL-enabled systems to maximize performance for various applications without requiring application modifications. This research aligns with national interests by enhancing technological capabilities, contributing to scientific progress, and potentially impacting national health and prosperity through improved computational infrastructure.This initiative delves into developing a robust memory management framework for terabyte-scale, CXL-enabled tiered memory systems. The research tackles these challenges through two main approaches: (1) An advanced page allocation mechanism that optimizes data distribution across different memory types, considering bandwidth and latency, functioning effectively without prior knowledge of application behavior. (2) A heuristic method for managing all memory pages, guided by accurate, low-overhead memory region-based profiling across the entire memory hierarchy. The proposed solution is noteworthy for its ability to address both latency and bandwidth considerations in multi-tiered memory systems, surpassing the capabilities of current approaches. The intellectual merit of this research lies in its potential to significantly enhance OS memory management and the understanding of heterogeneous memory systems. By addressing the complexities of managing diverse memory types on a large scale, the project is expected to dramatically improve the scalability and efficiency of computational infrastructures, enabling more complex data processing tasks. The broader impacts encompass facilitating the wider adoption of tiered memory systems, enhancing the scalability of computational infrastructures, and potentially influencing future operating system designs for managing heterogeneous memory systems at scale.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.