SPX: Collaborative Research: Memory Fabric: Data Management for Large-scale Hybrid Memory Systems

美国工业界正在为国家实验室开发的新型大规模高性能计算系统，结合了联合收割机异构存储器组件、加速器和加速器近存储器以及可编程高性能互连。这些内存丰富的设计是有吸引力的，因为它们提供了所需的计算近数据能力，以提高科学发现的时间，并支持新类别的延迟敏感的数据密集型应用程序。然而，现有的软件栈不具备处理这些机器设计的异构性和复杂性，这影响了应用性能和机器效率。该项目开发的Memory Fabric（MF）解决方案提供了新的抽象和机制，允许系统软件堆栈更深入地了解应用程序的数据使用模式和要求，并协调有关数据应如何在不同存储器中分布或沿着不同互连路径交换的决策。内存结构（MF）架构引入了新的以数据为中心的抽象、内存对象和内存对象流，以及伴随的内存和通信管理方法。在新的抽象中捕获的更高级别的信息使MF运行时能够更好地指导底层存储器和互连管理，并掩盖底层存储器基底的复杂性。额外的好处来自于近存储器结构计算的使用，包括经由动态插入的应用特定代码，其进一步专门化和加速由MF执行的操作。MF使用几个重要的应用领域进行评估，包括大数据学习和分析以及传统的高性能科学模拟。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。

New large-scale high performance computing systems being developed for the national labs and by US industry, combine heterogeneous memory components, accelerators and accelerator-near memory, and programmable high-performance interconnects. These memory-rich designs are attractive as they provide the compute-near-data capacity needed for improving the time to scientific discovery, and for supporting new classes of latency-sensitive data-intensive applications. However, existing software stacks are not equipped to deal with the heterogeneity and complexity of these machine designs, which impacts application performance and machine efficiency. The Memory Fabric (MF) solution developed in this project provides new abstractions and mechanisms that permit the systems software stacks to gain deeper insight into applications' data usage patterns and requirements, and to coordinate the decisions concerning how data should be distributed across different memories, or exchanged along different interconnection paths. The Memory Fabric (MF) architecture introduces new data-centric abstractions, memory object and memory object flow, and accompanying memory and communications management methods. The higher-level information captured in the new abstractions empowers the MF runtime to better guide the underlying memory and interconnect management, and to mask the complexities of the underlying memory substrate. Additional benefits are derived from use of near-memory-fabric computation, including via dynamically inserted application-specific codes, which further specialize and accelerate the operations carried out by MF. MF is evaluated using several important application domains, including big data learning and analytics, and traditional high-performance scientific simulations. Its benefits include gains in application performance and resource efficiency, while shielding applications and application developers from the underlying machine details.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.