Collaborative Research: PPoSS: Planning: Towards an Integrated, Full-stack System for Memory-centric Computing

协作研究：PPoSS：规划：面向以内存为中心的计算的集成全栈系统

• 批准号: 2028825
• 负责人: Peng Jiang
• 金额: $ 6.45万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028825&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Planning; Towards; Collaborative; Research; PPoSS; Planning; Towards

As the volume of data being processed by today’s systems continues to increase, the traditional organization of memory systems is shifting to accommodate that accelerating growth. Data-centric applications such as irregular graph-mining algorithms, distributed machine learning, and genome sequencing require a large amount of data to compute and store, and generate massive amounts of intermediate data to move around the compute resources. Memory-centric computing is a potential solution to overcome the performance bottleneck of current systems. Near or in-memory computing can mitigate the bandwidth limitations with fewer data movements between the memory and host processing units; a remote memory pool with a fast interconnect shared by all processing units can overcome the current capacity constraints. Both solutions are promising for breaking down the memory wall. However, it is challenging to release the power of both solutions with direct integration. In this project, the investigators propose an integrated, full-stack system to enable memory-centric computing (SMC2). The system will incorporate the emerging near-memory data processors (NDP) and an extensible remote memory pool to minimize the performance impact of memory accesses in graph-mining applications. The research tasks include optimizations in architecture, the software/hardware interface, programming models/compilers, and performance models/optimization. First, the architecture is revisited to utilize the NDP hardware to build an active memory system that supports intelligent data prefetch and speculative data push. Next, the system software is redesigned to support NDP function calls, data-push operations, and virtualization. Then, with new system abstractions, a new programming model is proposed to allow programmers to specify which tasks can run on the NDP resources, and to support efficient NDP-to-NDP communication. Lastly, a new system performance model and optimization framework are incorporated. By putting the four pieces together, the proposed system support can maximize the performance of memory-centric computing with new system abstractions and theories.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.

随着当今系统处理的数据量继续增加，传统的存储系统组织正在转移以适应加速增长。以数据为中心的应用程序（例如不规则的图形开采算法，分布式机器学习和基因组测序）需要大量数据来计算和存储，并生成大量的中间数据以围绕计算资源移动。以内存为中心的计算是克服当前系统的性能瓶颈的潜在解决方案。附近或内存计算可以减轻带宽限制，而在内存和主机处理单元之间的数据移动较少；所有处理单元共享的具有快速互连的远程存储池可以克服当前的容量约束。两种解决方案都承诺要分解记忆墙。但是，要通过直接集成释放两种解决方案的功能是挑战的。在这个项目中，调查人员提出了一个集成的全堆栈系统，以实现以内存为中心的计算（SMC2）。该系统将结合新兴的近内存数据处理器（NDP）和可扩展的遥控器池，以最大程度地减少在图形开采应用程序中内存访问的性能影响。研究任务包括架构，软件/硬件接口，编程模型/编译器以及性能模型/优化方面的优化。首先，对体系结构进行了重新访问以利用NDP硬件来构建一个主动存储系统，该系统支持智能数据预取和投机数据推动。接下来，重新设计系统软件以支持NDP函数调用，数据简介操作和虚拟化。然后，使用新的系统摘要，提出了一个新的编程模型，以允许程序员指定哪些任务可以在NDP资源上运行，并支持有效的NDP-TO-TO-NDP通信。最后，合并了新的系统性能模型和优化框架。通过将四个部分组合在一起，提出的系统支持可以通过新的系统抽象和理论最大化以内存计算的性能。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来通过评估来表现出珍贵的支持。