Disaggregated memory is a promising approach that addresses the limitations of traditional memory architectures by enabling memory to be decoupled from compute nodes and shared across a data center. Cloud platforms have deployed such systems to improve overall system memory utilization, but performance can vary across workloads. High-performance computing (HPC) is crucial in scientific and engineering applications, where HPC machines also face the issue of underutilized memory. As a result, improving system memory utilization while understanding workload performance is essential for HPC operators. Therefore, learning the potential of a disaggregated memory system before deployment is a critical step. This paper proposes a methodology for exploring the design space of a disaggregated memory system. It incorporates key metrics that affect performance on disaggregated memory systems: memory capacity, local and remote memory access ratio, injection bandwidth, and bisection bandwidth, providing an intuitive approach to guide machine configurations based on technology trends and workload characteristics. We apply our methodology to analyze thirteen diverse workloads, including AI training, data analysis, genomics, protein, fusion, atomic nuclei, and traditional HPC bookends. Our methodology demonstrates the ability to comprehend the potential and pitfalls of a disaggregated memory system and provides motivation for machine configurations. Our results show that eleven of our thirteen applications can leverage injection bandwidth disaggregated memory without affecting performance, while one pays a rack bisection bandwidth penalty and two pay the system-wide bisection bandwidth penalty. In addition, we also show that intra-rack memory disaggregation would meet the application's memory requirement and provide enough remote memory bandwidth.

翻译：分解式内存是一种有前景的方法，通过将内存与计算节点解耦并在数据中心内共享，突破了传统内存架构的局限性。云平台已部署此类系统以提升整体系统内存利用率，但不同工作负载的性能表现存在差异。高性能计算在科学与工程应用中至关重要，而HPC机器同样面临内存利用率不足的问题。因此，在提升系统内存利用率的同时理解工作负载性能，对HPC运营方至关重要。部署分解式内存系统前评估其潜力是决定性步骤。本文提出了一种探索分解式内存系统设计空间的方法，整合了影响分解式内存系统性能的关键指标：内存容量、本地与远程内存访问比率、注入带宽、对分带宽，基于技术趋势和工作负载特性提供直观的机器配置指导。我们将该方法应用于分析十三种多样化工作负载，涵盖AI训练、数据分析、基因组学、蛋白质、聚变、原子核及传统HPC基准程序。该方法能够揭示分解式内存系统的潜在优势与缺陷，为机器配置提供依据。结果表明，十一种工作负载可受益于注入带宽分解式内存且不影响性能，一种工作负载需承担机架对分带宽代价，两种工作负载需承担全系统对分带宽代价。此外，研究显示机架内内存分解可满足应用的内存需求并提供足够的远程内存带宽。