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Disaggregated memory is being proposed as a way to provide efficient memory scaling for data intensive applications. High performance interconnect technologies, such as CXL, make disaggregated, fabric-attached-memory (FAM) a viable secondary tier of memory. Previous work on remote memory relies on extending kernel level paging to utilize FAM as an additional storage tier after local memory. These approaches have the advantage of exposing remote memory in application transparent ways that do not require code changes, but they incur large overheads due to the mismatch between the abstraction of a flat virtual address space and the reality of the tiered nature of FAM. In this paper, we present an alternative approach to remote memory based on application-specific objects. We design FAM-Graph - a semi-external graph processing system that leverages application-level properties, such as read only edge data, to efficiently tier data between local and remote memory, and prefetch remote data for local computation. Using several graph algorithms and datasets, we demonstrate that FAM-Graph achieves end-to-end performance within factors of 1–6× of Galois, the state of the art shared memory graph processing system, while using up to 20× less local memory. When Galois is used in conjunction with an OS-level FAM solution, we show that FAM-Graph achieves better end-to-end performance by up to 9× when both systems are configured with the same amount of local memory. 

Emerging hybrid memory systems that comprise technologies such as Intel's Optane DC Persistent Memory, exhibit disparities in the access speeds and capacity ratios of their heterogeneous memory components. This breaks many assumptions and heuristics designed for traditional DRAM-only platforms. High application performance is feasible via dynamic data movement across memory units, which maximizes the capacity use of DRAM while ensuring efficient use of the aggregate system resources. Newly proposed solutions use performance models and machine intelligence to optimize which and how much data to move dynamically. However, the decision of when to move this data is based on empirical selection of time intervals, or left to the applications. Our experimental evaluation shows that failure to properly conFigure the data movement frequency can lead to 10%-100% performance degradation for a given data movement policy; yet, there is no established methodology on how to properly conFigure this value for a given workload, platform and policy. We propose Cori, a system-level tuning solution that identifies and extracts the necessary application-level data reuse information, and guides the selection of data movement frequency to deliver gains in application performance and system resource efficiency. Experimental evaluation shows that Cori configures data movement frequencies that provide application performance within 3% of the optimal one, and that it can achieve this up to 5 x more quickly than random or brute-force approaches. System-level validation of Cori on a platform with DRAM and Intel's Optane DC PMEM confirms its practicality and tuning efficiency. 

Application performance improvements in emerging systems with hybrid memory components, such as DRAM and Intel’s Optane DC persistent memory, are possible via periodic data movements, that maximize the DRAM use and system resource efficiency. Similarly, predominantly used NUMA DRAM-only systems benefit from data balancing solutions, such as AutoNUMA, which periodically remap an application and its data on the same NUMA node. Although there has been a significant body of research focused on the clever selection of the data to be moved periodically, there is little insight as to how to select the frequency of the data movements, i.e., the duration of the monitoring period. Our experimental analysis shows that fine-tuning the period frequency can boost application performance on average by 70% for systems with locally attached memory units and 5x when accessing remote memory via interconnection networks. Thus, there is potential for significant performance improvements just by cleverly selecting the frequency of the data movements apart from choosing the data itself. While existing solutions empirically set the duration of the period, our work provides insights into the application-level properties that influence the choice of the period. More specifically, we show that there is a correlation between the application-level data reuse distance and migration frequency. Future work aims to solidify this correlation and build a profiling solution that provides users with the data movement frequency which dynamic data management solutions can then use to enhance performance.