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1. Lining up Garbage Collection and Application for a Far-Memory-Friendly Runtime

   https://doi.org/10.1145/3749283  

   Li, Shengkai; Wang, Chenxi; Xue, Haonan; Ma, Haoran; Liu, Shi; Qiao, Yifan; Eyolfson, Jonathan; Navasca, Christian; Lu, Shan; Xu, Harry (August 2025, ACM Transactions on Computer Systems)

   Far-memory techniques that enable applications to use remote memory are increasingly appealing in modern data centers, supporting applications’ large memory footprint and improving machines’ resource utilization. Unfortunately, most far-memory techniques focus on OS-level optimizations and are agnostic to managed runtimes and garbage collections (GC) underneath applications written in high-level languages. With different object-access patterns from applications, GC can severely interfere with existing far-memory techniques, breaking remote memory prefetching algorithms and causing severe local-memory misses. We developed MemLiner, a runtime technique that improves the performance of far-memory systems by aligning memory accesses from application and GC threads so that they follow similar memory access paths, thereby (1) reducing the local-memory working set and (2) improving remote-memory prefetching through simplified memory access patterns. We implemented MemLiner in two widely used GCs in OpenJDK: G1 and Shenandoah. Our evaluation with a range of widely deployed cloud systems shows that MemLiner improves applications’ end-to-end performance by up to3.3×and reduces applications’ tail latency by up to220.0×. 

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2. Ferroelectric Memory Technology for Big Data Applications

   https://doi.org/10.1145/3764868  

   Shukla, Nikhil; Ni, Kai; Stevenson, Sam; Narayanan, Vijaykrishnan (August 2025, ACM Transactions on Embedded Computing Systems)

   Big Data has an insatiable appetite for larger and better-performing memory. While current memory technologies continue to advance, the performance gaps in current memory and storage technology have motivated the exploration of emerging memory technologies capable of providing new functionalities. Ferroelectric memory is one such promising candidate which has recently experienced a revival after the discovery of ferroelectricity in hafnium dioxide (HfO₂) – the dielectric of choice in advanced CMOS manufacturing. While the commercial viability of ferroelectric memory technology has made significant progress over the past decade, several challenges related to variation and reliability still stand as a barrier to large-scale commercial implementation. Here, we review some of the outstanding challenges of ferroelectric memory technology along with the recent materials and device innovations that are being considered to overcome them. Moreover, we aim to highlight these challenges as materials and device co-design problems that must be addressed through collaborative efforts that straddle the two disciplines. We identify and provide our perspective on some of the key challenges and opportunities for ferroelectric-based microelectronic technology. Ferroelectrics non-volatile memory, in-memory computation 

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3. Memory Disaggregation: Advances and Open Challenges

   https://doi.org/10.1145/3606557.3606562  

   Al_Maruf, Hasan; Chowdhury, Mosharaf (June 2023, ACM SIGOPS Operating Systems Review)

   Compute and memory are tightly coupled within each server in traditional datacenters. Large-scale datacenter operators have identified this coupling as a root cause behind fleetwide resource underutilization and increasing Total Cost of Ownership (TCO). With the advent of ultra-fast networks and cache-coherent interfaces, memory disaggregation has emerged as a potential solution, whereby applications can leverage available memory even outside server boundaries. This paper summarizes the growing research landscape of memory disaggregation from a software perspective and introduces the challenges toward making it practical under current and future hardware trends. We also reflect on our seven-year journey in the SymbioticLab to build a comprehensive disaggregated memory system over ultra-fast networks. We conclude with some open challenges toward building next-generation memory disaggregation systems leveraging emerging cache-coherent interconnects. 

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4. A Review of Racial and Ethnic Differences in Sleep-memory Associations and the Potential Contributions of Social Determinants of Sleep

   https://doi.org/10.1007/s40675-024-00281-0  

   Hokett, Emily; Duarte, Audrey (March 2024, Current Sleep Medicine Reports)

   Abstract Purpose of ReviewWe highlight evidence within the last 5 years of racial and ethnic differences in sleep-memory associations and explore potential causes and treatments of poor sleep in minoritized racial and ethnic groups. Recent FindingsEmerging research suggests that memory performance in Black adults may be more sensitive to sleep quality than in White adults. Sleep quality and sleep duration are also associated with memory performance in other minoritized racial and ethnic groups, including Asian and Hispanic adults. However, comparisons between groups are scarce. SummaryPoor sleep is associated with poor memory outcomes in minoritized racial and ethnic groups. Social determinants of health, including racism-related stress, discrimination, and neighborhood conditions, may contribute to these associations. More experimental and epidemiology research is needed to fully understand the relationship between sleep and memory performance in minoritized racial and ethnic groups. We conclude with suggested future directions for sleep and memory research. 

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5. Pointers in Far Memory: A rethink of how data and computations should be organized

   https://doi.org/10.1145/3606029  

   Miller, Ethan; Benetopoulos, Achilles; Neville-Neil, George; Mehra, Pankaj; Bittman, Daniel (June 2023, ACM queue)

   Effectively exploiting emerging far-memory technology requires consideration of operating on richly connected data outside the context of the parent process. Operating-system technology in development offers help by exposing abstractions such as memory objects and globally invariant pointers that can be traversed by devices and newly instantiated compute. Such ideas will allow applications running on future heterogeneous distributed systems with disaggregated memory nodes to exploit near-memory processing for higher performance and to independently scale their memory and compute resources for lower cost. 

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