Relevant bibliographies by topics / Disaggregated Memory

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Disaggregated Memory'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Disaggregated Memory"

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Calciu, Irina, M. Talha Imran, Ivan Puddu, et al. "Using Local Cache Coherence for Disaggregated Memory Systems." ACM SIGOPS Operating Systems Review 57, no. 1 (2023): 21–28. http://dx.doi.org/10.1145/3606557.3606561.

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Disaggregated memory provides many cost savings and resource provisioning benefits for current datacenters, but software systems enabling disaggregated memory access result in high performance penalties. These systems require intrusive code changes to port applications for disaggregated memory or employ slow virtual memory mechanisms to avoid code changes. Such mechanisms result in high overhead page faults to access remote data and high dirty data amplification when tracking changes to cached data at page-granularity. In this paper, we propose a fundamentally new approach for disaggregated me
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Cao, Wenqi, and Ling Liu. "Hierarchical Orchestration of Disaggregated Memory." IEEE Transactions on Computers 69, no. 6 (2020): 844–55. http://dx.doi.org/10.1109/tc.2020.2968525.

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Min, Xinhao, Kai Lu, Pengyu Liu, et al. "SepHash: A Write-Optimized Hash Index On Disaggregated Memory via Separate Segment Structure." Proceedings of the VLDB Endowment 17, no. 5 (2024): 1091–104. http://dx.doi.org/10.14778/3641204.3641218.

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Disaggregated memory separates compute and memory resources into independent pools connected by fast RDMA (Remote Direct Memory Access) networks, which can improve memory utilization, reduce cost, and enable elastic scaling of compute and memory resources. Hash indexes provide high-performance single-point operations and are widely used in distributed systems and databases. However, under disaggregated memory, existing hash indexes suffer from write performance degradation due to high resize overhead and concurrency control overhead. Traditional write-optimized hash indexes are not efficient f
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Maruf, Hasan Al, Yuhong Zhong, Hongyi Wang, Mosharaf Chowdhury, Asaf Cidon, and Carl Waldspurger. "Memtrade: Marketplace for Disaggregated Memory Clouds." ACM SIGMETRICS Performance Evaluation Review 51, no. 1 (2023): 1–2. http://dx.doi.org/10.1145/3606376.3593553.

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We present Memtrade, the first practical marketplace for disaggregated memory clouds. Clouds introduce a set of unique challenges for resource disaggregation across different tenants, including resource harvesting, isolation, and matching. Memtrade allows producer virtual machines (VMs) to lease both their unallocated memory and allocated-but-idle application memory to remote consumer VMs for a limited period of time. Memtrade does not require any modifications to host-level system software or support from the cloud provider. It harvests producer memory using an application-aware control loop
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Maruf, Hasan Al, Yuhong Zhong, Hongyi Wang, Mosharaf Chowdhury, Asaf Cidon, and Carl Waldspurger. "Memtrade: Marketplace for Disaggregated Memory Clouds." Proceedings of the ACM on Measurement and Analysis of Computing Systems 7, no. 2 (2023): 1–27. http://dx.doi.org/10.1145/3589985.

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We present Memtrade, the first practical marketplace for disaggregated memory clouds. Clouds introduce a set of unique challenges for resource disaggregation across different tenants, including resource harvesting, isolation, and matching. Memtrade allows producer virtual machines (VMs) to lease both their unallocated memory and allocated-but-idle application memory to remote consumer VMs for a limited period of time. Memtrade does not require any modifications to host-level system software or support from the cloud provider. It harvests producer memory using an application-aware control loop
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Koo, Bonmoo, Jaesang Hwang, Jonghyeok Park, and Wook-Hee Kim. "Converting Concurrent Range Index Structure to Range Index Structure for Disaggregated Memory." Applied Sciences 13, no. 20 (2023): 11130. http://dx.doi.org/10.3390/app132011130.

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In this work, we propose the Spread approach, which tailors a concurrent range index structure to a range index structure for disaggregated memory connected via RDMA (Remote Direct Memory Access). The Spread approach leverages the concept of tolerating transient inconsistencies in a concurrent range index structure to reduce the amount of expensive RDMA operations. Based on the Spread approach, we converted Blink-tree, a concurrent range index structure, to a range index structure for disaggregated memory called RF-tree. In our experimental study, RF-tree shows comparable performance to Sherma
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Ishizaki, Teruaki, and Yoshiro Yamabe. "Memory-centric Architecture for Disaggregated Computers." NTT Technical Review 19, no. 7 (2021): 65–69. http://dx.doi.org/10.53829/ntr202107fa9.

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Alachiotis, Nikolaos, Panagiotis Skrimponis, Manolis Pissadakis, and Dionisios Pnevmatikatos. "Scalable Phylogeny Reconstruction with Disaggregated Near-memory Processing." ACM Transactions on Reconfigurable Technology and Systems 15, no. 3 (2022): 1–32. http://dx.doi.org/10.1145/3484983.

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Disaggregated computer architectures eliminate resource fragmentation in next-generation datacenters by enabling virtual machines to employ resources such as CPUs, memory, and accelerators that are physically located on different servers. While this paves the way for highly compute- and/or memory-intensive applications to potentially deploy all CPUs and/or memory resources in a datacenter, it poses a major challenge to the efficient deployment of hardware accelerators: input/output data can reside on different servers than the ones hosting accelerator resources, thereby requiring time- and ene
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Widmoser, Manuel, Daniel Kocher, and Nikolaus Augsten. "Scalable Distributed Inverted List Indexes in Disaggregated Memory." Proceedings of the ACM on Management of Data 2, no. 3 (2024): 1–27. http://dx.doi.org/10.1145/3654974.

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Memory disaggregation separates compute (CPU) and main memory resources into disjoint physical units to enable elastic and independent scaling. Connected via high-speed RDMA-enabled networks, compute nodes can directly access remote memory. This setting often requires complex protocols with many network roundtrips as memory nodes have near-zero compute power. In this paper, we design a scalable distributed inverted list index for disaggregated memory architectures. An inverted list index maps a set of terms to lists of documents that contain this term. Current solutions either partition the in
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Lu, Baotong, Kaisong Huang, Chieh-Jan Mike Liang, Tianzheng Wang, and Eric Lo. "DEX: Scalable Range Indexing on Disaggregated Memory." Proceedings of the VLDB Endowment 17, no. 10 (2024): 2603–16. http://dx.doi.org/10.14778/3675034.3675050.

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Memory disaggregation can potentially allow memory-optimized range indexes such as B+-trees to scale beyond one machine while attaining high hardware utilization and low cost. Designing scalable indexes on disaggregated memory, however, is challenging due to rudimentary caching, unprincipled offloading and excessive inconsistency among servers. This paper proposes DEX, a new scalable B+-tree for memory disaggregation. DEX includes a set of techniques to reduce remote accesses, including logical partitioning, lightweight caching and cost-aware offloading. Our evaluation shows that DEX can outpe
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Dissertations / Theses on the topic "Disaggregated Memory"

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Bielski, Maciej. "Nouvelles techniques de virtualisation de la mémoire et des entrées-sorties vers les périphériques pour les prochaines générations de centres de traitement de données basés sur des équipements répartis déstructurés." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT022/document.

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Cette thèse s'inscrit dans le contexte de la désagrégation des systèmes informatiques - une approche novatrice qui devrait gagner en popularité dans le secteur des centres de données. A la différence des systèmes traditionnels en grappes, où les ressources sont fournies par une ou plusieurs machines, dans les systèmes désagrégés les ressources sont fournies par des nœuds discrets, chaque nœud ne fournissant qu'un seul type de ressources (unités centrales de calcul, mémoire, périphériques). Au lieu du terme de machine, le terme de créneau (slot) est utilisé pour décrire une unité de déploiement
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Pipereau, Yohan. "Improving memory usage in virtual machines." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS021.

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Les centres de données reposent largement sur les machines virtuelles comme unité de déploiement afin de garantir une isolation forte entre deux déploiements. L'introduction de la virtualisation a permis une amélioration significative de l'utilisation des ressources d'un datacenter par rapport à la location d'une machine physique pour chaque utilisateur. Malgré ce gain, les machines virtuelles offrent toujours un gain d'utilisation de ressources plus faible que l'isolation fournie par le système d'exploitation à travers des processus. Deux phénomènes expliquent le gain relatif des machines vir
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Bielski, Maciej. "Nouvelles techniques de virtualisation de la mémoire et des entrées-sorties vers les périphériques pour les prochaines générations de centres de traitement de données basés sur des équipements répartis déstructurés." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT022.

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Cette thèse s'inscrit dans le contexte de la désagrégation des systèmes informatiques - une approche novatrice qui devrait gagner en popularité dans le secteur des centres de données. A la différence des systèmes traditionnels en grappes, où les ressources sont fournies par une ou plusieurs machines, dans les systèmes désagrégés les ressources sont fournies par des nœuds discrets, chaque nœud ne fournissant qu'un seul type de ressources (unités centrales de calcul, mémoire, périphériques). Au lieu du terme de machine, le terme de créneau (slot) est utilisé pour décrire une unité de déploiement
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Book chapters on the topic "Disaggregated Memory"

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Wang, Tao, Haikun Liu, and Hai Jin. "Efficient Remote Memory Paging for Disaggregated Memory Systems." In Algorithms and Architectures for Parallel Processing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-22677-9_1.

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Singhal, Sharad, Clarete R. Crasta, Mashood Abdulla, et al. "OpenFAM: A Library for Programming Disaggregated Memory." In OpenSHMEM and Related Technologies. OpenSHMEM in the Era of Exascale and Smart Networks. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04888-3_2.

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Jasny, Matthias, Tobias Ziegler, and Carsten Binnig. "Scalable Data Management on Next-Generation Data Center Networks." In Scalable Data Management for Future Hardware. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74097-8_8.

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Abstract The landscape of distributed Database Management Systems (DBMSs) has been fundamentally transformed by advancements in data center network technologies. Traditionally, these systems were designed to minimize network communication, which was perceived as a significant bottleneck. However, with the advent of high-speed networks and technologies such as Remote Direct Memory Access (RDMA), this perception is changing. RDMA allows direct memory access to remote machines at speeds comparable to local memory access, challenging the long-standing belief that network communication is inherentl
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Keeton, Kimberly, Sharad Singhal, and Michael Raymond. "The OpenFAM API: A Programming Model for Disaggregated Persistent Memory." In OpenSHMEM and Related Technologies. OpenSHMEM in the Era of Extreme Heterogeneity. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04918-8_5.

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Vogel, Lukas, Christoph Anneser, Ferdinand Gruber, Thomas Neumann, Jana Giceva, and Alfons Kemper. "Scaling Beyond DRAM Without Compromising Performance." In Scalable Data Management for Future Hardware. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74097-8_6.

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Abstract In the past, the common assumption was that main memory would become cheap and abundant enough to power database systems solely. This assumption did not hold. This chapter presents approaches on how modern database systems can scale beyond a single server’s main memory capacity by utilizing trends in modern hardware without sacrificing performance. To this end, we present four systems: Mosaic, a storage engine for analytical workloads; Plush, a PMem-optimized storage structure for transactional workloads; PMFDS, a novel programming model for fully disaggregated systems; and finally, F
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Garrison, John S. "The Pleasure of the Sonnets." In The Pleasures of Memory in Shakespeare's Sonnets. Oxford University PressOxford, 2023. http://dx.doi.org/10.1093/oso/9780198857716.003.0003.

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Abstract This chapter pursues two interrelated questions: what constitutes pleasure in Shakespeare’s poems and what pleasure does the reader derive from reading them? The discussion utilizes Roland Barthes’s The Pleasure of the Text as a heuristic framework for pursuing these questions, and the chapter takes Sonnet 18 as its central case study, as this poem captures an elevated state of love that is at heart indescribable. The discussion is enhanced by analysis of collective memory and the promise of bliss in Sonnet 55, Sonnet 122, and Sonnet 126. The discussion underscores how memory—both the
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Grimm, Jannis Julien. "Conclusion and Implications." In Contested Legitimacies. Amsterdam University Press, 2022. http://dx.doi.org/10.5117/9789463722650_ch08.

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This concluding chapter discusses the findings of the previous chapters critically in light of ongoing theoretical debates on the power of nationalism, the role of affect and emotions, the imprint of violent events such as Rabaa on the collective memory of a people, and the resurgence of autocracy in the Middle East. It highlights the merits of a disaggregated and culture-sensitive interactionist approach to the study of protest and repression, reflects on the implications and limitations of this study, and offers points of contact for further comparative research. Finally, the concluding chap
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Skrimponis, Panagiotis, Emmanouil Pissadakis, Nikolaos Alachiotis, and Dionisios Pnevmatikatos. "Accelerating Binarized Convolutional Neural Networks with Dynamic Partial Reconfiguration on Disaggregated FPGAs." In Parallel Computing: Technology Trends. IOS Press, 2020. http://dx.doi.org/10.3233/apc200099.

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Convolutional Neural Networks (CNNs) currently dominate the fields of artificial intelligence and machine learning due to their high accuracy. However, their computational and memory needs intensify with the complexity of the problems they are deployed to address, frequently requiring highly parallel and/or accelerated solutions. Recent advances in machine learning showcased the potential of CNNs with reduced precision, by relying on binarized weights and activations, thereby leading to Binarized Neural Networks (BNNs). Due to the embarassingly parallel and discrete arithmetic nature of the re
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Conference papers on the topic "Disaggregated Memory"

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Tzenetopoulos, Achilleas, Michele Gazzetti, Dimosthenis Masouros, Christian Pinto, Sotirios Xydis, and Dimitrios Soudris. "Disaggregated RDDs: Extending and Analyzing Apache Spark for Memory Disaggregated Infrastructures." In 2024 IEEE International Conference on Cloud Engineering (IC2E). IEEE, 2024. http://dx.doi.org/10.1109/ic2e61754.2024.00019.

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Zhong, Linxuan, Wenxin Li, Yulong Li, et al. "Efficient Disaggregated Memory Eviction with Glitter." In 2024 IEEE International Conference on High Performance Computing and Communications (HPCC). IEEE, 2024. https://doi.org/10.1109/hpcc64274.2024.00090.

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Gao, Xianyang, Xi Chen, and Yun Wang. "Investigating the Memory Extension and Disaggregated Memory Pooling System." In 2024 4th International Conference on Electronic Information Engineering and Computer Science (EIECS). IEEE, 2024. https://doi.org/10.1109/eiecs63941.2024.10799987.

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Boëzennec, Robin, Danilo Carastan-Santos, Fanny Dufossé, and Guillaume Pallez. "Allocation Strategies for Disaggregated Memory in HPC Systems." In 2024 IEEE 31st International Conference on High Performance Computing, Data, and Analytics (HiPC). IEEE, 2024. https://doi.org/10.1109/hipc62374.2024.00010.

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Han, Daegyu, Sucheol Lee, Kyeungpyo Kim, and Sungsoon Park. "Improving Access to Object Storage on Disaggregated Storage." In 2024 13th Non-Volatile Memory Systems and Applications Symposium (NVMSA). IEEE, 2024. http://dx.doi.org/10.1109/nvmsa63038.2024.10693669.

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Sok, Song-Woo, Young Woo Kim, Hyuk Je Kwon, et al. "Ethernet-Based Memory Expansion with Cache-coherent Architecture for Disaggregated Memory Systems." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825328.

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Han, Shaobo, Philip N. Ji, and Ting Wang. "Dual Privacy Protection for Distributed Fiber Sensing with Disaggregated Inference and Fine-tuning of Memory-Augmented Networks." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.w1d.7.

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We propose a memory-augmented model architecture with disaggregated computation infrastructure for fiber sensing event recognition. By leveraging geo-distributed computing resources in optical networks, this approach empowers end-users to customize models while ensuring dual privacy protection.
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Li, Jie, George Michelogiannakis, Samuel Maloney, et al. "Job Scheduling in High Performance Computing Systems with Disaggregated Memory Resources." In 2024 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2024. http://dx.doi.org/10.1109/cluster59578.2024.00033.

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Wahlgren, Jacob, Gabin Schieffer, Maya Gokhale, Roger Pearce, and Ivy Peng. "Disaggregated Memory with SmartNIC Offloading: a Case Study on Graph Processing." In 2024 IEEE 36th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). IEEE, 2024. http://dx.doi.org/10.1109/sbac-pad63648.2024.00022.

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Wang, Jing, Hanzhang Yang, Chao Li, et al. "Boosting Data Center Performance via Intelligently Managed Multi-backend Disaggregated Memory." In SC24: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2024. https://doi.org/10.1109/sc41406.2024.00043.

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Reports on the topic "Disaggregated Memory"

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Kommareddy, Vamsee, Clayton Hughes, Simon David Hammond, and Amro Awad. Opal: A Centralized Memory Manager for Investigating Disaggregated Memory Systems. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1467164.

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