Academic literature on the topic 'Disaggregated Memory'
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Journal articles on the topic "Disaggregated Memory":
Cao, Wenqi, and Ling Liu. "Hierarchical Orchestration of Disaggregated Memory." IEEE Transactions on Computers 69, no. 6 (June 1, 2020): 844–55. http://dx.doi.org/10.1109/tc.2020.2968525.
Calciu, Irina, M. Talha Imran, Ivan Puddu, Sanidhya Kashyap, Hasan Al Maruf, Onur Mutlu, and Aasheesh Kolli. "Using Local Cache Coherence for Disaggregated Memory Systems." ACM SIGOPS Operating Systems Review 57, no. 1 (June 26, 2023): 21–28. http://dx.doi.org/10.1145/3606557.3606561.
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 (June 26, 2023): 1–2. http://dx.doi.org/10.1145/3606376.3593553.
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 (May 19, 2023): 1–27. http://dx.doi.org/10.1145/3589985.
Min, Xinhao, Kai Lu, Pengyu Liu, Jiguang Wan, Changsheng Xie, Daohui Wang, Ting Yao, and Huatao Wu. "SepHash: A Write-Optimized Hash Index On Disaggregated Memory via Separate Segment Structure." Proceedings of the VLDB Endowment 17, no. 5 (January 2024): 1091–104. http://dx.doi.org/10.14778/3641204.3641218.
Ishizaki, Teruaki, and Yoshiro Yamabe. "Memory-centric Architecture for Disaggregated Computers." NTT Technical Review 19, no. 7 (July 2021): 65–69. http://dx.doi.org/10.53829/ntr202107fa9.
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 (October 10, 2023): 11130. http://dx.doi.org/10.3390/app132011130.
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 (September 30, 2022): 1–32. http://dx.doi.org/10.1145/3484983.
Jeong, Yeonwoo, Gyeonghwan Jung, Kyuli Park, Youngjae Kim, and Sungyong Park. "Empirical Analysis of Disaggregated Cloud Memory on Memory Intensive Applications." JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE 23, no. 5 (October 31, 2023): 273–82. http://dx.doi.org/10.5573/jsts.2023.23.5.273.
Gonzalez, Jorge, Mauricio G. Palma, Maarten Hattink, Ruth Rubio-Noriega, Lois Orosa, Onur Mutlu, Keren Bergman, and Rodolfo Azevedo. "Optically connected memory for disaggregated data centers." Journal of Parallel and Distributed Computing 163 (May 2022): 300–312. http://dx.doi.org/10.1016/j.jpdc.2022.01.013.
Dissertations / Theses on the topic "Disaggregated Memory":
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.
This dissertation is positioned in the context of the system disaggregation - a novel approach expected to gain popularity in the data center sector. In traditional clustered systems resources are provided by one or multiple machines. Differently to that, in disaggregated systems resources are provided by discrete nodes, each node providing only one type of resources (CPUs, memory and peripherals). Instead of a machine, the term of a slot is used to describe a workload deployment unit. The slot is dynamically assembled before a workload deployment by the unit called system orchestrator.In the introduction of this work, we discuss the subject of disaggregation and present its benefits, compared to clustered architectures. We also add a virtualization layer to the picture as it is a crucial part of data center systems. It provides an isolation between deployed workloads and a flexible resources partitioning. However, the virtualization layer needs to be adapted in order to take full advantage of disaggregation. Thus, the main contributions of this work are focused on the virtualization layer support for disaggregated memory and devices provisioning.The first main contribution presents the software stack modifications related to flexible resizing of a virtual machine (VM) memory. They allow to adjust the amount of guest (running in a VM) RAM at runtime on a memory section granularity. From the software perspective it is transparent whether they come from local or remote memory banks.As a second main contribution we discuss the notions of inter-VM memory sharing and VM migration in the disaggregation context. We first present how regions of disaggregated memory can be shared between VMs running on different nodes. This sharing is performed in a way that involved guests which are not aware of the fact that they are co-located on the same computing node or not. Additionally, we discuss different flavors of concurrent accesses serialization methods. We then explain how the VM migration term gained a twofold meaning. Because of resources disaggregation, a workload is associated to at least one computing node and one memory node. It is therefore possible that it is migrated to a different computing node and keeps using the same memory, or the opposite. We discuss both cases and describe how this can open new opportunities for server consolidation.The last main contribution of this dissertation is related to disaggregated peripherals virtualization. Starting from the assumption that the architecture disaggregation brings many positive effects in general, we explain why it breaks the passthrough peripheral attachment technique (also known as a direct attachment), which is very popular for its near-native performance. To address this limitation we present a design that adapts the passthrough attachment concept to the architecture disaggregation. By this novel design, disaggregated devices can be directly attached to VMs, as if they were plugged locally. Moreover, all modifications do not involve the guest OS itself, for which the setup of the underlying infrastructure is not visible
Pipereau, Yohan. "Improving memory usage in virtual machines." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS021.
Data-centers rely on virtual machines (VMs) to offer isolation between deployments.While, the use of VMs enables better resource usage compared to running a service per bare-metal machine, it achieves poorer resource usage than multi-processes solutions.This is caused by two phenomenon:At VM allocation time, VMs are scheduled as resource requests on a VM scheduler which perform virtual machine allocations across a set of servers.Optimal solution to this scheduling problem is NP-hard leading to the adoption of heuristic based allocation that let a good percentage of unallocated memory on each servers known as `stranded memory`.At VM runtime, VM memory is consumed on-demand and the difference between memory allocation and usage results in a decent portion of `allocated unused memory` currently impractically usable.First, we propose a transparent solution for applications running inside VMs to remotely access stranded memory in remote machines with fine-grained reservation of remote resources.Second, we review current techniques trying to fit allocated memory to used memory.We show that all these techniques are managed by the hypervisor and introduce performance degradation in VMs and more importantly high response time which makes resource sharing unpractical.Instead, we propose an abstraction to perform VM-initiated memory provisioning and we present early result of fast adaptation of VM memory
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.
This dissertation is positioned in the context of the system disaggregation - a novel approach expected to gain popularity in the data center sector. In traditional clustered systems resources are provided by one or multiple machines. Differently to that, in disaggregated systems resources are provided by discrete nodes, each node providing only one type of resources (CPUs, memory and peripherals). Instead of a machine, the term of a slot is used to describe a workload deployment unit. The slot is dynamically assembled before a workload deployment by the unit called system orchestrator.In the introduction of this work, we discuss the subject of disaggregation and present its benefits, compared to clustered architectures. We also add a virtualization layer to the picture as it is a crucial part of data center systems. It provides an isolation between deployed workloads and a flexible resources partitioning. However, the virtualization layer needs to be adapted in order to take full advantage of disaggregation. Thus, the main contributions of this work are focused on the virtualization layer support for disaggregated memory and devices provisioning.The first main contribution presents the software stack modifications related to flexible resizing of a virtual machine (VM) memory. They allow to adjust the amount of guest (running in a VM) RAM at runtime on a memory section granularity. From the software perspective it is transparent whether they come from local or remote memory banks.As a second main contribution we discuss the notions of inter-VM memory sharing and VM migration in the disaggregation context. We first present how regions of disaggregated memory can be shared between VMs running on different nodes. This sharing is performed in a way that involved guests which are not aware of the fact that they are co-located on the same computing node or not. Additionally, we discuss different flavors of concurrent accesses serialization methods. We then explain how the VM migration term gained a twofold meaning. Because of resources disaggregation, a workload is associated to at least one computing node and one memory node. It is therefore possible that it is migrated to a different computing node and keeps using the same memory, or the opposite. We discuss both cases and describe how this can open new opportunities for server consolidation.The last main contribution of this dissertation is related to disaggregated peripherals virtualization. Starting from the assumption that the architecture disaggregation brings many positive effects in general, we explain why it breaks the passthrough peripheral attachment technique (also known as a direct attachment), which is very popular for its near-native performance. To address this limitation we present a design that adapts the passthrough attachment concept to the architecture disaggregation. By this novel design, disaggregated devices can be directly attached to VMs, as if they were plugged locally. Moreover, all modifications do not involve the guest OS itself, for which the setup of the underlying infrastructure is not visible
Book chapters on the topic "Disaggregated Memory":
Wang, Tao, Haikun Liu, and Hai Jin. "Efficient Remote Memory Paging for Disaggregated Memory Systems." In Algorithms and Architectures for Parallel Processing, 1–20. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-22677-9_1.
Singhal, Sharad, Clarete R. Crasta, Mashood Abdulla, Faizan Barmawer, Dave Emberson, Ramya Ahobala, Gautham Bhat, Rishi kesh K. Rajak, and P. N. Soumya. "OpenFAM: A Library for Programming Disaggregated Memory." In OpenSHMEM and Related Technologies. OpenSHMEM in the Era of Exascale and Smart Networks, 21–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04888-3_2.
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, 70–89. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04918-8_5.
Garrison, John S. "The Pleasure of the Sonnets." In The Pleasures of Memory in Shakespeare's Sonnets, 41–62. Oxford University PressOxford, 2023. http://dx.doi.org/10.1093/oso/9780198857716.003.0003.
Grimm, Jannis Julien. "Conclusion and Implications." In Contested Legitimacies. Nieuwe Prinsengracht 89 1018 VR Amsterdam Nederland: Amsterdam University Press, 2022. http://dx.doi.org/10.5117/9789463722650_ch08.
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.
Conference papers on the topic "Disaggregated Memory":
Calciu, Irina, M. Talha Imran, Ivan Puddu, Sanidhya Kashyap, Hasan Al Maruf, Onur Mutlu, and Aasheesh Kolli. "Rethinking software runtimes for disaggregated memory." In ASPLOS '21: 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3445814.3446713.
Calabretta, Nicola, Xiaotao Guo, Georgios Exarchakos, Xuwei Xue, and Bitao Pan. "Optical Switching for Memory-Disaggregated Datacenters." In Optical Fiber Communication Conference. Washington, D.C.: OSA, 2021. http://dx.doi.org/10.1364/ofc.2021.f2f.4.
Maruf, Hasan Al, Yuhong Zhong, Hongyi Wang, Mosharaf Chowdhury, Asaf Cidon, and Carl Waldspurger. "Memtrade: Marketplace for Disaggregated Memory Clouds." In SIGMETRICS '23: ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3578338.3593553.
Lim, Kevin, Yoshio Turner, Jose Renato Santos, Alvin AuYoung, Jichuan Chang, Parthasarathy Ranganathan, and Thomas F. Wenisch. "System-level implications of disaggregated memory." In 2012 IEEE 18th International Symposium on High Performance Computer Architecture (HPCA). IEEE, 2012. http://dx.doi.org/10.1109/hpca.2012.6168955.
Gonzalez, Jorge, Alexander Gazman, Maarten Hattink, Mauricio G. Palma, Meisam Bahadori, Ruth Rubio-Noriega, Lois Orosa, et al. "Optically Connected Memory for Disaggregated Data Centers." In 2020 IEEE 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). IEEE, 2020. http://dx.doi.org/10.1109/sbac-pad49847.2020.00017.
Zahka, Daniel, and Ada Gavrilovska. "FAM-Graph: Graph Analytics on Disaggregated Memory." In 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2022. http://dx.doi.org/10.1109/ipdps53621.2022.00017.
Zacarias, Felippe, Paul Carpenter, and Vinicius Petrucci. "Dynamic Memory Provisioning on Disaggregated HPC Systems." In SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3624062.3624174.
Aguilera, Marcos K., Naama Ben-David, Rachid Guerraoui, Antoine Murat, Athanasios Xygkis, and Igor Zablotchi. "uBFT: Microsecond-Scale BFT using Disaggregated Memory." In ASPLOS '23: 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3575693.3575732.
Theodoropoulos, Dimitris, Andrea Reale, Dimitris Syrivelis, Maciej Bielski, Nikolaos Alachiotis, and Dionisios Pnevmatikatos. "REMAP: Remote mEmory Manager for disAggregated Platforms." In 2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP). IEEE, 2018. http://dx.doi.org/10.1109/asap.2018.8445095.
Song, Jongtae, Jiwook Youn, Dae-Ub Kim, Kyeong-Eun Han, and Joon Ki Lee. "Performance Evaluation on Optically Disaggregated Memory Architecture." In 2021 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2021. http://dx.doi.org/10.1109/ictc52510.2021.9621006.
Reports on the topic "Disaggregated Memory":
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), August 2018. http://dx.doi.org/10.2172/1467164.