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Memory management

About: Memory management is a research topic. Over the lifetime, 16743 publications have been published within this topic receiving 312028 citations. The topic is also known as: memory allocation.


Papers
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Proceedings ArticleDOI
12 Nov 2007
TL;DR: This paper focuses on the VM-based resource reservation problem, that is, the reservations of CPU, memory and network resources for individual VM instances, as well as for VM clusters, and shows that migration of VMs in parallel results in shorter aggregate migration times, but with higher per-VM migration latencies.
Abstract: Virtual Machines are becoming increasingly valuable to resource consolidation and management, providing efficient and secure resource containers, along with desired application execution environments. This paper focuses on the VM-based resource reservation problem, that is, the reservations of CPU, memory and network resources for individual VM instances, as well as for VM clusters. In particular, it considers the scenario where one or several physical servers need to be vacated to start a cluster of VMs for dedicated execution of parallel jobs. VMs provide a primitive for transparently vacating workloads through migration; however, the process of migrating several VMs can be time-consuming and needs to be estimated. To achieve this goal, this paper seeks to provide a model that can characterize the VM migration process and predict its performance, based on a comprehensive experimental analysis. The results show that, given a certain VM's migration time, it is feasible to predict the time for a VM with other configurations, as well as the time for migrating a number of VMs. The paper also shows that migration of VMs in parallel results in shorter aggregate migration times, but with higher per-VM migration latencies. Experimental results also quantify the benefits of buffering the state of migrated VMs in main memory without committing to hard disks.

200 citations

Proceedings ArticleDOI
04 Oct 2010
TL;DR: DieHarder as mentioned in this paper analyzes a range of widely deployed memory allocators, including those used by Windows, Linux, FreeBSD and OpenBSD, and shows that they remain vulnerable to heap-based attacks.
Abstract: Heap-based attacks depend on a combination of memory management error and an exploitable memory allocator. Many allocators include ad hoc countermeasures against particular exploits but their effectiveness against future exploits has been uncertain. This paper presents the first formal treatment of the impact of allocator design on security. It analyzes a range of widely-deployed memory allocators, including those used by Windows, Linux, FreeBSD and OpenBSD, and shows that they remain vulnerable to attack. It them presents DieHarder, a new allocator whose design was guided by this analysis. DieHarder provides the highest degree of security from heap-based attacks of any practical allocator of which we are aware while imposing modest performance overhead. In particular, the Firefox web browser runs as fast with DieHarder as with the Linux allocator.

199 citations

Journal ArticleDOI
TL;DR: The Data Diffusion Machine (DDM) as mentioned in this paper is a cache-only memory architecture that relies on a hierarchical network structure, and it can be seen as an extension of the COMA.
Abstract: The Data Diffusion Machine (DDM), a cache-only memory architecture (COMA) that relies on a hierarchical network structure, is described. The key ideas behind DDM are introduced by describing a small machine, which could be a COMA on its own or a subsystem of a larger COMA, and its protocol. A large machine with hundreds of processors is also described. The DDM prototype project is discussed, and simulated performance results are presented. >

199 citations

Patent
14 Mar 2002
TL;DR: In this paper, a smart memory computing system that uses smart memory for massive data storage as well as for massive parallel execution is described, where the data stored in the smart memory can be accessed just like the conventional main memory, but the execution units also have many execution units to process data in situ.
Abstract: A smart memory computing system that uses smart memory for massive data storage as well as for massive parallel execution is disclosed. The data stored in the smart memory can be accessed just like the conventional main memory, but the smart memory also has many execution units to process data in situ. The smart memory computing system offers improved performance and reduced costs for those programs having massive data-level parallelism. This smart memory computing system is able to take advantage of data-level parallelism to improve execution speed by, for example, use of inventive aspects such as algorithm mapping, compiler techniques, architecture features, and specialized instruction sets.

196 citations

Proceedings ArticleDOI
01 Feb 2018
TL;DR: Many artificial intelligence (AI) edge devices use nonvolatile memory (NVM) to store the weights for the neural network (trained off-line on an AI server), and require low-energy and fast I/O accesses.
Abstract: Many artificial intelligence (AI) edge devices use nonvolatile memory (NVM) to store the weights for the neural network (trained off-line on an AI server), and require low-energy and fast I/O accesses. The deep neural networks (DNN) used by AI processors [1,2] commonly require p-layers of a convolutional neural network (CNN) and q-layers of a fully-connected network (FCN). Current DNN processors that use a conventional (von-Neumann) memory structure are limited by high access latencies, I/O energy consumption, and hardware costs. Large working data sets result in heavy accesses across the memory hierarchy, moreover large amounts of intermediate data are also generated due to the large number of multiply-and-accumulate (MAC) operations for both CNN and FCN. Even when binary-based DNN [3] are used, the required CNN and FCN operations result in a major memory I/O bottleneck for AI edge devices.

195 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202333
202288
2021629
2020467
2019461
2018591