Готові списки джерел за темами / Automatic dynamic memory management

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Добірка наукової літератури з теми "Automatic dynamic memory management"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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Статті в журналах з теми "Automatic dynamic memory management"

APPEL, ANDREW W. "Garbage Collection: Algorithms for Automatic Dynamic Memory Management by Richard Jones and Rafael Lins, John Wiley & Sons, 1996." Journal of Functional Programming 7, no. 2 (March 1997): 227–29. http://dx.doi.org/10.1017/s0956796897212682.

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Qu, Yanyu, Fangling Pu, Jianguo Yin, Lingzi Liu, and Xin Xu. "Dynamic Traffic Detection and Modeling for Beidou Satellite Networks." Journal of Sensors 2020 (January 22, 2020): 1–11. http://dx.doi.org/10.1155/2020/4575721.

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Beidou navigation system (BDS) has been developed as an integrated system. The third BDS, BSD-3, will be capable of providing not only global positioning and navigation but also data communication. When the volume of data transmitted through BDS-3 continues to increase, BDS-3 will encounter network traffic congestion, unbalanced resource usage, or security attacks as terrestrial networks. The network traffic monitoring is essential for automatic management and safety assurance of BDS-3. A dynamic traffic detection method including traffic prediction by Long Short-Term Memory (LSTM) and a dynamically adjusting polling strategy is proposed to unevenly sample the traffic of each link. A distributed traffic detection architecture is designed for collection of the detected traffic and its related temporal and spatial information with low delay. A time-varying graph (TVG) model is introduced to represent the dynamic topology, the time-varying link, and its traffic. The BDS-3 network is simulated by STK. The WIDE dataset is used to simulate the traffic between the satellite and ground station. Simulation results show that the dynamic traffic detection method can follow the variation of the traffic of each link with uneven sampling. The detected traffic can be transmitted to the ground station in near real time through the distributed traffic detection architecture. The traffic and its related information are stored by using Neo4j in terms of the TVG model. The nodes, edges, and traffic of BDS-3 can be quickly queried through Neo4j. The presented dynamic traffic detection and representation schemes will support BDS-3 to establish automatic management and security system and develop business.

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Li, A. Xiaochang, B. Jichen Chen, C. Zhengjun Zhai, D. Mingchen Feng, and E. Xin Ye. "URM: A Unified RAM Management Scheme for NAND Flash Storage Devices." Discrete Dynamics in Nature and Society 2022 (May 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/3376904.

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In NAND flash storage devices, the random access memory (RAM) is composed of a data buffer and mapping cache that play critical roles in storage performance. Furthermore, as the capacity growth rate of RAM chips lags far behind that of flash memory chips, determining how to take advantage of precious RAM is still a crucial issue. However, most existing buffer management studies on storage devices report performance degradation since these devices cannot refine reference regularities such as sequential, hot, or looping data patterns. In addition, most of these studies focus only on separately managing the data buffer or mapping cache. Compared with the existing buffer/cache management schemes (BMSs), we propose a unified RAM management (URM) scheme for not only the mapping cache but also the data buffer in NAND flash storage devices. URM compresses the mapping table to save memory space, and the remaining dynamic RAM space is used for the data buffer. For the data buffer part, we utilize the program counter-technique in the host layer that provides automatic pattern recognition for different applications, in contrast to existing BMSs. The program counter-technique in our design is able to distinguish four patterns. According to these patterns, the data buffer is divided into four size-adjustable zones. Therefore, our approach is linked to multimodal data and used in a data-intensive system. In particular, in URM, we use a multivariate classification to predict prefetching length in mapping buffer management. Our multivariate classification is transformed into multiple binary classifications (logistic regressions). Finally, we extensively evaluate URM using various realistic workloads, and the experimental results show that, compared with three data buffer management schemes, CFLRU, BPLRU, and VBBMS, URM can improve the hit ratio of data buffer and save response time by an average to 32% and 18%, respectively.

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Chen, Zai Ping, and Ya Jing Yang. "The Application and Implementation of ADO Technology in Industrial Management System Based on MFC." Advanced Materials Research 791-793 (September 2013): 1562–65. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1562.

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With the rapid development of computer network, automation and electronic commerce, more and more industrial production are in urgent need of the convenient and efficient integration of production and management system to adapt to the change and solve the condition of the onerous task, low efficiency, high cost, personnel tension. This article focuses on a typical industrial management system which is based on C/S architecture and uses MFC application framework. And it introduces ADO technology as the middle bridge which is used by the client to access databases. What is important is that the dynamic link library is used in the system to help ADO access database. The ADO technology has the characteristics of easily-using, high speed, low memory expenditure and less disk space, which optimize the system, make the system realize the high performance, high compatible data access.

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Corker, Kevin M. "Cognitive Performance for Multiple Operators in Complex Dynamic Airspace Systems: Computational Representation and Empirical Analyses." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 42, no. 3 (October 1998): 341–45. http://dx.doi.org/10.1177/154193129804200334.

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This paper presents a set of studies in full mission simulation and the development of a predictive computational model of human performance in control of complex airspace operations. NASA and the FAA have initiated programs of research and development to provide flight crew, airline operations and air traffic managers with automation aids to increase capacity in enroute and terminal area to support the goals of safe, flexible, predictable and efficient operations. In support of these developments, we present a computational model to aid design that includes representation of multiple cognitive agents (both human operators and intelligent aiding systems). The demands of air traffic management require representation of many intelligent agents sharing world-models, coordinating action/intention, and scheduling goals and actions in a potentially unpredictable world of operations. The operator-model structure includes attention functions, action priority, and situation assessment. The cognitive model has been expanded to include working memory operations including retrieval from long-term store, and interference. The operator's activity structures have been developed to provide for anticipation through knowledge of the intention and action of remote operators. System stability and operator actions can be predicted by using the model. The model's predictive accuracy was verified using the full-mission simulation data of commercial flight deck operations with advanced air traffic management techniques.

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Du, Wenjun, Bo Sun, Jiating Kuai, Jiemin Xie, Jie Yu, and Tuo Sun. "Highway Travel Time Prediction of Segments Based on ANPR Data considering Traffic Diversion." Journal of Advanced Transportation 2021 (July 9, 2021): 1–16. http://dx.doi.org/10.1155/2021/9512501.

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Travel time is one of the most critical parameters in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid model named LSTM-CNN for predicting the travel time of highways by integrating the long short-term memory (LSTM) and the convolutional neural networks (CNNs) with the attention mechanism and the residual network. The highway is divided into multiple segments by considering the traffic diversion and the relative location of automatic number plate recognition (ANPR). There are four steps in this hybrid approach. First, the average travel time of each segment in each interval is calculated from ANPR and fed into LSTM in the form of a multidimensional array. Second, the attention mechanism is adopted to combine the hidden layer of LSTM with dynamic temporal weights. Third, the residual network is introduced to increase the network depth and overcome the vanishing gradient problem, which consists of three pairs of one-dimensional convolutional layers (Conv1D) and batch normalization (BatchNorm) with the rectified linear unit (ReLU) as the activation function. Finally, a series of Conv1D layers is connected to extract features further and reduce dimensionality. The proposed LSTM-CNN approach is tested on the three-month ANPR data of a real-world 39.25 km highway with four pairs of ANPR detectors of the uplink and downlink, Zhejiang, China. The experimental results indicate that LSTM-CNN learns spatial, temporal, and depth information better than the state-of-the-art traffic forecasting models, so LSTM-CNN can predict more accurate travel time. Moreover, LSTM-CNN outperforms the state-of-the-art methods in nonrecurrent prediction, multistep-ahead prediction, and long-term prediction. LSTM-CNN is a promising model with scalability and portability for highway traffic prediction and can be further extended to improve the performance of the advanced traffic management system (ATMS) and advanced traffic information system (ATIS).

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Laqtib, Safaa, Khalid El Yassini, and Moulay Lahcen Hasnaoui. "A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (June 1, 2020): 2701. http://dx.doi.org/10.11591/ijece.v10i3.pp2701-2709.

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Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network Inception-CNN, Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning methods in MANET.

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Gunawan, Chichi Rizka, Nurdin Nurdin, and Fajriana Fajriana. "Design of A Real-Time Object Detection Prototype System with YOLOv3 (You Only Look Once)." International Journal of Engineering, Science and Information Technology 2, no. 3 (October 17, 2022): 96–99. http://dx.doi.org/10.52088/ijesty.v2i3.309.

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Object detection is an activity that aims to gain an understanding of the classification, concept estimation, and location of objects in an image. As one of the fundamental computer vision problems, object detection can provide valuable information for the semantic understanding of images and videos and is associated with many applications, including image classification. Object detection has recently become one of the most exciting fields in computer vision. Detection of objects on this system using YOLOv3. The You Only Look Once (YOLO) method is one of the fastest and most accurate methods for object detection and is even capable of exceeding two times the capabilities of other algorithms. You Only Look Once, an object detection method, is very fast because a single neural network predicts bounded box and class probabilities directly from the whole image in an evaluation. In this study, the object under study is an object that is around the researcher (a random thing). System design using Unified Modeling Language (UML) diagrams, including use case diagrams, activity diagrams, and class diagrams. This system will be built using the python language. Python is a high-level programming language that can execute some multi-use instructions directly (interpretively) with the Object Oriented Programming method and also uses dynamic semantics to provide a level of syntax readability. As a high-level programming language, python can be learned easily because it has been equipped with automatic memory management, where the user must run through the Anaconda prompt and then continue using Jupyter Notebook. The purpose of this study was to determine the accuracy and performance of detecting random objects on YOLOv3. The result of object detection will display the name and bounding box with the percentage of accuracy. In this study, the system is also able to recognize objects when they object is stationary or moving.

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Wu, Hong, Jiatong Ji, Haimei Tian, Yao Chen, Weihong Ge, Haixia Zhang, Feng Yu, Jianjun Zou, Mitsuhiro Nakamura, and Jun Liao. "Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding–Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model." JMIR Medical Informatics 9, no. 12 (December 1, 2021): e26407. http://dx.doi.org/10.2196/26407.

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Background With the increasing variety of drugs, the incidence of adverse drug events (ADEs) is increasing year by year. Massive numbers of ADEs are recorded in electronic medical records and adverse drug reaction (ADR) reports, which are important sources of potential ADR information. Meanwhile, it is essential to make latent ADR information automatically available for better postmarketing drug safety reevaluation and pharmacovigilance. Objective This study describes how to identify ADR-related information from Chinese ADE reports. Methods Our study established an efficient automated tool, named BBC-Radical. BBC-Radical is a model that consists of 3 components: Bidirectional Encoder Representations from Transformers (BERT), bidirectional long short-term memory (bi-LSTM), and conditional random field (CRF). The model identifies ADR-related information from Chinese ADR reports. Token features and radical features of Chinese characters were used to represent the common meaning of a group of words. BERT and Bi-LSTM-CRF were novel models that combined these features to conduct named entity recognition (NER) tasks in the free-text section of 24,890 ADR reports from the Jiangsu Province Adverse Drug Reaction Monitoring Center from 2010 to 2016. Moreover, the man-machine comparison experiment on the ADE records from Drum Tower Hospital was designed to compare the NER performance between the BBC-Radical model and a manual method. Results The NER model achieved relatively high performance, with a precision of 96.4%, recall of 96.0%, and F1 score of 96.2%. This indicates that the performance of the BBC-Radical model (precision 87.2%, recall 85.7%, and F1 score 86.4%) is much better than that of the manual method (precision 86.1%, recall 73.8%, and F1 score 79.5%) in the recognition task of each kind of entity. Conclusions The proposed model was competitive in extracting ADR-related information from ADE reports, and the results suggest that the application of our method to extract ADR-related information is of great significance in improving the quality of ADR reports and postmarketing drug safety evaluation.

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Chang, J. Morris, Witawas Srisa-an, Chia-Tien Dan Lo, and Edward F. Gehringer. "DMMX: Dynamic memory management extensions." Journal of Systems and Software 63, no. 3 (September 2002): 187–99. http://dx.doi.org/10.1016/s0164-1212(02)00014-6.

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Дисертації з теми "Automatic dynamic memory management"

Österlund, Erik. "Automatic memory management system for automatic parallelization." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-13693.

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With Moore’s law coming to an end and the era of multiprocessor chips emerging, the need for ways of dealing with the essential problems with concurrency is becoming imminent. Automatic parallelization for imperative languages and pure functions in functional programming languages all try to prove independence statically. This thesis argues that independence is dynamic in nature. Static analysis for automatic parallelization has failed to do anything but trivial optimizations. This thesis shows a new approach where dynamic analysis about the system is provided for very low costs using a garbage collector that has to go through all live cells anyway. Immutable sub-graphs of objects that cannot change state are found. Their methods become pure functions that can be parallelized. The garbage collector implemented is a kind of replicating collector. It is about three times faster than Boehm’s collector in garbage collection, fully concurrent and provides the dynamic analysis almost for free.

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Stojanovic, Marta. "Automatic memory management in Java." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ65392.pdf.

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Doddapaneni, Srinivas P. "Automatic dynamic decomposition of programs on distributed memory machines." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/8158.

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Zhang, Yang. "Dynamic Memory Management for the Loci Framework." MSSTATE, 2004. http://sun.library.msstate.edu/ETD-db/theses/available/etd-04062004-215627/.

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Resource management is a critical part in high-performance computing software. While management of processing resources to increase performance is the most critical, efficient management of memory resources plays an important role in solving large problems. This thesis research seeks to create an effective dynamic memory management scheme for a declarative data-parallel programming system. In such systems, some sort of automatic resource management is a requirement. Using the Loci framework, this thesis research focuses on exploring such opportunities. We believe there exists an automatic memory management scheme for such declarative data-parallel systems that provides good compromise between memory utilization and performance. In addition to basic memory management, this thesis research also seeks to develop methods that take advantages of the cache memory subsystem and explore balances between memory utilization and parallel communication costs in such declarative data-parallel frameworks.

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Van, Vleet Taylor. "Dynamic cache-line sizes /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/6899.

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Herrmann, Edward C. "Threaded Dynamic Memory Management in Many-Core Processors." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1277132326.

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Burrell, Tiffany. "System Identification in Automatic Database Memory Tuning." Scholar Commons, 2010. https://scholarcommons.usf.edu/etd/1583.

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Databases are very complex systems that require database system administrators to perform system tuning in order to achieve optimal performance. Memory tuning is vital to the performance of a database system because when the database workload exceeds its memory capacity, the results of the queries running on a system are delayed and can cause substantial user dissatisfaction. In order to solve this problem, this thesis presents a platform modeled after a closed control feedback loop to control the level of multi-query processing. Utilizing this platform provides two key assets. First, the system identification is acquired, which is one of two crucial steps involved in developing a closed feedback loop. Second, the platform provides a means to experimentally study database tuning problem and verify the effectiveness of research ideas related to database performance.

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Ma, Yuke. "Design, Test and Implement a Reflective Scheduler with Task Partitioning Support of a Grid." Thesis, Cranfield University, 2008. http://hdl.handle.net/1826/3510.

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How to manage a dynamic environment and how to provide task partitioning are two key concerns when developing distributed computing applications. The emergence of Grid computing environments extends these problems. Conventional resource management systems are based on a relatively static resource model and a centralized scheduler that assigns computing resources to users. Distributed management introduces resource heterogeneity: not only the set of available resources, but even the set of resource types is constantly changing. Obviously this is unsuitable for the present Grid. In addition, the Grid provides users with the physical infrastructure to run parallel programs. Because of this increasing availability, there are more requirements for parallelization technologies. Therefore, based on problems outlined above, this thesis provides a novel scheduler which not only enables dynamic management but also provides skeleton library to support the task partition. Dynamic management is derived from the concept of reflectiveness, which allows the Grid to perform like an efficient market with some limited government controls. To supplement the reflective mechanism, this thesis integrates a statistical forecasting approach to predict the environment of the Grid in the next period. The task partitioning support is extended from the skeleton library in the parallel computing and cluster computing areas. The thesis shows how this idea can be applied in the Grid environment to simplify the user’s programming works. Later in this PhD thesis, a Petri-net based simulation methodology is introduced to examine the performance of the reflective scheduler. Moreover, a real testing environment is set up by using a reflective scheduler to run a geometry optimization application. In summary, by combining knowledge from economics, statistics, mathematics and computer science, this newly invented scheduler not only provides a convenient and efficient way to parallelize users’ tasks, but also significantly improves the performance of the Grid.

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Li, Wentong Kavi Krishna M. "High performance architecture using speculative threads and dynamic memory management hardware." [Denton, Tex.] : University of North Texas, 2007. http://digital.library.unt.edu/permalink/meta-dc-5150.

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Li, Wentong. "High Performance Architecture using Speculative Threads and Dynamic Memory Management Hardware." Thesis, University of North Texas, 2007. https://digital.library.unt.edu/ark:/67531/metadc5150/.

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With the advances in very large scale integration (VLSI) technology, hundreds of billions of transistors can be packed into a single chip. With the increased hardware budget, how to take advantage of available hardware resources becomes an important research area. Some researchers have shifted from control flow Von-Neumann architecture back to dataflow architecture again in order to explore scalable architectures leading to multi-core systems with several hundreds of processing elements. In this dissertation, I address how the performance of modern processing systems can be improved, while attempting to reduce hardware complexity and energy consumptions. My research described here tackles both central processing unit (CPU) performance and memory subsystem performance. More specifically I will describe my research related to the design of an innovative decoupled multithreaded architecture that can be used in multi-core processor implementations. I also address how memory management functions can be off-loaded from processing pipelines to further improve system performance and eliminate cache pollution caused by runtime management functions.

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Книги з теми "Automatic dynamic memory management"

Rafael, Lins, ed. Garbage collection: Algorithms for automatic dynamic memory management. Chichester, [Eng.]: Wiley, 1996.

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Atienza Alonso, David, Stylianos Mamagkakis, Christophe Poucet, Miguel Peón-Quirós, Alexandros Bartzas, Francky Catthoor, and Dimitrios Soudris. Dynamic Memory Management for Embedded Systems. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10572-7.

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Daconta, Michael C. C++ pointers and dynamic memory management. New York: Wiley, 1995.

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C pointers and dynamic memory management. Boston: QED Pub. Group, 1993.

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Antony, Hosking, and Moss Eliot, eds. The garbage collection handbook: The art of automatic memory management. Boca Raton, FL: CRC Press, 2012.

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Easy Oracle automation: Oracle 10g automatic storage, memory and diagnostic features. [Kittrell, N.C.]: Rampant TechPress, 2004.

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Dave, Schnee, Mullin Peter, and International Business Machines Corporation. International Technical Support Organization., eds. Automatic partition resource manager for System i and iSeries. [Poughkeepsie, NY]: IBM, International Technical Suppport Organization, 2007.

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Magic tricks, card shuffling, and dynamic computer memories. [Washington, D.C.]: Mathematical Association of America, 1998.

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Wong, Gregory L. The Dynamic Planner: The sequencer, scheduler, and runway allocator for aircraft traffic control automation. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 2000.

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M, Marinaro, Scarpetta Silvia, and Yamaguchi Yoko, eds. Dynamic brain--from neural spikes to behaviors: 12th International Summer School on Neural Networks, Erice, Italy, December 5-12, 2007 : revised lectures. Berlin: Springer, 2008.

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Частини книг з теми "Automatic dynamic memory management"

Graba, Jan. "Dynamic Memory Management." In Up and Running with C++, 85–96. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-3433-6_8.

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Mailund, Thomas. "Dynamic Memory Management." In Pointers in C Programming, 219–58. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6927-5_9.

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Wilson, Paul R., Mark S. Johnstone, Michael Neely, and David Boles. "Dynamic storage allocation: A survey and critical review." In Memory Management, 1–116. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60368-9_19.

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Wadler, Philip. "Static analysis refuses to stay still: Prospects of static analyis for dynamic allocation." In Memory Management, 117. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60368-9_20.

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Jagtap, Amol M., and Ajit S. Mali. "Void Pointer and Dynamic Memory Management." In Data Structures using C, 75–96. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003105800-3.

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Nguyen, Tam M., and Vason P. Srini. "A Dynamic Memory Management Scheme for Shared Memory Multiprocessors." In Scalable Shared Memory Multiprocessors, 313–29. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3604-8_17.

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Atienza Alonso, David, Stylianos Mamagkakis, Christophe Poucet, Miguel Peón-Quirós, Alexandros Bartzas, Francky Catthoor, and Dimitrios Soudris. "Profiling and Analysis of Dynamic Applications." In Dynamic Memory Management for Embedded Systems, 51–92. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10572-7_3.

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Atienza Alonso, David, Stylianos Mamagkakis, Christophe Poucet, Miguel Peón-Quirós, Alexandros Bartzas, Francky Catthoor, and Dimitrios Soudris. "Intermediate Variable Removal from Dynamic Applications." In Dynamic Memory Management for Embedded Systems, 135–66. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10572-7_5.

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Atienza Alonso, David, Stylianos Mamagkakis, Christophe Poucet, Miguel Peón-Quirós, Alexandros Bartzas, Francky Catthoor, and Dimitrios Soudris. "Dynamic Memory Management Optimization for Multimedia Applications." In Dynamic Memory Management for Embedded Systems, 167–92. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10572-7_6.

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Kashiji, Shinkaku, Toru Sumitomo, Kazuhiro Morita, Masaki Oono, Masao Fuketa, and Jun-ichi Aoe. "A Fast Dynamic Method Using Memory Management." In Lecture Notes in Computer Science, 558–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30132-5_78.

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Тези доповідей конференцій з теми "Automatic dynamic memory management"

Haggander, D., P. Liden, and L. Lundberg. "A method for automatic optimization of dynamic memory management in C++." In Proceedings International Conference on Parallel Processing. IEEE, 2001. http://dx.doi.org/10.1109/icpp.2001.952096.

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Chen, G., O. Ozturk, M. Kandemir, and M. Karakoy. "Dynamic Scratch-Pad Memory Management for Irregular Array Access Patterns." In 2006 Design, Automation and Test in Europe. IEEE, 2006. http://dx.doi.org/10.1109/date.2006.243810.

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Ahn, Junwhan, Sungjoo Yoo, and Kiyoung Choi. "Dynamic Power Management of Off-Chip Links for Hybrid Memory Cubes." In the The 51st Annual Design Automation Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2593069.2593128.

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Kannan, Arun, Aviral Shrivastava, Amit Pabalkar, and Jong-eun Lee. "A software solution for dynamic stack management on scratch pad memory." In 2009 Asia and South Pacific Design Automation Conference (ASP-DAC). IEEE, 2009. http://dx.doi.org/10.1109/aspdac.2009.4796548.

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Siddhu, Lokesh, Rajesh Kedia, and Preeti Ranjan Panda. "CoreMemDTM: Integrated Processor Core and 3D Memory Dynamic Thermal Management for Improved Performance." In 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2022. http://dx.doi.org/10.23919/date54114.2022.9774743.

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Souza, Francisco Vieira de, and Rafael Dueire Lins. "Aspectos do Comportamento Espaço-temporal de Programas Funcionais em Uni e Multiprocessadores." In Simpósio Brasileiro de Arquitetura de Computadores e Processamento de Alto Desempenho. Sociedade Brasileira de Computação, 1998. http://dx.doi.org/10.5753/sbac-pad.1998.22689.

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Анотація:

This paper analyses the performance of two of the most important techniques for automatic dynamic memory management in lazy functional languages. Our results show that, as garbage, collection enforces data locality, it is cheaper to garbage collect than to avoid it by increasing heap size. We observed this behaviour both in uni and multiprocessors running on operating systems with virtual memory.

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Jones, Richard. "Dynamic memory management." In the 2007 International Lisp Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1622123.1622126.

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Rahman, Md Mazder, Konstantin Nasartschuk, Kenneth B. Kent, and Gerhard W. Dueck. "Trace Files for Automatic Memory Management Systems." In 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER). IEEE, 2016. http://dx.doi.org/10.1109/saner.2016.118.

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Krishnamoorthy, Sriram, Umit Catalyurek, Jarek Nieplocha, Atanas Rountev, and P. Sadayappan. "Hypergraph Partitioning for Automatic Memory Hierarchy Management." In ACM/IEEE SC 2006 Conference (SC'06). IEEE, 2006. http://dx.doi.org/10.1109/sc.2006.36.

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Ungar, David, David Grove, and Hubertus Franke. "Dynamic atomicity: optimizing swift memory management." In SPLASH '17: Conference on Systems, Programming, Languages, and Applications: Software for Humanity. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3133841.3133843.

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Звіти організацій з теми "Automatic dynamic memory management"

Bartlett, Roscoe A. Teuchos::RefCountPtr beginner's guide : an introduction to the Trilinos smart reference-counted pointer class for (almost) automatic dynamic memory management in C++. Office of Scientific and Technical Information (OSTI), June 2004. http://dx.doi.org/10.2172/919177.

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Vineyard, Craig Michael, and Stephen Joseph Verzi. A Case Study on Neural Inspired Dynamic Memory Management Strategies for High Performance Computing. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1396076.

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Brown, J. C., and A. A. Mirin. MICA, a facility to achieve portability for message-passing and dynamic memory management in FORTRAN. Office of Scientific and Technical Information (OSTI), January 1994. http://dx.doi.org/10.2172/10123838.

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