Littérature scientifique sur le sujet « GPU-CPU »
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Articles de revues sur le sujet "GPU-CPU"
Zhu, Ziyu, Xiaochun Tang et Quan Zhao. « A unified schedule policy of distributed machine learning framework for CPU-GPU cluster ». Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no 3 (juin 2021) : 529–38. http://dx.doi.org/10.1051/jnwpu/20213930529.
Texte intégralCui, Pengjie, Haotian Liu, Bo Tang et Ye Yuan. « CGgraph : An Ultra-Fast Graph Processing System on Modern Commodity CPU-GPU Co-processor ». Proceedings of the VLDB Endowment 17, no 6 (février 2024) : 1405–17. http://dx.doi.org/10.14778/3648160.3648179.
Texte intégralLee, Taekhee, et Young J. Kim. « Massively parallel motion planning algorithms under uncertainty using POMDP ». International Journal of Robotics Research 35, no 8 (21 août 2015) : 928–42. http://dx.doi.org/10.1177/0278364915594856.
Texte intégralYogatama, Bobbi W., Weiwei Gong et Xiangyao Yu. « Orchestrating data placement and query execution in heterogeneous CPU-GPU DBMS ». Proceedings of the VLDB Endowment 15, no 11 (juillet 2022) : 2491–503. http://dx.doi.org/10.14778/3551793.3551809.
Texte intégralPower, Jason, Joel Hestness, Marc S. Orr, Mark D. Hill et David A. Wood. « gem5-gpu : A Heterogeneous CPU-GPU Simulator ». IEEE Computer Architecture Letters 14, no 1 (1 janvier 2015) : 34–36. http://dx.doi.org/10.1109/lca.2014.2299539.
Texte intégralRaju, K., et Niranjan N Chiplunkar. « PERFORMANCE ENHANCEMENT OF CUDA APPLICATIONS BY OVERLAPPING DATA TRANSFER AND KERNEL EXECUTION ». Applied Computer Science 17, no 3 (30 septembre 2021) : 5–18. http://dx.doi.org/10.35784/acs-2021-17.
Texte intégralLiu, Gaogao, Wenbo Yang, Peng Li, Guodong Qin, Jingjing Cai, Youming Wang, Shuai Wang, Ning Yue et Dongjie Huang. « MIMO Radar Parallel Simulation System Based on CPU/GPU Architecture ». Sensors 22, no 1 (5 janvier 2022) : 396. http://dx.doi.org/10.3390/s22010396.
Texte intégralZou, Yong Ning, Jue Wang et Jian Wei Li. « Cutting Display of Industrial CT Volume Data Based on GPU ». Advanced Materials Research 271-273 (juillet 2011) : 1096–102. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1096.
Texte intégralJiang, Ronglin, Shugang Jiang, Yu Zhang, Ying Xu, Lei Xu et Dandan Zhang. « GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform ». International Journal of Antennas and Propagation 2014 (2014) : 1–8. http://dx.doi.org/10.1155/2014/321081.
Texte intégralSemenenko, Julija, Aliaksei Kolesau, Vadimas Starikovičius, Artūras Mackūnas et Dmitrij Šešok. « COMPARISON OF GPU AND CPU EFFICIENCY WHILE SOLVING HEAT CONDUCTION PROBLEMS ». Mokslas - Lietuvos ateitis 12 (24 novembre 2020) : 1–5. http://dx.doi.org/10.3846/mla.2020.13500.
Texte intégralThèses sur le sujet "GPU-CPU"
Fang, Zhuowen. « Java GPU vs CPU Hashing Performance ». Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-33994.
Texte intégralDollinger, Jean-François. « A framework for efficient execution on GPU and CPU+GPU systems ». Thesis, Strasbourg, 2015. http://www.theses.fr/2015STRAD019/document.
Texte intégralTechnological limitations faced by the semi-conductor manufacturers in the early 2000's restricted the increase in performance of the sequential computation units. Nowadays, the trend is to increase the number of processor cores per socket and to progressively use the GPU cards for highly parallel computations. Complexity of the recent architectures makes it difficult to statically predict the performance of a program. We describe a reliable and accurate parallel loop nests execution time prediction method on GPUs based on three stages: static code generation, offline profiling, and online prediction. In addition, we present two techniques to fully exploit the computing resources at disposal on a system. The first technique consists in jointly using CPU and GPU for executing a code. In order to achieve higher performance, it is mandatory to consider load balance, in particular by predicting execution time. The runtime uses the profiling results and the scheduler computes the execution times and adjusts the load distributed to the processors. The second technique, puts CPU and GPU in a competition: instances of the considered code are simultaneously executed on CPU and GPU. The winner of the competition notifies its completion to the other instance, implying the termination of the latter
Gjermundsen, Aleksander. « CPU and GPU Co-processing for Sound ». Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11794.
Texte intégralCARLOS, EDUARDO TELLES. « HYBRID FRUSTUM CULLING USING CPU AND GPU ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=31453@1.
Texte intégralUm dos problemas mais antigos da computação gráfica tem sido a determinação de visibilidade. Vários algoritmos têm sido desenvolvidos para viabilizar modelos cada vez maiores e detalhados. Dentre estes algoritmos, destaca-se o frustum culling, cujo papel é remover objetos que não sejam visíveis ao observador. Esse algoritmo, muito comum em várias aplicações, vem sofrendo melhorias ao longo dos anos, a fim de acelerar ainda mais a sua execução. Apesar de ser tratado como um problema bem resolvido na computação gráfica, alguns pontos ainda podem ser aperfeiçoados, e novas formas de descarte desenvolvidas. No que se refere aos modelos massivos, necessita-se de algoritmos de alta performance, pois a quantidade de cálculos aumenta significativamente. Este trabalho objetiva avaliar o algoritmo de frustum culling e suas otimizações, com o propósito de obter o melhor algoritmo possível implementado em CPU, além de analisar a influência de cada uma de suas partes em modelos massivos. Com base nessa análise, novas técnicas de frustum culling serão desenvolvidas, utilizando o poder computacional da GPU (Graphics Processing Unit), e comparadas com o resultado obtido apenas pela CPU. Como resultado, será proposta uma forma de frustum culling híbrido, que tentará aproveitar o melhor da CPU e da GPU.
The definition of visibility is a classical problem in Computer Graphics. Several algorithms have been developed to enable the visualization of huge and complex models. Among these algorithms, the frustum culling, which plays an important role in this area, is used to remove invisible objects by the observer. Besides being very usual in applications, this algorithm has been improved in order to accelerate its execution. Although being treated as a well-solved problem in Computer Graphics, some points can be enhanced yet, and new forms of culling may be disclosed as well. In massive models, for example, algorithms of high performance are required, since the calculus arises considerably. This work analyses the frustum culling algorithm and its optimizations, aiming to obtain the state-of-the-art algorithm implemented in CPU, as well as explains the influence of each of its steps in massive models. Based on this analysis, new GPU (Graphics Processing Unit) based frustum culling techniques will be developed and compared with the ones using only CPU. As a result, a hybrid frustum culling will be proposed, in order to achieve the best of CPU and GPU processing.
Farooqui, Naila. « Runtime specialization for heterogeneous CPU-GPU platforms ». Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54915.
Texte intégralSmith, Michael Shawn. « Performance Analysis of Hybrid CPU/GPU Environments ». PDXScholar, 2010. https://pdxscholar.library.pdx.edu/open_access_etds/300.
Texte intégralWong, Henry Ting-Hei. « Architectures and limits of GPU-CPU heterogeneous systems ». Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2529.
Texte intégralGummadi, Deepthi. « Improving GPU performance by regrouping CPU-memory data ». Thesis, Wichita State University, 2014. http://hdl.handle.net/10057/10959.
Texte intégralThesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
Chen, Wei. « Dynamic Workload Division in GPU-CPU Heterogeneous Systems ». The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1364250106.
Texte intégralBen, Romdhanne Bilel. « Simulation des réseaux à grande échelle sur les architectures de calculs hétérogènes ». Thesis, Paris, ENST, 2013. http://www.theses.fr/2013ENST0088/document.
Texte intégralThe simulation is a primary step on the evaluation process of modern networked systems. The scalability and efficiency of such a tool in view of increasing complexity of the emerging networks is a key to derive valuable results. The discrete event simulation is recognized as the most scalable model that copes with both parallel and distributed architecture. Nevertheless, the recent hardware provides new heterogeneous computing resources that can be exploited in parallel.The main scope of this thesis is to provide a new mechanisms and optimizations that enable efficient and scalable parallel simulation using heterogeneous computing node architecture including multicore CPU and GPU. To address the efficiency, we propose to describe the events that only differs in their data as a single entry to reduce the event management cost. At the run time, the proposed hybrid scheduler will dispatch and inject the events on the most appropriate computing target based on the event descriptor and the current load obtained through a feedback mechanisms such that the hardware usage rate is maximized. Results have shown a significant gain of 100 times compared to traditional CPU based approaches. In order to increase the scalability of the system, we propose a new simulation model, denoted as general purpose coordinator-master-worker, to address jointly the challenge of distributed and parallel simulation at different levels. The performance of a distributed simulation that relies on the GP-CMW architecture tends toward the maximal theoretical efficiency in a homogeneous deployment. The scalability of such a simulation model is validated on the largest European GPU-based supercomputer
Livres sur le sujet "GPU-CPU"
Piccoli, María Fabiana. Computación de alto desempeño en GPU. Editorial de la Universidad Nacional de La Plata (EDULP), 2011. http://dx.doi.org/10.35537/10915/18404.
Texte intégralChapitres de livres sur le sujet "GPU-CPU"
Ou, Zhixin, Juan Chen, Yuyang Sun, Tao Xu, Guodong Jiang, Zhengyuan Tan et Xinxin Qi. « AOA : Adaptive Overclocking Algorithm on CPU-GPU Heterogeneous Platforms ». Dans Algorithms and Architectures for Parallel Processing, 253–72. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-22677-9_14.
Texte intégralStuart, Jeff A., Michael Cox et John D. Owens. « GPU-to-CPU Callbacks ». Dans Euro-Par 2010 Parallel Processing Workshops, 365–72. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21878-1_45.
Texte intégralWille, Mario, Tobias Weinzierl, Gonzalo Brito Gadeschi et Michael Bader. « Efficient GPU Offloading with OpenMP for a Hyperbolic Finite Volume Solver on Dynamically Adaptive Meshes ». Dans Lecture Notes in Computer Science, 65–85. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32041-5_4.
Texte intégralReinders, James, Ben Ashbaugh, James Brodman, Michael Kinsner, John Pennycook et Xinmin Tian. « Programming for GPUs ». Dans Data Parallel C++, 353–85. Berkeley, CA : Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5574-2_15.
Texte intégralShi, Lin, Hao Chen et Ting Li. « Hybrid CPU/GPU Checkpoint for GPU-Based Heterogeneous Systems ». Dans Communications in Computer and Information Science, 470–81. Berlin, Heidelberg : Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53962-6_42.
Texte intégralLi, Jie, George Michelogiannakis, Brandon Cook, Dulanya Cooray et Yong Chen. « Analyzing Resource Utilization in an HPC System : A Case Study of NERSC’s Perlmutter ». Dans Lecture Notes in Computer Science, 297–316. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32041-5_16.
Texte intégralLi, Jianqing, Hongli Li, Jing Li, Jianmin Chen, Kai Liu, Zheng Chen et Li Liu. « Distributed Heterogeneous Parallel Computing Framework Based on Component Flow ». Dans Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 437–45. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_45.
Texte intégralKrol, Dawid, Jason Harris et Dawid Zydek. « Hybrid GPU/CPU Approach to Multiphysics Simulation ». Dans Progress in Systems Engineering, 893–99. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08422-0_130.
Texte intégralSao, Piyush, Richard Vuduc et Xiaoye Sherry Li. « A Distributed CPU-GPU Sparse Direct Solver ». Dans Lecture Notes in Computer Science, 487–98. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09873-9_41.
Texte intégralChen, Lin, Deshi Ye et Guochuan Zhang. « Online Scheduling on a CPU-GPU Cluster ». Dans Lecture Notes in Computer Science, 1–9. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38236-9_1.
Texte intégralActes de conférences sur le sujet "GPU-CPU"
Elis, Bengisu, Olga Pearce, David Boehme, Jason Burmark et Martin Schulz. « Non-Blocking GPU-CPU Notifications to Enable More GPU-CPU Parallelism ». Dans HPCAsia 2024 : International Conference on High Performance Computing in Asia-Pacific Region. New York, NY, USA : ACM, 2024. http://dx.doi.org/10.1145/3635035.3635036.
Texte intégralYang, Yi, Ping Xiang, Mike Mantor et Huiyang Zhou. « CPU-assisted GPGPU on fused CPU-GPU architectures ». Dans 2012 IEEE 18th International Symposium on High Performance Computer Architecture (HPCA). IEEE, 2012. http://dx.doi.org/10.1109/hpca.2012.6168948.
Texte intégralRai, Siddharth, et Mainak Chaudhuri. « Improving CPU Performance Through Dynamic GPU Access Throttling in CPU-GPU Heterogeneous Processors ». Dans 2017 IEEE International Parallel and Distributed Processing Symposium : Workshops (IPDPSW). IEEE, 2017. http://dx.doi.org/10.1109/ipdpsw.2017.37.
Texte intégralChadwick, Jools, Francois Taiani et Jonathan Beecham. « From CPU to GP-GPU ». Dans the 10th International Workshop. New York, New York, USA : ACM Press, 2012. http://dx.doi.org/10.1145/2405136.2405142.
Texte intégralWang, Xin, et Wei Zhang. « A Sample-Based Dynamic CPU and GPU LLC Bypassing Method for Heterogeneous CPU-GPU Architectures ». Dans 2017 IEEE Trustcom/BigDataSE/ICESS. IEEE, 2017. http://dx.doi.org/10.1109/trustcom/bigdatase/icess.2017.309.
Texte intégralK., Raju, Niranjan N. Chiplunkar et Kavoor Rajanikanth. « A CPU-GPU Cooperative Sorting Approach ». Dans 2019 Innovations in Power and Advanced Computing Technologies (i-PACT). IEEE, 2019. http://dx.doi.org/10.1109/i-pact44901.2019.8960106.
Texte intégralXu, Yan, Gary Tan, Xiaosong Li et Xiao Song. « Mesoscopic traffic simulation on CPU/GPU ». Dans the 2nd ACM SIGSIM/PADS conference. New York, New York, USA : ACM Press, 2014. http://dx.doi.org/10.1145/2601381.2601396.
Texte intégralKerr, Andrew, Gregory Diamos et Sudhakar Yalamanchili. « Modeling GPU-CPU workloads and systems ». Dans the 3rd Workshop. New York, New York, USA : ACM Press, 2010. http://dx.doi.org/10.1145/1735688.1735696.
Texte intégralKang, SeungGu, Hong Jun Choi, Cheol Hong Kim, Sung Woo Chung, DongSeop Kwon et Joong Chae Na. « Exploration of CPU/GPU co-execution ». Dans the 2011 ACM Symposium. New York, New York, USA : ACM Press, 2011. http://dx.doi.org/10.1145/2103380.2103388.
Texte intégralAciu, Razvan-Mihai, et Horia Ciocarlie. « Algorithm for Cooperative CPU-GPU Computing ». Dans 2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2013. http://dx.doi.org/10.1109/synasc.2013.53.
Texte intégralRapports d'organisations sur le sujet "GPU-CPU"
Samfass, Philipp. Porting AMG2013 to Heterogeneous CPU+GPU Nodes. Office of Scientific and Technical Information (OSTI), janvier 2017. http://dx.doi.org/10.2172/1343001.
Texte intégralSmith, Michael. Performance Analysis of Hybrid CPU/GPU Environments. Portland State University Library, janvier 2000. http://dx.doi.org/10.15760/etd.300.
Texte intégralRudin, Sven. VASP calculations on Chicoma : CPU vs. GPU. Office of Scientific and Technical Information (OSTI), mars 2023. http://dx.doi.org/10.2172/1962769.
Texte intégralOwens, John. A Programming Framework for Scientific Applications on CPU-GPU Systems. Office of Scientific and Technical Information (OSTI), mars 2013. http://dx.doi.org/10.2172/1069280.
Texte intégralPietarila Graham, Anna, Daniel Holladay, Jonah Miller et Jeffrey Peterson. Spiner-EOSPAC Comparison : performance and accuracy on Power9 CPU and GPU. Office of Scientific and Technical Information (OSTI), mars 2022. http://dx.doi.org/10.2172/1859858.
Texte intégralKurzak, Jakub, Pitior Luszczek, Mathieu Faverge et Jack Dongarra. LU Factorization with Partial Pivoting for a Multi-CPU, Multi-GPU Shared Memory System. Office of Scientific and Technical Information (OSTI), mars 2012. http://dx.doi.org/10.2172/1173291.
Texte intégralSnider, Dale M. DOE SBIR Phase-1 Report on Hybrid CPU-GPU Parallel Development of the Eulerian-Lagrangian Barracuda Multiphase Program. Office of Scientific and Technical Information (OSTI), février 2011. http://dx.doi.org/10.2172/1009440.
Texte intégralAnathan, Sheryas, Alan Williams, James Overfelt, Johnathan Vo, Philip Sakievich, Timothy Smith, Jonathan Hu et al. Demonstration and performance testing of extreme-resolution simulations with static meshes on Summit (CPU & ; GPU) for a parked-turbine con%0Cfiguration and an actuator-line (mid-fidelity model) wind farm con%0Cfiguration. Office of Scientific and Technical Information (OSTI), octobre 2020. http://dx.doi.org/10.2172/1706223.
Texte intégral