Suchergebnisse - "GPU programming in Python"

In-Line Digital Holographic Reconstruction by Using GPU Programming with Python

Autoren: Dai NAKAI Yohsuke TANAKA

Quelle: Advanced Experimental Mechanics. 2022, 7:169

Hands-on GPU programming with Python and CUDA : explore high-performance parallel computing with CUDA / Brian Tuomanen.

Autoren: Tuomanen, Brian. Autor Packt Publishing. Wydawca pbl

Weitere Verfasser: Tuomanen, Brian. Autor Packt Publishing. Wydawca pbl

Quelle: Indeks.

Schlagwörter: CUDA (informatyka), Python (język programowania), Procesory kart graficznych

Zugangs-URL: http://katalog.nukat.edu.pl/lib/item?id=chamo:4602660&theme=nukat

Hands-On GPU Programming with Python and CUDA : Explore High-performance Parallel Computing with CUDA

Autoren: Dr. Brian Tuomanen

Resource Type: eBook.

Schlagworte: Graphics processing units, Python (Computer program language), CUDA (Computer architecture)

Categories: COMPUTERS / Languages / Python, COMPUTERS / Distributed Systems / General, COMPUTERS / Programming / Parallel

Hands-On GPU Programming with Python and CUDA

Autoren: Tuomanen, Dr. Brian, Tuomanen, Dr. Brian

Index Begriffe: book

URL: https://international.scholarvox.com/book/88865405

Efficient Multi-GPU Programming in Python: Reducing Synchronization and Access Overheads

Autoren: Oden, Lena Nölp, Klaus

Quelle: 2025 IEEE International Conference on Cluster Computing (CLUSTER) ; page 1-10

Verfügbarkeit: https://doi.org/10.1109/cluster59342.2025.11186485
http://xplorestaging.ieee.org/ielx8/11186399/11186452/11186485.pdf?arnumber=11186485

Data for our Paper 'Efficient Multi-GPU Programming in Python: Reducing Synchronization and Access Overheads'

Autoren: Oden, Lena

Relation: https://zenodo.org/records/16600717; oai:zenodo.org:16600717; https://doi.org/10.5281/zenodo.16600717

Verfügbarkeit: https://doi.org/10.5281/zenodo.16600717
https://zenodo.org/records/16600717

Parallel and GPU Programming in Python

Autoren: Marco Verdicchio orcid:0000-0001-5286- Carlos Teijeiro Barjas et al.

Schlagwörter: HPC, GPU Programming, Python, Training Material

Relation: https://zenodo.org/communities/prace/; https://zenodo.org/records/7477512; oai:zenodo.org:7477512; https://doi.org/10.5281/zenodo.7477512

Verfügbarkeit: https://doi.org/10.5281/zenodo.7477512
https://zenodo.org/records/7477512

Performance Characterization of Python Runtimes for Multi-device Task Parallel Programming.

Autoren: Ruys, William Lee, Hochan You, Bozhi et al.

Quelle: International Journal of Parallel Programming. Apr2025, Vol. 53 Issue 2, p1-24. 24p.

Hands-On GPU Programming with Python and CUDA

Autoren: Tuomanen, Brian

Schlagwörter: XX

Relation: http://cds.cern.ch/record/2735971; oai:cds.cern.ch:2735971

Verfügbarkeit: http://cds.cern.ch/record/2735971

Evaluating multi-GPU computing capabilities of Numba and CuPy.

Autoren: Askar, Tair Lukac, Martin Shukirgaliyev, Bekdaulet et al.

Quelle: Cluster Computing; 2025, Vol. 28 Issue 11, p1-13, 13p

Schlagwörter: GRAPHICS processing units, HIGH performance computing, PARALLEL programming, SCALABILITY, CUDA (Computer architecture), MONTE Carlo method, MECHANICAL efficiency

Automated Classification and Change Detection of Remote Sensing Images Based on Convolutional Neural Networks.

Autoren: Wu, Nannan Dong, Wenxiao Wu, Xiangjin et al.

Quelle: Journal of Combinatorial Mathematics & Combinatorial Computing; Dec2025, Vol. 127a, p4723-4747, 25p

Schlagwörter: REMOTE-sensing images, ARTIFICIAL neural networks, PREDICTION models, REMOTE sensing, DEEP learning

An Efficient Parallelization of Microscopic Traffic Simulation.

Autoren: Heidary, Benyamin Schweizer, Joerg Nguyen, Ngoc An et al.

Quelle: Applied Sciences (2076-3417); Jul2025, Vol. 15 Issue 13, p6960, 21p

Schlagwörter: GRAPHICS processing units, PARALLEL programming, TRANSPORTATION management, TRAFFIC flow, MATHEMATICAL optimization, COMPUTER performance

Hands-On GPU Computing with Python

Autoren: Bandyopadhyay, Avimanyu, Bandyopadhyay, Avimanyu

Index Begriffe: book

URL: https://international.scholarvox.com/book/88871037

Aplicación basada en SVM y programación paralela para estimar los indicadores de disponibilidad de los servicios de telecomunicaciones en las principales ciudades de Colombia ; Application based on SVM and parallel programming to estimate the availability indicators of telecommunications services in the main cities of Colombia

Autoren: Sánchez Acuña, Carlos Orlando Jiménez Rodríguez, William Camilo Hernández Martínez, Henry Alberto

Weitere Verfasser: Sánchez Acuña, Carlos Orlando Jiménez Rodríguez, William Camilo Hernández Martínez, Henry Alberto

Schlagwörter: Calidad del servicio, Mineria de datos, Programación, Servicios de telecomunicaciones, Maquinas de sectores soporte, Ingeniería en Telecomunicaciones -- Tesis y disertaciones académicas, Telecomunicaciones -- Servicios, Aprendizaje automático (Inteligencia artificial), Programación paralela (Computadores electrónicos), Análisis de datos, Service Quality, Telecommunications services, Data mining, Programming, Support vector machines

Dateibeschreibung: pdf; application/pdf

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Kandrot, CUDA by Example: An Introduction to General-Purpose GPU Programming, Addison-Wesley, 2010; F. Nelli, Python Data Analytics: With Pandas, NumPy, and Matplotlib, Apress, 2018; D. B. Kirk y W. W. Hwu, Programming Massively Parallel Processors: A Hands-on Approach, Morgan Kaufmann, 2016; J. Nickolls, I. Buck, M. Garland, y K. Skadron, "Scalable Parallel Programming with CUDA," Queue, vol. 6, no. 2, pp. 40-53, 2008; A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, O'Reilly Media, 2019; A. Klöckner, "PyCUDA: Even Simpler GPU Programming with Python," in GPU Computing Gems Emerald Edition, Elsevier, 2011, pp. 471-483; A. Grama, A. Gupta, G. Karypis, y V. Kumar, Introduction to Parallel Computing, 2nd ed., Addison-Wesley, 2003; T. H. Lee, The Design of CMOS Radio-Frequency Integrated Circuits, 2nd ed., Cambridge University Press, 2004; T. Hastie, R. Tibshirani, y J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer, 2009; A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd ed., O'Reilly Media, 2019; J. G. Proakis y M. Salehi, Digital Communications, 5th ed., McGraw-Hill, 2008; T. S. Rappaport, Wireless Communications: Principles and Practice, 2nd ed., Prentice Hall, 2002; M. F. Othman, H. S. Abdul Rahman, y N. L. M. Noor, "Capacity Management in Telecommunication Networks: A Systematic Literature Review," Telecommunication Systems, vol. 59, no. 2, pp. 247-263, 2015; K. Walkowiak, Modeling and Optimization of Cloud-Ready and Content-Oriented Networks, Springer, 2016; J. F. Sofokleous, G. P. Evangelou, y P. D. Kakouris, "The Role of ISO/IEC 27001 in Protecting Information Security," Information Security Journal: A Global Perspective, vol. 26, no. 1, pp. 42-55, 2017; Congreso de la República de Colombia, «Ley 1341 de 2009», Diario Oficial No. 47.426, 30 de julio de 2009; Comisión de Regulación de Comunicaciones (CRC), «Resolución 3502 de 2011», Diario Oficial No. 48.253, 22 de diciembre de 2011; Congreso de la República de Colombia, «Ley 1581 de 2012», Diario Oficial No. 48.587, 17 de octubre de 2012; International Organization for Standardization (ISO), «ISO/IEC 27001:2013 - Information technology — Security techniques — Information security management systems — Requirements», ISO, 2013; Congreso de la República de Colombia, «Ley 1978 de 2019», Diario Oficial No. 51.044, 25 de julio de 2019; V. Vapnik, Statistical Learning Theory, New York: Wiley, 199; N. Cristianini y J. Shawe-Taylor, An Introduction to Support Vector Machines and 97 %7C Other Kernel-based Learning Methods, Cambridge: Cambridge University Press, 2000; B. Schölkopf y A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, 2002; L. Bottou, C. Cortes, y V. Vapnik, «Support Vector Networks», Machine Learning, vol. 20, no. 3, pp. 273-297, 1994; S. Shalev-Shwartz y S. Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014; J. Dean y S. Ghemawat, «MapReduce: Simplified Data Processing on Large Clusters», Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008; Z.-H. Zhou, Ensemble Methods: Foundations and Algorithms, CRC Press, 2012; X. Wu, V. Kumar, J. Ross Quinlan, et al., «Top 10 algorithms in data mining», Knowledge and Information Systems, vol. 14, no. 1, pp. 1-37, 2008; T. Hastie, R. Tibshirani, y J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009; C.-J. Lin y S. Wang, «Large-Scale Linear Support Vector Machines», Journal of Machine Learning Research, vol. 9, pp. 207-244, 2001; D. A. Reed y J. Dongarra, «Exascale Computing and Big Data», Communications of the ACM, vol. 58, no. 7, pp. 56-68, 2015; J. Brownlee, Master Machine Learning Algorithms, Machine Learning Mastery, 2016; R. Ghanea-Hercock, Applied Artificial Intelligence: A Handbook for Business Leaders, Palgrave Macmillan, 2014; S. B. Kotsiantis, «Supervised Machine Learning: A Review of Classification Techniques», Informatica, vol. 31, pp. 249-268, 2007; L. González y A. Gómez, «Implementación de un sistema de monitoreo para redes de telecomunicaciones usando SVM», Universidad Nacional de Colombia, 2017; J. Martínez y P. Rodríguez, «Optimización de redes de telecomunicaciones mediante técnicas de aprendizaje automático», Pontificia Universidad Javeriana, 2019; C. Ardila y M. López, «Programación paralela en el análisis de big data para telecomunicaciones en Colombia», Universidad de los Andes, 2020; S. Fernández y H. Ramírez, «Predicción de disponibilidad en redes 4G utilizando SVM», Universidad del Norte, 2021; M. Torres y E. Guerrero, «Modelos predictivos para la disponibilidad de servicios de telecomunicaciones en Bogotá», Universidad de Antioquia, 2018; D. Gutiérrez y R. Pérez, «Aplicación de técnicas de machine learning en la optimización de servicios de telecomunicaciones en Medellín», Universidad EAFIT, 2020; J. Suárez y A. Hernández, «Análisis de la disponibilidad de redes de telecomunicaciones en zonas rurales de Colombia», Universidad del Rosario, 2019; P. Cárdenas y V. Mendoza, «Evaluación del rendimiento de redes 5G en Colombia mediante técnicas de machine learning», Universidad del Valle, 2021; J. López y F. Torres, «Despliegue de infraestructura de telecomunicaciones utilizando programación paralela», Universidad de la Sabana, 2022; A. Rodríguez y L. Martínez, «Análisis de datos de telecomunicaciones en Colombia mediante SVM», Universidad Industrial de Santander, 2018; https://hdl.handle.net/11349/93851

Verfügbarkeit: https://hdl.handle.net/11349/93851

Experimental Analysis of Microparticle Elongation in the Optical Axis in Holographic Particle Measurement.

Autoren: Yohsuke TANAKA Hiroki MATSUSHI Dai NAKAI

Quelle: Advanced Experimental Mechanics; 2024, Vol. 9, p48-52, 5p

Parallel and High Performance Programming with Python

Autoren: Fabio Nelli

Resource Type: eBook.

Schlagworte: Language and languages, Parallelism (Linguistics), Python (Computer program language), Electronic data processing--Distributed processing, Cloud computing, Computer programming, Computers

Categories: COMPUTERS / Programming / Parallel

Using the Graphics Processing Unit to Evaluate American-Style Derivatives.

Autoren: Leon Xing Li Ren-Raw Chen

Quelle: Journal of Financial Data Science; Summer2023, Vol. 5 Issue 3, p88-106, 19p

Schlagwörter: GRAPHICS processing units, MONTE Carlo method, DERIVATIVE securities, PRICING, FINANCIAL risk management

Kemeny ranking aggregation meets the GPU.

Autoren: Rico, Noelia Alonso, Pedro Díaz, Irene

Quelle: Journal of Supercomputing; Jun2023, Vol. 79 Issue 9, p10335-10352, 18p

Schlagwörter: NUMBER systems, SOCIAL choice, NUMERALS

Spatial Frequency Analysis of Holograms of Two Droplets in Close Proximity.

Autoren: Dai NAKAI Yohsuke TANAKA

Quelle: Advanced Experimental Mechanics; 2023, Vol. 8, p1-6, 6p

Hands-On GPU Computing with Python : Explore the Capabilities of GPUs for Solving High Performance Computational Problems

Autoren: Avimanyu Bandyopadhyay

Resource Type: eBook.

Schlagworte: Python (Computer program language), Graphics processing units--Programming

Categories: COMPUTERS / Design, Graphics & Media / Graphics Tools, COMPUTERS / Programming / Parallel, COMPUTERS / Software Development & Engineering / Systems Analysis & Design