Suchergebnisse - GPU programming in Python*

Practical GPU programming : high-performance computing with CUDA, CuPy, and Python on modern GPUs / Maris Fenlor.

Autoren: Fenlor, Maris. Autor GitforGits. Wydawca pbl

Weitere Verfasser: Fenlor, Maris. Autor GitforGits. Wydawca pbl

Quelle: Indeks.

Schlagwörter: Programowanie równoległe (informatyka) -- podręczniki, Python (język programowania), CUDA (informatyka), CUDA (informatyka), Python (język programowania), Procesory kart graficznych, Procesor graficzny (GPU), Programowanie (informatyka)

Zugangs-URL: http://katalog.nukat.edu.pl/lib/item?id=chamo:5879597&theme=nukat

Comparative Overview of HPC Frameworks for CPU/GPU Programming

Autoren: Smirnov, Ivan Smirnova, Maryana Veselov, Vladislav et al.

Schlagwörter: SYCL, HIP, OpenCL, programming models, Fortran, High Performance Computing, GPU, Functional Portability, OpenMP, ALPAKA, Kokkos, OpenACC, TOP500 List, HPC, Performance Portability, RAJA, cuda, MPI, CPU, C++, Python

Mojo: MLIR-Based Performance-Portable HPC Science Kernels on GPUs for the Python Ecosystem

Autoren: Godoy, William F. Melnichenko, Tatiana Valero-Lara, Pedro et al.

Schlagwörter: Computational Engineering, Finance, and Science (cs.CE), FOS: Computer and information sciences, Emerging Technologies (cs.ET), Computational Engineering, Finance, and Science, Programming Languages, Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC), Emerging Technologies, Programming Languages (cs.PL)

Zugangs-URL: http://arxiv.org/abs/2509.21039

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.

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

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 : 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

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

Index Begriffe: book

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

Hybrid GPU Programming Education with Python and C++: Preferences, Performance and Common Python Pitfalls

Autoren: Oden, Lena

Quelle: Proceedings of the SC '25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis ; page 359-366

Verfügbarkeit: https://doi.org/10.1145/3731599.3767379
https://dl.acm.org/doi/pdf/10.1145/3731599.3767379

Programming Heterogeneous Hardware via Managed Runtime Systems

Autoren: Fumero Alfonso, Juan Stratikopoulos, Athanasios Kotselidis, Christos-Efthymios

Quelle: SpringerBriefs in Computer Science ISBN: 9783031495588
Fumero Alfonso, J, Stratikopoulos, A & Kotselidis, C-E 2024, Programming Heterogeneous Hardware via Managed Runtime Systems. SpringerBriefs in Computer Science, Springer Nature.

Schlagwörter: FPGA Programming, Programming Languages, GPU Programming, Computer Hardware Acceleration, Java, Managed Runtime Systems, Python

Zugangs-URL: https://research.manchester.ac.uk/en/publications/3989a55d-6005-4523-ac7c-43a753a39f01
https://link.springer.com/book/10.1007/978-3-031-49559-5
https://research.manchester.ac.uk/en/publications/3989a55d-6005-4523-ac7c-43a753a39f01

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

Developing Graphics Frameworks with Python and OpenGL

Autoren: Lee Stemkoski Michael Pascale

Resource Type: eBook.

Schlagworte: Python (Computer program language), Computer graphics--Computer programs, Computer graphics--Mathematics

Categories: COMPUTERS / Software Development & Engineering / Computer Graphics, COMPUTERS / Programming / Games, COMPUTERS / Computer Science, COMPUTERS / Programming / General, COMPUTERS / Languages / Python, COMPUTERS / Programming / Open Source

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

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

PyFWI: A Python package for full-waveform inversion and reservoir monitoring

Autoren: Amir Mardan Bernard Giroux Gabriel Fabien-Ouellet

Quelle: SoftwareX, Vol 22, Iss , Pp 101384- (2023)

Schlagwörter: Full-waveform inversion, Reservoir monitoring, Time-lapse seismic, Python, GPU programming, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2352711023000808; https://doaj.org/toc/2352-7110

Zugangs-URL: https://doaj.org/article/dd9e589f763d41a68474f3691a28637a

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

Accelerating Computation of a Reduced Order Model of a Structural System Resulting from Craig–Bampton Reduction Using GPU Programming

Autoren: Gorecki, Piotr Kalinowski, Miłosz Jeziorek, Łukasz et al.

Convolution hierarchical deep-learning neural network (C-HiDeNN) with graphics processing unit (GPU) acceleration.

Autoren: Park, Chanwook Lu, Ye Saha, Sourav et al.

Quelle: Computational Mechanics; Aug2023, Vol. 72 Issue 2, p383-409, 27p

Schlagwörter: GRAPHICS processing units, FINITE element method, PYTHON programming language, DEGREES of freedom, MANUFACTURING processes

gpuRIR: A python library for room impulse response simulation with GPU acceleration.

Autoren: Diaz-Guerra, David Miguel, Antonio Beltran, Jose R.

Quelle: Multimedia Tools & Applications; 2021, Vol. 80 Issue 4, p5653-5671, 19p

Schlagwörter: IMPULSE response, ARCHITECTURAL acoustics, REVERBERATION time, PYTHON programming language, MULTIMEDIA systems, SIGNAL processing, AUGMENTED reality

X-ray simulations with gVXR in education, digital twining, experiment planning, and data analysis

Autoren: Vidal, Franck P. Afshari, Shaghayegh Ahmed, Sharif et al.

Quelle: Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms. 568

Schlagwörter: Machine learning, Digital twinning, X-ray imaging, Simulation, GPU programming, Computed tomography, Registration

Dateibeschreibung: electronic

Zugangs-URL: https://research.chalmers.se/publication/549191
https://research.chalmers.se/publication/549191/file/549191_Fulltext.pdf