CUDA

The NVIDIA® CUDA® Toolkit provides a comprehensive development environment for C and C++ developers building GPU-accelerated applications. The CUDA Toolkit includes a compiler for NVIDIA GPUs, math libraries, and tools for debugging and optimizing the performance of your applications. You’ll also find programming guides, user manuals, API reference, and other documentation to help you get started quickly accelerating your application with GPUs.


References in zbMATH (referenced in 1185 articles , 2 standard articles )

Showing results 1 to 20 of 1185.
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  1. Algosaibi, Abdulelah A.: High-performance computing based approach for improving semantic-based federated data processing (2021)
  2. Costa, Pedro; Phillips, Everett; Brandt, Luca; Fatica, Massimiliano: GPU acceleration of \textitCaNSfor massively-parallel direct numerical simulations of canonical fluid flows (2021)
  3. Koga, Kazuki: Signal processing approach to mesh refinement in simulations of axisymmetric droplet dynamics (2021)
  4. Xu, Nengxiong; Mei, Gang; Qin, Jiayu; Li, Yazhe; Xu, Liangliang: GeoMFree (^\operatorname3D): a package of meshfree local radial point interpolation method (RPIM) for geomechanics (2021)
  5. Younes Nejahi, Mohammad Soroush Barhaghi, Gregory Schwing, Loren Schwiebert, Jeffrey Potoff: Update 2.70 to GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids (2021) not zbMATH
  6. Afzal, Asif; Ansari, Zahid; Ramis, M. K.: Parallel performance analysis of coupled heat and fluid flow in parallel plate channel using CUDA (2020)
  7. Akimova, Elena N.; Martyshko, Petr S.; Misilov, Vladimir E.; Miftakhov, Valeriy O.: Cost-efficient numerical algorithm for solving the linear inverse problem of finding a variable magnetization (2020)
  8. Avramidis, Eleftherios; Lalik, Marta; Akman, Ozgur E.: SODECL: an open-source library for calculating multiple orbits of a system of stochastic differential equations in parallel (2020)
  9. Blanchard, Pierre; Higham, Nicholas J.; Lopez, Florent; Mary, Theo; Pranesh, Srikara: Mixed precision block fused multiply-add: error analysis and application to GPU tensor cores (2020)
  10. Brehler, Marius; Schirwon, Malte; Krummrich, Peter M.; Göddeke, Dominik: Simulation of nonlinear signal propagation in multimode fibers on multi-GPU systems (2020)
  11. Carcenac, Manuel; Redif, Soydan: Application of the sequential matrix diagonalization algorithm to high-dimensional functional MRI data (2020)
  12. Carreño, José Juan; Martínez, José Antonio; Puertas, María Luz: A general lower bound for the domination number of cylindrical graphs (2020)
  13. Cosco, F.; Greco, F.; Desmet, W.; Mundo, D.: GPU accelerated initialization of local maximum-entropy meshfree methods for vibrational and acoustic problems (2020)
  14. Daniel Jünger; Robin Kobus; André Müller; Christian Hundt, Kai Xu; Weiguo Liu; Bertil Schmidt: WarpCore: A Library for fast Hash Tables on GPUs (2020) arXiv
  15. Dorodnitsyn, V. A.; Kaptsov, E. I.: Shallow water equations in Lagrangian coordinates: symmetries, conservation laws and its preservation in difference models (2020)
  16. Guo, Jian; Liao, Guohong; Liu, Guozhen; Liu, Meicheng; Qiao, Kexin; Song, Ling: Practical collision attacks against round-reduced SHA-3 (2020)
  17. Huang, Jianyu; Yu, Chenhan D.; Geijn, Robert A. van de: Strassen’s algorithm reloaded on GPUs (2020)
  18. Ji, Zhe; Fu, Lin; Hu, Xiangyu; Adams, Nikolaus: A consistent parallel isotropic unstructured mesh generation method based on multi-phase SPH (2020)
  19. Khrapov, S. S.; Khoperskov, A. V.: Application of graphics processing units for self-consistent modelling of shallow water dynamics and sediment transport (2020)
  20. Kiran, Utpal; Gautam, Sachin Singh; Sharma, Deepak: GPU-based matrix-free finite element solver exploiting symmetry of elemental matrices (2020)

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