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 1148 articles , 2 standard articles )

Showing results 1 to 20 of 1148.
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  1. Afzal, Asif; Ansari, Zahid; Ramis, M. K.: Parallel performance analysis of coupled heat and fluid flow in parallel plate channel using CUDA (2020)
  2. 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)
  3. Carcenac, Manuel; Redif, Soydan: Application of the sequential matrix diagonalization algorithm to high-dimensional functional MRI data (2020)
  4. Carreño, José Juan; Martínez, José Antonio; Puertas, María Luz: A general lower bound for the domination number of cylindrical graphs (2020)
  5. Cosco, F.; Greco, F.; Desmet, W.; Mundo, D.: GPU accelerated initialization of local maximum-entropy meshfree methods for vibrational and acoustic problems (2020)
  6. Guo, Jian; Liao, Guohong; Liu, Guozhen; Liu, Meicheng; Qiao, Kexin; Song, Ling: Practical collision attacks against round-reduced SHA-3 (2020)
  7. Ji, Zhe; Fu, Lin; Hu, Xiangyu; Adams, Nikolaus: A consistent parallel isotropic unstructured mesh generation method based on multi-phase SPH (2020)
  8. Martiradonna, Angela; Colonna, Gianpiero; Diele, Fasma: \textitGeCo: geometric conservative nonstandard schemes for biochemical systems (2020)
  9. Mena, Hermann; Pfurtscheller, Lena-Maria; Stillfjord, Tony: GPU acceleration of splitting schemes applied to differential matrix equations (2020)
  10. Morozov, A. Yu.; Zhuravlev, A. A.; Reviznikov, D. L.: Analysis and optimization of an adaptive interpolation algorithm for the numerical solution of a system of ordinary differential equations with interval parameters (2020)
  11. Nogueira, Bruno; Pinheiro, Rian G. S.: A GPU based local search algorithm for the unweighted and weighted maximum (s)-plex problems (2020)
  12. Otero, B.; Rojas, O.; Moya, F.; Castillo, J. E.: Alternating direction implicit time integrations for finite difference acoustic wave propagation: parallelization and convergence (2020)
  13. Peter J. Christopher, Andrew Kadis, George S. D. Gordon, Timothy D. Wilkinson: HoloGen: An open source toolbox for high-speed hologram generation (2020) arXiv
  14. Reguly, István Z.; Mudalige, Gihan R.: Productivity, performance, and portability for computational fluid dynamics applications (2020)
  15. Sashikumaar Ganesan, Manan Shah: SParSH-AMG: A library for hybrid CPU-GPU algebraic multigrid and preconditioned iterative methods (2020) arXiv
  16. Schryen, Guido: Parallel computational optimization in operations research: a new integrative framework, literature review and research directions (2020)
  17. Žukovič, Milan; Borovský, Michal; Lach, Matúš; Hristopulos, Dionissios T.: GPU-accelerated simulation of massive spatial data based on the modified planar rotator model (2020)
  18. Acer, Seher; Kayaaslan, Enver; Aykanat, Cevdet: A hypergraph partitioning model for profile minimization (2019)
  19. Alpak, F. O.; Zacharoudiou, I.; Berg, S.; Dietderich, J.; Saxena, N.: Direct simulation of pore-scale two-phase visco-capillary flow on large digital rock images using a phase-field lattice Boltzmann method on general-purpose graphics processing units (2019)
  20. Bernaschi, Massimo; Carrozzo, Mauro; Franceschini, Andrea; Janna, Carlo: A dynamic pattern factored sparse approximate inverse preconditioner on graphics processing units (2019)

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