CUDA.jl

CUDA programming in Julia: The CUDA.jl package is the main entrypoint for for programming NVIDIA GPUs using CUDA. The package makes it possible to do so at various abstraction levels, from easy-to-use arrays down to hand-written kernels using low-level CUDA APIs.

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References in zbMATH (referenced in 8 articles )

Showing results 1 to 8 of 8.
Sorted by year (citations)

  1. Bou-Rabee, Ahmed: Dynamic dimensional reduction in the abelian sandpile (2022)
  2. Faugeras, Olivier D.; Song, Anna; Veltz, Romain: Spatial and color hallucinations in a mathematical model of primary visual cortex (2022)
  3. Tangi Migot; Dominique Orban; Abel Soares Siqueira: DCISolver.jl: A Julia Solver for Nonlinear Optimization using Dynamic Control of Infeasibility (2022) not zbMATH
  4. Xie, Jiaxi; Ehmann, Kornel; Cao, Jian: MetaFEM: a generic FEM solver by meta-expressions (2022)
  5. Gozzini, Francesco: A high-performance code for EPRL spin foam amplitudes (2021)
  6. Gao, Kaifeng; Mei, Gang; Piccialli, Francesco; Cuomo, Salvatore; Tu, Jingzhi; Huo, Zenan: Julia language in machine learning: algorithms, applications, and open issues (2020)
  7. Miles Lucas; Michael Bottom: ADI.jl: A Julia Package for High-Contrast Imaging (2020) not zbMATH
  8. Seyoon Ko, Hua Zhou, Jin Zhou, Joong-Ho Won: DistStat.jl: Towards Unified Programming for High-Performance Statistical Computing Environments in Julia (2020) arXiv