Thrust is a C++ template library for CUDA based on the Standard Template Library (STL). Thrust allows you to implement high performance parallel applications with minimal programming effort through a high-level interface that is fully interoperable with CUDA C. Thrust provides a rich collection of data parallel primitives such as scan, sort, and reduce, which can be composed together to implement complex algorithms with concise, readable source code. By describing your computation in terms of these high-level abstractions you provide Thrust with the freedom to select the most efficient implementation automatically. As a result, Thrust can be utilized in rapid prototyping of CUDA applications, where programmer productivity matters most, as well as in production, where robustness and absolute performance are crucial.

References in zbMATH (referenced in 54 articles )

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  1. Yang, Carl; Buluç, Aydın; Owens, John D.: GraphBLAST: a high-performance linear algebra-based graph framework on the GPU (2022)
  2. Dong, Zhaonan; Georgoulis, Emmanuil H.; Kappas, Thomas: GPU-accelerated discontinuous Galerkin methods on polytopic meshes (2021)
  3. Auer, Ekaterina; Rauh, Andreas; Kersten, Julia: Experiments-based parameter identification on the GPU for cooperative systems (2020)
  4. Berenger Bramas: TBFMM: A C++ generic and parallel fast multipole method library (2020) not zbMATH
  5. Long, Qinyuan; Mei, Gang: GScan: a parallel Graham scan algorithm for calculating two-dimensional convex hulls on graphic processing units (2020)
  6. Reguly, István Z.; Mudalige, Gihan R.: Productivity, performance, and portability for computational fluid dynamics applications (2020)
  7. Mozaffar, Mojtaba; Ndip-Agbor, Ebot; Lin, Stephen; Wagner, Gregory J.; Ehmann, Kornel; Cao, Jian: Acceleration strategies for explicit finite element analysis of metal powder-based additive manufacturing processes using graphical processing units (2019)
  8. Tim Besard, Valentin Churavy, Alan Edelman, Bjorn De Sutter: Rapid software prototyping for heterogeneous and distributed platforms (2019) not zbMATH
  9. Zaspel, Peter: Algorithmic patterns for (\mathcalH)-matrices on many-core processors (2019)
  10. Alan Hylton, Gregory Henselman-Petrusek, Janche Sang, Robert Short: Tuning the Performance of a Computational Persistent Homology Package (2018) arXiv
  11. Gremse, Felix; Küpper, Kerstin; Naumann, Uwe: Memory-efficient sparse matrix-matrix multiplication by row merging on many-core architectures (2018)
  12. Kikinzon, Evgeny; Shashkov, Mikhail; Garimella, Rao: Establishing mesh topology in multi-material cells: enabling technology for robust and accurate multi-material simulations (2018)
  13. Sweezy, Jeremy E.: A Monte Carlo volumetric-ray-casting estimator for global fluence tallies on GPUs (2018)
  14. Zanella, R.; Porta, F.; Ruggiero, V.; Zanetti, M.: Serial and parallel approaches for image segmentation by numerical minimization of a second-order functional (2018)
  15. Aissa, Mohamed; Verstraete, Tom; Vuik, Cornelis: Toward a GPU-aware comparison of explicit and implicit CFD simulations on structured meshes (2017)
  16. Jambunathan, Revathi; Levin, Deborah A.: Advanced parallelization strategies using hybrid MPI-CUDA octree DSMC method for modeling flow through porous media (2017)
  17. Li, Ang; Serban, Radu; Negrut, Dan: Analysis of a splitting approach for the parallel solution of linear systems on GPU cards (2017)
  18. Peter Wittek and Shi Gao and Ik Lim and Li Zhao: somoclu: An Efficient Parallel Library for Self-Organizing Maps (2017) not zbMATH
  19. Cuvelier, François; Japhet, Caroline; Scarella, Gilles: An efficient way to assemble finite element matrices in vector languages (2016)
  20. Rupp, Karl; Tillet, Philippe; Rudolf, Florian; Weinbub, Josef; Morhammer, Andreas; Grasser, Tibor; Jüngel, Ansgar; Selberherr, Siegfried: ViennaCL-linear algebra library for multi- and many-core architectures (2016)

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