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 55 articles )

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  1. 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)
  2. Svensson, Bo Joel; Newton, Ryan R.; Sheeran, Mary: A language for hierarchical data parallel design-space exploration on GPUs (2016)
  3. Dalton, Steven; Olson, Luke; Bell, Nathan: Optimizing sparse matrix-matrix multiplication for the GPU (2015)
  4. Gorlatch, Sergei; Steuwer, Michel: Towards high-level programming for systems with many cores (2015) ioport
  5. Nicholas P. Bailey, Trond S. Ingebrigtsen, Jesper Schmidt Hansen, Arno A. Veldhorst, Lasse Bohling, Claire A. Lemarchand, Andreas E. Olsen, Andreas K. Bacher, Lorenzo Costigliola, Ulf R. Pedersen, Heine Larsen, Jeppe C. Dyre, Thomas B. Schroder: RUMD: A general purpose molecular dynamics package optimized to utilize GPU hardware down to a few thousand particles (2015) arXiv
  6. Pazouki, Arman; Negrut, Dan: A numerical study of the effect of particle properties on the radial distribution of suspensions in pipe flow (2015)
  7. Roberto Casarin; Stefano Grassi; Francesco Ravazzolo; Herman van Dijk: Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox (2015) not zbMATH
  8. Serban, Radu; Melanz, Daniel; Li, Ang; Stanciulescu, Ilinca; Jayakumar, Paramsothy; Negrut, Dan: A GPU-based preconditioned Newton-Krylov solver for flexible multibody dynamics (2015)
  9. Wong, J.; Kuhl, E.; Darve, E.: A new sparse matrix vector multiplication graphics processing unit algorithm designed for finite element problems (2015)
  10. Ahnert, Karsten; Demidov, Denis; Mulansky, Mario: Solving ordinary differential equations on GPUs (2014)
  11. Einkemmer, L.; Wiesenberger, M.: A conservative discontinuous Galerkin scheme for the 2D incompressible Navier-Stokes equations (2014)
  12. Gandham, Rajesh; Esler, Kenneth; Zhang, Yongpeng: A GPU accelerated aggregation algebraic multigrid method (2014)
  13. Goldsworthy, M. J.: A GPU-CUDA based direct simulation Monte Carlo algorithm for real gas flows (2014)
  14. King, James; Yakovlev, Sergey; Fu, Zhisong; Kirby, Robert M.; Sherwin, Spencer J.: Exploiting batch processing on streaming architectures to solve 2D elliptic finite element problems: a hybridized discontinuous Galerkin (HDG) case study (2014)
  15. Ying, Xiang; Xin, Shi-Qing; He, Ying: Parallel Chen-Han (PCH) algorithm for discrete geodesics (2014)
  16. Blanchard, Jeffrey D.; Tanner, Jared: GPU accelerated greedy algorithms for compressed sensing (2013)
  17. Ferreiro, A. M.; García, J. A.; López-Salas, J. G.; Vázquez, C.: An efficient implementation of parallel simulated annealing algorithm in GPUs (2013)
  18. Glimberg, S. L.; Engsig-Karup, A. P.; Madsen, M. G.: A fast GPU-accelerated mixed-precision strategy for fully nonlinear water wave computations (2013)
  19. Knepley, Matthew G.; Terrel, Andy R.: Finite element integration on GPGPUs (2013)
  20. Lobachev, Oleg; Guthe, Michael; Loogen, Rita: Estimating parallel performance (2013) ioport