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 20 articles )

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  1. Cuvelier, François; Japhet, Caroline; Scarella, Gilles: An efficient way to assemble finite element matrices in vector languages (2016)
  2. 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)
  3. Ahnert, Karsten; Demidov, Denis; Mulansky, Mario: Solving ordinary differential equations on GPUs (2014)
  4. Einkemmer, L.; Wiesenberger, M.: A conservative discontinuous Galerkin scheme for the 2D incompressible Navier-Stokes equations (2014)
  5. 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)
  6. Blanchard, Jeffrey D.; Tanner, Jared: GPU accelerated greedy algorithms for compressed sensing (2013)
  7. Ferreiro, A.M.; García, J.A.; López-Salas, J.G.; Vázquez, C.: An efficient implementation of parallel simulated annealing algorithm in GPUs (2013)
  8. Glimberg, S.L.; Engsig-Karup, A.P.; Madsen, M.G.: A fast GPU-accelerated mixed-precision strategy for fully nonlinear water wave computations (2013)
  9. Knepley, Matthew G.; Terrel, Andy R.: Finite element integration on GPGPUs (2013)
  10. Szafaryn, Lukasz G.; Gamblin, Todd; de Supinski, Bronis R.; Skadron, Kevin: Trellis: portability across architectures with a high-level framework (2013)
  11. Alabi, Tolu; Blanchard, Jeffrey D.; Gordon, Bradley; Steinbach, Russel: Fast $k$-selection algorithms for graphics processing units (2012)
  12. Alexandru, A.; Pelissier, C.; Gamari, B.; Lee, F.X.: Multi-mass solvers for lattice QCD on GPUs (2012)
  13. Astorino, M.; Becerra-Sagredo, J.; Quarteroni, A.: A modular lattice Boltzmann solver for GPU computing (2012)
  14. Beliakov, G.; Johnstone, M.; Nahavandi, S.: Computing of high breakdown regression estimators without sorting on graphics processing units (2012)
  15. Bergstrom, Lars; Reppy, John: Nested data-parallelism on the GPU (2012)
  16. Corrigan, Andrew; Camelli, Fernando; Löhner, Rainald; Mut, Fernando: Semi-automatic porting of a large-scale Fortran CFD code to GPUs (2012)
  17. Negrut, Dan; Tasora, Alessandro; Mazhar, Hammad; Heyn, Toby; Hahn, Philipp: Leveraging parallel computing in multibody dynamics (2012)
  18. Mazhar, Hammad; Heyn, Toby; Negrut, Dan: A scalable parallel method for large collision detection problems (2011)
  19. Novaković, Vedran; Singer, Sanja: A GPU-based hyperbolic SVD algorithm (2011)
  20. Edgar, R.G.; Clark, M.A.; Dale, K.; Mitchell, D.A.; Ord, S.M.; Wayth, R.B.; Pfister, H.; Greenhill, L.J.: Enabling a high throughput real time data pipeline for a large radio telescope array with GPUs (2010)