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. Szafaryn, Lukasz G.; Gamblin, Todd; de Supinski, Bronis R.; Skadron, Kevin: Trellis: portability across architectures with a high-level framework (2013) ioport
  2. Walsh, Stuart D. C.; Saar, Martin O.: Developing extensible lattice-Boltzmann simulators for general-purpose graphics-processing units (2013)
  3. Alabi, Tolu; Blanchard, Jeffrey D.; Gordon, Bradley; Steinbach, Russel: Fast (k)-selection algorithms for graphics processing units (2012)
  4. Alexandru, A.; Pelissier, C.; Gamari, B.; Lee, F. X.: Multi-mass solvers for lattice QCD on GPUs (2012)
  5. Astorino, M.; Becerra-Sagredo, J.; Quarteroni, A.: A modular lattice Boltzmann solver for GPU computing (2012)
  6. Beliakov, G.; Johnstone, M.; Nahavandi, S.: Computing of high breakdown regression estimators without sorting on graphics processing units (2012)
  7. Beliakov, Gleb; Kelarev, Andrei; Yearwood, John: Derivative-free optimization and neural networks for robust regression (2012)
  8. Bergstrom, Lars; Reppy, John: Nested data-parallelism on the GPU (2012)
  9. Corrigan, Andrew; Camelli, Fernando; Löhner, Rainald; Mut, Fernando: Semi-automatic porting of a large-scale Fortran CFD code to GPUs (2012)
  10. Engsig-Karup, A. P.; Madsen, Morten G.; Glimberg, Stefan L.: A massively parallel GPU-accelerated model for analysis of fully nonlinear free surface waves (2012)
  11. Negrut, Dan; Tasora, Alessandro; Mazhar, Hammad; Heyn, Toby; Hahn, Philipp: Leveraging parallel computing in multibody dynamics (2012)
  12. Pazouki, A.; Mazhar, H.; Negrut, D.: Parallel collision detection of ellipsoids with applications in large scale multibody dynamics (2012)
  13. Mazhar, Hammad; Heyn, Toby; Negrut, Dan: A scalable parallel method for large collision detection problems (2011)
  14. Novaković, Vedran; Singer, Sanja: A GPU-based hyperbolic SVD algorithm (2011)
  15. 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)