General-purpose computing on graphics processing units: Difference between revisions

Revision as of 22:48, 18 March 2025

Revision as of 16:26, 27 April 2025

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[[ROCm]], launched in 2016, is AMD's open-source response to CUDA. It is, as of 2022, on par with CUDA with regards to features,{{source?|date=December 2024}} and still lacking in consumer support.{{source?|date=December 2024}}

OpenVIDIA was developed at [[University of Toronto]] between 2003–2005,<ref name="Fung">{{cite journalbook | last1 = Fung | first1 = James | last2 = Mann | first2 = Steve | author-link2 = Steve Mann (inventor) | last3 = Aimone | first3 = Chris | titlechapter = OpenVIDIA: Parallel GPU Computercomputer Visionvision | title = Proceedings of the 13th annual ACM international conference on Multimedia | publication-date = 6 November 2005 | date = 6–11 November 2005 | isbn = 1595930442 | publisher = [[Association for Computing Machinery]] | ___location = Singapore | journal = Proceedings of the 13th Annual ACM International Conference on Multimedia | doi = 10.1145/1101149.1101334 | pages = 849-852849–852 | accessdate = 18 March 2025 | chapter-url = http://www.eyetap.org/papers/docs/oss1-fung.pdf | archive-url = https://web.archive.org/web/20191223164955/http://www.eyetap.org/papers/docs/oss1-fung.pdf | archive-date = 23 December 2019}}</ref> in collaboration with Nvidia.

Altimesh Hybridizer created by [[Altimesh]] compiles [[Common Intermediate Language]] to CUDA binaries.<ref>{{cite web|title=Hybridizer|url=http://www.altimesh.com/hybridizer-essentials/|website=Hybridizer|url-status=live|archive-url=https://web.archive.org/web/20171017150337/http://www.altimesh.com/hybridizer-essentials/|archive-date=17 October 2017|df=dmy-all}}</ref><ref>{{cite web|title=Home page|url=http://www.altimesh.com/|website=Altimesh|url-status=live|archive-url=https://web.archive.org/web/20171017145518/http://www.altimesh.com/|archive-date=17 October 2017|df=dmy-all}}</ref> It supports generics and virtual functions.<ref>{{cite web|title=Hybridizer generics and inheritance|url=http://www.altimesh.com/generics-and-inheritance/|url-status=live|archive-url=https://web.archive.org/web/20171017145927/http://www.altimesh.com/generics-and-inheritance/|archive-date=17 October 2017|df=dmy-all|date=2017-07-27}}</ref> Debugging and profiling is integrated with [[Visual Studio]] and Nsight.<ref>{{cite web|title=Debugging and Profiling with Hybridizer|url=http://www.altimesh.com/debugging-and-profiling/|url-status=live|archive-url=https://web.archive.org/web/20171017201449/http://www.altimesh.com/debugging-and-profiling/|archive-date=17 October 2017|df=dmy-all|date=2017-06-05}}</ref> It is available as a Visual Studio extension on Visual Studio Marketplace.

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Due to a trend of increasing power of mobile GPUs, general-purpose programming became available also on the mobile devices running major [[mobile operating system]]s.

[[Google]] [[Android (operating system)|Android]] 4.2 enabled running [[RenderScript]] code on the mobile device GPU.<ref>{{cite web|url=http://developer.android.com/about/versions/android-4.2.html|title=Android 4.2 APIs - Android Developers|website=developer.android.com|url-status=live|archive-url=https://web.archive.org/web/20130826191621/http://developer.android.com/about/versions/android-4.2.html|archive-date=26 August 2013|df=dmy-all}}</ref> Renderscript has since been deprecated in favour of first OpenGL compute shaders<ref>{{cite web | url=https://developer.android.com/guide/topics/renderscript/migrate/migrate-gles | title=Migrate scripts to OpenGL ES 3.1 }}</ref> and later Vulkan Compute.<ref>{{cite web | url=https://developer.android.com/guide/topics/renderscript/migrate/migrate-vulkan | title=Migrate scripts to Vulkan }}</ref> OpenCL is available on many Android devices, but is not officially supported by Android.<ref>{{cite web|url=https://khronos.org/blog/catching-up-with-khronos-experts-qa-on-opencl-3.0-and-sycl-2020|title=Catching Up with Khronos: Experts’Experts' Q&A on OpenCL 3.0 and SYCL 2020|last=McIntosh-Smith|first=Simon|date=2020-07-15|publisher=The Khronos Group|access-date=16 February 2025}}</ref> [[Apple Inc.|Apple]] introduced the proprietary [[Metal (API)|Metal]] API for [[iOS]] applications, able to execute arbitrary code through Apple's GPU compute shaders.{{fact|date=June 2024}}

==Hardware support==

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=== Linear algebra ===

Using GPU for numerical linear algebra began at least in 2001.<ref>{{Cite journalbook |lastlast1=Larsen |firstfirst1=E. Scott |last2=McAllister |first2=David |chapter=Fast matrix multiplies using graphics hardware |date=2001-11-10 |title=FastProceedings matrixof multipliesthe using2001 graphicsACM/IEEE hardwareconference on Supercomputing |chapter-url=https://dl.acm.org/doi/10.1145/582034.582089 |language=en |publisher=ACM |pages=55–5555 |doi=10.1145/582034.582089 |isbn=978-1-58113-293-9}}</ref> It had been used for Gauss-Seidel solver, conjugate gradients, etc.<ref>{{Cite journalbook |lastlast1=Krüger |firstfirst1=Jens |last2=Westermann |first2=Rüdiger |title=ACM SIGGRAPH 2005 Courses on - SIGGRAPH '05 |date=2005 |titlechapter=Linear algebra operators for GPU implementation of numerical algorithms |chapter-url=http://portal.acm.org/citation.cfm?doid=1198555.1198795 |language=en |publisher=ACM Press |pages=234 |doi=10.1145/1198555.1198795}}</ref>

==Stream processing==

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* [[Automatic parallelization]]<ref>Leung, Alan, Ondřej Lhoták, and Ghulam Lashari. "[https://cormack.uwaterloo.ca/~olhotak/pubs/pppj09.pdf Automatic parallelization for graphics processing units]." Proceedings of the 7th International Conference on Principles and Practice of Programming in Java. ACM, 2009.</ref><ref>Henriksen, Troels, Martin Elsman, and Cosmin E. Oancea. "[https://futhark-lang.org/publications/fhpc14.pdf Size slicing: a hybrid approach to size inference in futhark]." Proceedings of the 3rd ACM SIGPLAN workshop on Functional high-performance computing. ACM, 2014.</ref><ref>{{Cite book |chapter-url=https://www.researchgate.net/publication/221235428 |doi=10.1145/1375527.1375562|chapter=A compiler framework for optimization of affine loop nests for gpgpus |title=Proceedings of the 22nd annual international conference on Supercomputing - ICS '08 |year=2008 |last1=Baskaran |first1=Muthu Manikandan |last2=Bondhugula |first2=Uday |last3=Krishnamoorthy |first3=Sriram |last4=Ramanujam |first4=J. |last5=Rountev |first5=Atanas |last6=Sadayappan |first6=P. |page=225 |isbn=9781605581583 |s2cid=6137960 }}</ref>

* [[Computational physics|Physical based simulation]] and [[physics engine]]s<ref name="Joselli">Joselli,{{cite Mark,book et| al. "[chapter-url=https://dl.acm.org/doi/10.1145/1401843.1401871 | doi=10.1145/1401843.1401871 | chapter=A new physics engine with automatic process distribution between CPU-GPU]{{cbignore |bot title=medic}}." Proceedings of the 2008 ACM SIGGRAPH symposium on Video games. ACM,| date=2008. | last1=Joselli | first1=Mark | last2=Clua | first2=Esteban | last3=Montenegro | first3=Anselmo | last4=Conci | first4=Aura | last5=Pagliosa | first5=Paulo | pages=149–156 | isbn=978-1-60558-173-6 }}</ref> (usually based on [[Newtonian physics]] models)

** [[Conway's Game of Life]], [[cloth simulation]], fluid [[incompressible flow]] by solution of [[Euler equations (fluid dynamics)]]<ref>{{cite web|url=https://developer.nvidia.com/gpugems/gpugems3/part-v-physics-simulation/chapter-30-real-time-simulation-and-rendering-3d-fluids|title=K. Crane, I. Llamas, S. Tariq, 2008. Real-Time Simulation and Rendering of 3D Fluids. In Nvidia: GPU Gems 3, Chapter 30.}}</ref> or [[Navier–Stokes equations]]<ref>{{cite web|url=http://developer.nvidia.com/GPUGems/gpugems_ch38.html|title=M. Harris, 2004. Fast Fluid Dynamics Simulation on the GPU. In Nvidia: GPU Gems, Chapter 38.|url-status=live|archive-url=https://web.archive.org/web/20171007170306/https://developer.nvidia.com/GPUGems/gpugems_ch38.html|archive-date=7 October 2017|df=dmy-all}}</ref>

* [[Statistical physics]]