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These were followed by Nvidia's [[CUDA]], which allowed programmers to ignore the underlying graphical concepts in favor of more common [[high-performance computing]] concepts.<ref name="du">{{Cite journal |doi= 10.1016/j.parco.2011.10.002 |title= From CUDA to OpenCL: Towards a performance-portable solution for multi-platform GPU programming |journal= Parallel Computing |volume= 38 |issue= 8 |pages= 391–407 |year= 2012 |last1= Du |first1= Peng |last2= Weber |first2= Rick |last3= Luszczek |first3= Piotr |last4= Tomov |first4= Stanimire |last5= Peterson |first5= Gregory |last6= Dongarra |first6= Jack |author-link6= Jack Dongarra |df= dmy-all |citeseerx= 10.1.1.193.7712 }}</ref> Newer, hardware-vendor-independent offerings include Microsoft's [[DirectCompute]] and Apple/Khronos Group's [[OpenCL]].<ref name="du"/> This means that modern GPGPU pipelines can leverage the speed of a GPU without requiring full and explicit conversion of the data to a graphical form.
Mark Harris, the founder of GPGPU.org, coined the term ''GPGPU''.{{source?|date=
==Implementations==
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The dominant proprietary framework is [[Nvidia]] [[CUDA]].<ref>{{cite web |url=http://www.hpcwire.com/hpcwire/2012-02-28/opencl_gains_ground_on_cuda.html |title=OpenCL Gains Ground on CUDA |access-date=2012-04-10 |url-status=live |archive-url=https://web.archive.org/web/20120423060057/http://www.hpcwire.com/hpcwire/2012-02-28/opencl_gains_ground_on_cuda.html |archive-date=23 April 2012 |df=dmy-all |date=2012-02-28 }} "As the two major programming frameworks for GPU computing, OpenCL and CUDA have been competing for mindshare in the developer community for the past few years."</ref> Nvidia launched CUDA in 2006, a [[software development kit]] (SDK) and [[application programming interface]] (API) that allows using the programming language [[C (programming language)|C]] to code algorithms for execution on [[GeForce 8 series]] and later GPUs.
[[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=
OpenVIDIA was developed at [[University of Toronto]] between 2003–2005,<ref name="Fung">James Fung, Steve Mann, Chris Aimone, "[http://www.eyetap.org/papers/docs/oss1-fung.pdf OpenVIDIA: Parallel GPU Computer Vision] {{Webarchive|url=https://web.archive.org/web/20191223164955/http://www.eyetap.org/papers/docs/oss1-fung.pdf |date=23 December 2019 }}", Proceedings of the ACM Multimedia 2005, Singapore, 6–11 November 2005, pages 849–852</ref> in collaboration with Nvidia.
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