CUDA
CUDA, or Compute Unified Device Architecture, is a proprietary and closed-source parallel computing platform and application programming interface (API) developed by Nvidia. It was first introduced in 2006 and is designed to work with programming languages such as C, C++, and Fortran. CUDA allows developers to use the processing power of Nvidia's graphics processing units (GPUs) for general-purpose computing, an approach known as general-purpose computing on GPUs (GPGPU)[1].
CUDA's primary function is to facilitate the development of software that can execute operations in parallel, leveraging the multiple cores of a GPU to perform computations more quickly than a traditional CPU could. This is particularly useful for tasks that can be broken down into smaller, independent tasks, such as image and video processing, numerical computations, and more recently, machine learning and deep learning applications[2].
The CUDA Toolkit provides a development environment for creating high-performance, GPU-accelerated applications. It includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library[3]. CUDA also supports programming frameworks such as OpenMP, OpenACC, OpenCL, and HIP by compiling such code to CUDA[1].
CUDA has several advantages over traditional GPGPU methods. It does not require converting algorithms into a pipe-like format, provides random memory access (scatter or gather), and uses all execution units. It also expands functionality owing to integer arithmetic and bitshift operations. Moreover, CUDA opens some hardware features, which were not available from graphics APIs, such as shared memory[4].
Since its introduction, CUDA has been widely deployed through thousands of applications and published research papers, and supported by an installed base of over 500 million CUDA-enabled GPUs in notebooks, workstations, compute clusters, and supercomputers[7]. It has seen unprecedented growth in the last decade and is being used in a wide variety of applications in various domains[8].
However, CUDA also has some limitations. It is proprietary to Nvidia, meaning it can't be used with GPUs from other manufacturers. Moreover, achieving optimal performance with CUDA often requires an in-depth understanding of both the hardware and software, which can make it challenging for beginners[14].
Citations:
[1] https://en.wikipedia.org/wiki/CUDA
[2] https://developer.nvidia.com/cuda-zone
[3] https://developer.nvidia.com/cuda-toolkit
[4] http://ixbtlabs.com/articles3/video/cuda-1-p3.html
[5] https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html
[6] https://www.merriam-webster.com/dictionary/cuda
[7] https://developer.nvidia.com/about-cuda
[8] https://subscription.packtpub.com/book/data/9781789348293/1/ch01lvl1sec04/cuda-applications
[9] https://developer.nvidia.com/blog/cuda-refresher-getting-started-with-cuda/
[11] https://www.pcmag.com/encyclopedia/term/cuda
[13] https://cse.usf.edu/~haozheng/REU/KubackiReport.pdf
[14] https://typeset.io/questions/what-are-the-advantages-and-disadvantages-of-cuda-in-4qg9uo3rwk
[15] https://cuda-tutorial.readthedocs.io/en/latest/tutorials/tutorial01/
[16] https://blogs.nvidia.com/blog/what-is-cuda-2/
[17] https://www.incredibuild.com/integrations/cuda
[18] https://www.youtube.com/watch?v=-lcWV4wkHsk
[19] https://www.dictionary.com/browse/cuda
[20] https://www.geeksforgeeks.org/introduction-to-cuda-programming/
[21] https://www.turing.com/kb/understanding-nvidia-cuda
[22] https://www.infoworld.com/article/3299703/what-is-cuda-parallel-programming-for-gpus.html
[23] https://www.collinsdictionary.com/us/dictionary/english/cuda