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2 comments:

  1. Comparison between OpenCL and CUDA

    CUDA : Compute Unified Device Architecture is a parallel computing architecture developed by Nvidia. CUDA is the computing engine in Nvidia graphics processing units (GPUs) that is accessible to software developers through variants of industry standard programming languages.

    OpenCL : Open Computing Language is a framework for writing programs that execute across heterogeneous platforms consisting of CPUs, GPUs, and other processors. OpenCL includes a language (based on C99) for writing kernels (functions that execute on OpenCL devices), plus APIs that are used to define and then control the platforms.
    OpenCL was initially developed by Apple Inc., which holds trademark rights, and refined into an initial proposal in collaboration with technical teams at AMD, IBM, Intel, and Nvidia.

    Similarities :
    CUDA and OpenCL are both programming frameworks that allow to use GPUs for general purpose tasks that can be parallelized.
    Both CUDA and OpenCL are supported by multiple operating systems (Windows, Linux, and MacOS.)
    CUDA's "driver API" is similar to OpenCL.


    Differences :
    CUDA is a proprietary GPU architecture and programming framework by Nvidia that is only accelerated on Nvidia hardware.
    OpenCL is an open standard that runs on hardware by multiple vendors, including desktop and laptop GPUs by AMD/ATI and Nvidia. OpenCL can fallback to execution on the host CPU, if a supported GPU is not present.
    CUDA is more mature and have more convenient high-level APIs. This may change as OpenCL is developed further.
    OpenCL has the advantage of being an open standard, allowing any vendor to implement OpenCL support on its products. Intel has announced that it will support OpenCL on future CPU products.
    OpenCL does not appear to support pinned host memory. This may cause a penalty of about a factor of two in host-device transfer rates.
    CUDA's synchronization features are not as flexible as those of OpenCL.
    CUDA has more mature tools, including a debugger and a profiler, also CUBLAS and CUFFT. For C programmer, the CUDA "runtime API" is easier to use than OpenCL, though somewhat more restricted.
    CUDA allows C++ constructs (templates, realistically) in GPU code, OpenCL is based on C99.
    OpenCL can enqueue regular CPU function pointers in its command queues, CUDA can't.
    OpenCL comes with run-time code generation built-in. In CUDA, you have to use tools (such as PyCUDA) to add it.

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    1. If you have any article, please make sure you draft a mail on my gmail id instead of pasting the content here. Also, we are looking for genuine original content that is own written. I could find hell lot of references for above content from other articles.

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