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  2. Z-buffering - Wikipedia

    en.wikipedia.org/wiki/Z-buffering

    Z-buffering is a technique used in almost all contemporary computers, laptops, and mobile phones for generating 3D computer graphics.The primary use now is for video games, which require fast and accurate processing of 3D scenes.

  3. General-purpose computing on graphics processing units

    en.wikipedia.org/wiki/General-purpose_computing...

    General-purpose computing on graphics processing units (GPGPU, or less often GPGP) is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU).

  4. RPCS3 - Wikipedia

    en.wikipedia.org/wiki/RPCS3

    RPCS3 is a free and open-source emulator and debugger for the Sony PlayStation 3 that runs on Windows, Linux, FreeBSD and macOS operating systems, allowing PlayStation 3 games and software to be played and debugged on a personal computer.

  5. RSX Reality Synthesizer - Wikipedia

    en.wikipedia.org/wiki/RSX_Reality_Synthesizer

    The RSX 'Reality Synthesizer' on a PlayStation 3 motherboard. The RSX 'Reality Synthesizer ' is a proprietary graphics processing unit (GPU) codeveloped by Nvidia and Sony for the PlayStation 3 game console.

  6. AOL latest headlines, entertainment, sports, articles for business, health and world news.

  7. Volume rendering - Wikipedia

    en.wikipedia.org/wiki/Volume_rendering

    This technique is relatively fast in software at the cost of less accurate sampling and potentially worse image quality compared to ray casting. There is memory overhead for storing multiple copies of the volume, for the ability to have near axis aligned volumes. This overhead can be mitigated using run length encoding.

  8. AOL Mail for Verizon Customers - AOL Help

    help.aol.com/products/aol-mail-verizon

    AOL Mail welcomes Verizon customers to our safe and delightful email experience!

  9. Precision and recall - Wikipedia

    en.wikipedia.org/wiki/Precision_and_recall

    In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).