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A general-purpose graphics processor is a graphics processing unit (GPU) used for computation beyond rendering images. That use is commonly called general-purpose computing on the GPU (GPGPU) or GPU computing. The GPU is the hardware; GPGPU is a way of using it.
What makes a GPU “general-purpose”?
GPUs began as processors for graphics, but their programmable resources can also perform other kinds of computation. In a 2008 overview, John D. Owens and co-authors describe the GPU as both a graphics engine and a highly parallel programmable processor, and use “GPGPU” for applying it to broader computing tasks. The article appeared in Proceedings of the IEEE on May 1, 2008, so its conceptual description remains useful while its performance comparisons should be read as historical. Read the overview.
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The term does not mean that a GPU has stopped handling graphics, nor that it is a universal replacement for a CPU. It means that the GPU is being used for non-graphics work as well as, or instead of, rendering.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteHow GPU computing works
GPU computing is most naturally suited to work that can apply similar operations to many data elements, with relatively few dependencies between those elements. This allows many pieces of work to be processed in parallel. A workload that must proceed one step at a time may make less use of that strength.
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In a common arrangement, the CPU coordinates the application and sends suitable work to the GPU. NVIDIA’s CUDA programming model describes CPU-side “host” code copying data between host and device memory, launching GPU code, and waiting for execution or transfers to finish. The amount of data movement matters: transferring data can add overhead, so performance depends on more than the GPU’s ability to perform calculations. NVIDIA’s CUDA programming model explains this host-and-device relationship.
What kinds of work can use a GPU?
GPU computing is used in areas that include scientific and technical computing, mathematical computation, game physics, and computational biophysics. Intel describes general-purpose GPU computing as computation beyond traditional image and video graphics creation in its oneAPI Optimization Guide, version 2023.2. These are examples of workload categories, not a promise that every application—or every GPU—will benefit.
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GPU versus CPU: what determines whether acceleration helps?
There is no automatic speed advantage just because a task runs on a GPU. Whether GPU acceleration is useful depends on the workload, the software and the device. Before expecting a benefit, consider:
- Parallelism: Can the computation operate on many data elements at once?
- Dependencies: Can those elements proceed largely independently, or must each step wait for earlier results?
- Data movement: How much data must be transferred between host and device memory, and how often?
- Software support: Does the application or programming environment support the target GPU and its programming model?
- Measured results: Is there a benchmark for the specific workload and device? Without one, a general definition cannot establish an expected speedup.
What CUDA and other programming models mean
CUDA is NVIDIA’s programming platform for using GPU capabilities in computational workloads. NVIDIA says CUDA was introduced in 2006 to let developers use GPU throughput independently of graphics APIs; that is NVIDIA’s account of its own platform history, not a description of the only way to program GPUs. Intel’s oneAPI guide is another example of vendor-specific GPU programming documentation. These sources do not establish that programming interfaces, supported features or performance are interchangeable between vendors. For implementation details, consult documentation for the particular platform and version you plan to use.
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Is a graphics card the same thing as a GPU?
No. A GPU is the processor; a discrete graphics card is a physical product that contains GPU hardware. A computer can also have GPU hardware integrated into another component rather than on a separate card. The term “general-purpose graphics processor” describes what the processor can be used for, not a particular card, model or compatibility guarantee.
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