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A general-purpose computing GPU (GPGPU) is a graphics processing unit used for computation beyond graphics rendering. It is most useful when a task can be split into many similar, independent operations that run in parallel. In practice, a CPU usually handles an application’s control and sequential work while the GPU accelerates selected compute-heavy parts.
What does GPGPU mean?
GPGPU means “general-purpose computing on GPUs.” It describes using GPU hardware for general computation rather than only for graphics. NVIDIA’s Base Command Manager 11 manual calls GPUs designed for general-purpose computing “General Purpose GPUs, or GPGPUs,” while NVIDIA’s history of GPU computing describes the shift from graphics-specific work to non-graphics computing.
“General-purpose” does not mean a GPU is equally suited to every task, nor does it necessarily mean a separate kind of device. It describes how GPU hardware is being used. Whether a particular GPU can run a particular workload depends on the task, the software, and hardware compatibility.
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GPUs are designed to process many threads in parallel, making them a strong fit when the same operation can be applied to many independent data elements. Examples of fields that use GPU acceleration include deep learning, scientific computing, and high-performance computing, as described in NVIDIA’s CUDA Programming Guide, version 13.2.0.
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CPUs and GPUs have different design priorities. NVIDIA describes CPUs as emphasizing fast serial-thread execution, while GPUs emphasize throughput across many threads. That distinction can make a GPU useful for highly parallel work, but it does not mean a GPU will always finish a task faster: work that is sequential or cannot be mapped efficiently to parallel operations may not benefit.
How do the CPU and GPU work together?
GPU computing commonly uses both processors rather than replacing the CPU. The CPU can run application control and sequential sections, then send compute-intensive work with enough parallelism to the GPU. This division is often called a hybrid computing model. An application’s performance depends on how well its work maps to the GPU as well as the rest of the system.
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Is CUDA the same as a GPU?
No. A GPU is hardware; CUDA is NVIDIA’s parallel computing platform and programming model for using supported GPUs to accelerate computation. A developer can use CUDA through programming options, libraries, and frameworks documented in NVIDIA’s CUDA Programming Guide.
OpenCL is another, separate API for heterogeneous computing. NVIDIA’s OpenCL developer page describes using it to launch GPU compute kernels. Support depends on the hardware, driver, and software environment; NVIDIA’s documentation describes NVIDIA’s implementation and should not be treated as a universal statement about every GPU vendor or operating system.
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| Term | What it refers to |
|---|---|
| GPGPU | Using GPU hardware for general computation beyond graphics. |
| GPU | The processor hardware that can perform graphics and, when supported, general compute work. |
| CUDA | NVIDIA’s parallel computing platform and programming model. |
| OpenCL | An API for heterogeneous computing that can be used to launch GPU compute kernels. |
What should you check before choosing a GPU for computing?
- Workload fit: Determine whether the task involves many similar operations that can run independently, or mostly sequential work.
- Software support: Check whether the application or framework supports the GPU and the programming interface it requires, such as CUDA or OpenCL.
- System compatibility: Confirm that the GPU, drivers, operating system, and application work together. The term GPGPU alone does not identify a model or guarantee compatibility.
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