Graphics processing unit (GPU)

A graphics processing unit (GPU) is a computer chip that performs rapid mathematical calculations, primarily for the purpose of rendering images. In the early days of computing, the central processing unit (CPU) performed these calculations. As more graphics-intensive applications such as AutoCAD were developed, however, their demands put strain on the CPU and degraded performance. GPUs came about as a way to offload those tasks from CPUs and free up processing power.

Today, graphics chips are being adapted to share the work of CPUs and train deep neural networks for AI applications. A GPU may be found integrated with a CPU on the same circuit, on a graphics card or in the motherboard of a personal computer or server. NVIDIA, AMD, Intel and ARM are some of the major players in the GPU market.  

GPU vs. CPU

A GPU is able to render images more quickly than a CPU because of its parallel processing architecture, which allows it to perform multiple calculations at the same time. A single CPU does not have this capability, although multicore processors can perform calculations in parallel by combining more than one CPU onto the same chip.

A CPU also has a higher clock speed, meaning it can perform an individual calculation faster than a GPU so it is often better equipped to handle basic computing tasks.

In general, a GPU is designed for data-parallelism and applying the same operation to multiple data-items (SIMD). A CPU is designed for task-parallelism and doing different operations.

GPU (graphics processing unit)

A graphics processing unit (GPU) is a computer chip that performs rapid mathematical calculations, primarily for the purpose of rendering images. In the early days of computing, the central processing unit (CPU) performed these calculations. As more graphics-intensive applications such as AutoCAD were developed, however, their demands put strain on the CPU and degraded performance. GPUs came about as a way to offload those tasks from CPUs and free up processing power.

Today, graphics chips are being adapted to share the work of CPUs and train deep neural networks for AI applications. A GPU may be found integrated with a CPU on the same circuit, on a graphics card or in the motherboard of a personal computer or server. NVIDIA, AMD, Intel and ARM are some of the major players in the GPU market.  

GPU vs. CPU

A GPU is able to render images more quickly than a CPU because of its parallel processing architecture, which allows it to perform multiple calculations at the same time. A single CPU does not have this capability, although multicore processors can perform calculations in parallel by combining more than one CPU onto the same chip.

A CPU also has a higher clock speed, meaning it can perform an individual calculation faster than a GPU so it is often better equipped to handle basic computing tasks.

In general, a GPU is designed for data-parallelism and applying the same operation to multiple data-items (SIMD). A CPU is designed for task-parallelism and doing different operations.

How a GPU works

CPU and GPU architectures are also differentiated by the number of cores. The core is essentially the processor within the processor. Most CPUs have between four and eight cores, though some have up to 32 cores. Each core can process its own tasks, or threads. Because some processors have multithreading capability -- in which the core is divided virtually, allowing a single core to process two threads -- the number of threads can be much higher than the number of cores. This can be useful in video editing and transcoding. CPUs can run two threads (independent instructions) per core (the independent processor unit). GPUs can have four to 10 threads per core.