What’s the Difference Between a CPU and a GPU?

CPUs and GPUs are similar in their ability to process thousands of operations per second, but were originally designed for different tasks. This article explores the distinctions between CPUs and GPUs and their respective functionalities. 

It is important to ensure that server CPUs meet specific reliability, uptime, and self-correcting system requirements. While server CPUs have a slightly different architecture designed for 24/7 operation and high load, graphics processors in servers are intended for continuous processing of large data arrays. NVidia has developed a range of GPUs specifically for server hardware, with QUADRO GPU for deep learning, and TESLA for virtualization to support more complex tasks.

What is a CPU (Central Processing Unit)?

CPU is responsible for the consistent and fast execution of tasks, including processing data from RAM, sending commands to internal and external devices, storing calculations and commands history, and receiving and processing signals from external devices. The CPU's performance is determined by clock frequency and bit rate, enabling it to perform various tasks, from simple calculations to running highly loaded programs.

What is a GPU (Graphics Processing Unit)?

GPU is optimized for parallel computing, rendering graphics and performing complex calculations simultaneously. The solution is ideal for 3D graphics, virtual reality, scientific research.

Key Differences between CPU and GPU

Cache memory is crucial for CPUs and occupies a significant amount of device memory. In contrast, a GPU does not require a large cache memory, typically needing 128-256 kB for rendering images.

A CPU consists of an arithmetic-logic unit (ALU), a control unit (CU), and memory, responsible for determining the speed of program execution. On the other hand, a GPU is optimized for processing graphics data, converting data formats and rendering images.

Processing Method
While a CPU performs sequential tasks such as calculations, OS management, and program execution, a GPU processes data in parallel, enabling it to perform intensive tasks.

Processing Speed
Clock frequency, measured in Hertz, is a key technical indicator for any processor.

Number of threads
The number of threads supported by the CPU is two per core, while the GPU can handle thousands of threads per multiprocessor, with several multiprocessors in a single chip. The CPU requires hundreds of clock cycles to switch from one thread to another, while the GPU can switch several threads in one clock cycle.

CPU and GPU in server hardware

The CPU processes information sequentially, which is optimal for tasks of the same priority type. However, if a failure occurs at any stage, the whole process will fail. By using multiple cores, multitasking can be achieved, but the data is still processed streamingly, just separately on each core.

The GPU architecture is very different. It already provides for parallel processing of tasks, which guarantees both multitasking and increased stability. This means that GPU performance is significantly higher and does not require a cache to process data arrays. These features are why GPUs are often used in servers that work with large data arrays, graphics and video.

Cloud4U provides a cloud GPUs server rental service. You gain access to the computing power required to handle machine learning, 3D modellingб AI, and high-performance computing without the need to invest in hardware.

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