The CPU and GPU are processors designed to process and execute various types of instructions and tasks. Although they serve different purposes and offer different benefits, both are an important part of high-performance servers. In fact, they can work together to provide even more processing power.
One of the key differences is that CPUs are designed for serial processing while GPUs are designed for parallel processing. This largely determines the specific characteristics and use cases of each processor type.
To answer the CPU vs GPU question, let’s start by defining them.
CPU: central processing unit
A CPU or central processing unit is a general-purpose silicon processor, composed of an electronic circuit, designed to receive and execute instructions; which includes basic arithmetic, logical, control and input/output operations. In other words, the CPU is considered the brain of servers and computer systems, without which they cannot function.
Modern processors have multiple cores or processing cores, which can process multiple threads at the same time thanks to Intel technology known as “hyperthreading”. Therefore, CPU efficiency increases as the number of processing cores increases.
CPUs can be used for a wide variety of workloads and applications: serial processing, running database management systems, operating systems, etc. Additionally, they are specially optimized to provide low latency and high performance per core, as the processing cores focus on completing individual tasks quickly.
GPU: Graphics processing unit
A GPU or graphics processing unit is a specialized silicon processor, composed of electronic circuits, optimized for computer graphics manipulation and image processing. Unlike the CPU, it is not essential to the operation of a server.
However, the combination of CPU and GPU allows you to handle a wider range of tasks more efficiently and increase processing speed. For example, our NVIDIA Tesla T4 16GB GPU dedicated servers are equipped with 2nd generation Intel® Xeon® Scalable processors to accelerate data processing with the best performance/price ratio.
GPUs are made up of hundreds and even thousands of processing cores, and their parallel architecture allows tasks to be broken down into chunks to run simultaneously on multiple cores. This allows you to handle thousands of threads in parallel and provides great performance.
Likewise, while GPUs were initially used to accelerate video games and graphics, they are now essential in many industries, such as automotive and medical. They can be used for a wide variety of workloads and applications: image recognition, artificial intelligence (AI), supercomputing, etc.
What is the difference between CPU and GPU?
Below we summarize the main differences between GPU and CPU.
|Optimized to execute tasks sequentially and designed to handle a wide range of complex, general-purpose tasks.
More complex arithmetic logic unit (ALU) and control unit.
|Optimized to execute tasks simultaneously and designed specifically for parallel processing.
Simpler ALU and control unit.
|Number of cores
|A few powerful cores that execute tasks in order, following the FIFO (first in, first out) method.
|Hundreds or even thousands of slower processing cores, optimized for parallel computing.
|Faster cache memory and larger memory capacity.
|Smaller cache memory and higher memory bandwidth.
|Optimized for high-performance single-threaded tasks, with a focus on providing low latency.
|Optimized for parallel computations, with a focus on providing higher performance and computational speed.
|General computing, operating systems and a wide range of applications.
CPUs can execute a large number of tasks.
|Graphics rendering, robotics, simulations, machine learning (ML), Big Data analytics, etc.
GPUs can only execute graphics-related tasks.
|Designed to operate with low energy consumption.
|Greater energy consumption due to having a greater number of cores, but optimized performance for each unit of energy.
|It is usually cooled using small coolers or heatsinks.
|It is cooled with fans or liquid cooling systems, since it generates more heat.
In short, both GPUs and CPUs are critical components of modern computers and servers. Servers with GPUs are key to accelerating high-performance computing, AI, and machine learning, among other workloads. Likewise, apart from these, there are also other processors such as quantum processors, made of aluminum.