What is a CPU (Central Processing Unit)?

CPU stands for Central Processing Unit. The CPU is the central component of every computer and is often referred to as the “brain” of the system. It is responsible for executing program commands and performing calculations.

The history of the CPU dates back to the 1970s, when Intel launched the first commercial microprocessor, the Intel 4004, in 1971. Since then, the technology has developed rapidly: from simple processors with a few thousand transistors to modern CPUs with several billion transistors on a single chip.

A CPU consists of several main components that work closely together:

Performs all arithmetic operations such as addition and subtraction as well as logical comparisons.

Coordinates the sequence of all operations and controls the flow of data between the various units. The control unit interprets instructions and ensures that they are correctly forwarded to the appropriate processing units.

Extremely fast memory cells directly in the CPU, which are used for short-term storage of data and commands.

Acts as a temporary storage between the CPU and main memory. It is divided into several levels: L1 cache is the smallest but fastest, followed by L2 and L3, which are larger but slower.

Modern CPUs are designed as multi-core processors and have several processor cores, each of which functions as an independent processing unit. Each core can process one or more threads simultaneously through threading, which enables the parallel execution of tasks. This architecture allows the workload to be distributed across multiple cores, which significantly increases the overall speed of corresponding applications.

The functioning of a CPU is based on the fetch-decode-execute cycle, a continuous sequence of three steps:

The fetch-decode-execute cycle

Fetch: The CPU fetches the next instruction from the main memory.

Decode: The instruction is interpreted, i.e., the CPU determines which operation is to be performed.

Execute: The CPU executes the instruction and stores the result.

This cycle repeats billions of times per second, continuously processing data processing instructions. The speed at which these instructions are processed depends on the clock rate (also known as clock frequency) and the efficiency of the CPU architecture. The CPU works closely with other components: The main memory (RAM) supplies data and programs, the motherboard connects all components, and the chipset coordinates communication between the CPU and other hardware elements.

The most important technical indicators of a CPU can be summarized as follows:

The clock frequency or clock rate is measured in gigahertz (GHz) and indicates how many calculations a CPU can perform per second. It significantly determines the speed of data processing. A CPU with 3.5 GHz performs 3.5 billion clock cycles per second. However, a higher clock frequency does not automatically mean better performance, as other factors also play a role.

Smaller manufacturing processes allow for more transistors on the same area, higher performance, and lower power consumption. The TDP (Thermal Design Power) is particularly important for selecting the right cooling system.

The CPU landscape can be divided into different categories:

Intel and AMD dominate the desktop market. Intel offers its Core processors in various performance classes, while AMD competes with its Ryzen series. Both manufacturers offer processors ranging from entry-level to high-end gaming and workstation use.

Mobile CPUs for laptops, tablets, and smartphones differ in their focus on energy efficiency. ARM processors dominate the smartphone market and are increasingly being used in laptops. Apple has developed its own ARM-based processors with Apple Silicon (M1, M2, M3, M4), which combine performance and efficiency.

Server CPUs are optimized for continuous operation and high reliability. These processors have a particularly large number of cores, large cache memories, and ECC RAM support to detect and correct memory errors.

In high-performance computing (HPC), CPUs are used under extreme conditions. HPC systems, also known as supercomputers, are used for scientific simulations, climate modeling, genome research, and complex calculations that require enormous computing power. The requirements differ significantly from consumer or standard server applications.

Parallel processing and scalability are key aspects in the HPC field. A single supercomputer can consist of thousands of CPUs working simultaneously on sub-problems. The ability to efficiently distribute tasks across many processors and coordinate the results is critical to the overall performance of the system. Modern HPC CPUs such as AMD EPYC^TM or Intel Xeon Scalable Processors offer up to 96 or more cores per CPU and are specifically optimized for this parallel processing.

In the HPC environment, CPUs often work together with accelerators. GPUs (graphics processing units) are used for massively parallel calculations, while FPGAs (field programmable gate arrays) can be configured for specialized tasks. The CPU often takes on the role of coordinator, distributing tasks and controlling communication between the various computing units.

A GPU (graphics processing unit) specializes in parallel processing and has thousands of simple computing cores. While CPUs have few but complex cores for sequential tasks, GPUs are ideal for graphics calculations and machine learning. A CPU can flexibly handle different tasks, while a GPU is highly specialized in parallel computing operations.

A SoC (System on a Chip) integrates the CPU, GPU, memory controller, and other components on a single chip. This is typical for smartphones and modern laptops. Apple M processors and Qualcomm Snapdragon are examples of SoCs. The advantage lies in their compact design and high energy efficiency, while classic desktop CPUs are designed for maximum computing power.

Despite specialized processors, the CPU remains the central element of a computer, as it handles overall coordination and is responsible for most general computing tasks. Modern systems increasingly use heterogeneous architectures in which the CPU, GPU, and other accelerators play to their respective strengths.