CPU is a computer’s central processing unit that performs arithmetic and logic operations with minimum latency. In contrast, a GPU is an embedded or external graphics processing unit that can perform floating-point arithmetic to render the polygonal coordinates in high-resolution images and videos. This article discusses the differences between a CPU and a GPU.
What Is a CPU?
A CPU is the central component that distinguishes a computer device. However, it can only operate with the assistance of additional components like silicon chips. The silicon chip is installed in a particular socket on the device’s primary circuit board (the motherboard or mainboard).
It is distinct from memory, where temporary information storage occurs. Furthermore, it is different from the graphic card or graphics chip since these create the video and 3D images shown on the screen and are built using graphics processing unit (GPU) technology.
CPUs are present in nearly all consumer devices, such as smartwatches, computers, and thermostats. As the principal operating center of any gadget, they are accountable for executing and processing instructions.
How does a CPU work?
The central processing unit contains a control unit that manages and coordinates the computer’s functions. It identifies and collects commands from the main memory in the correct sequence. It then interprets them so that the other operational components of the system are activated at the right time to complete their respective functions.
All input information is transferred from the main memory into the arithmetic-logic units (ALUs) for processing, which includes four basic arithmetic operations — addition, subtraction, multiplication, and division — and certain logic operations. These include data comparison and problem-solving or a viable alternative based on preset decision criteria.
Here are the key features of a CPU:
- Multiple cores embedded in the same chip: Multicore processors are used in modern PCs. Each core is functionally equivalent to the others. Each core has its own cache but may interact with other cores when necessary. The GPU core of AMD’s Accelerated Processing Unit (APU) technology is superior to the conventional core in certain aspects of mathematical operations. However, GPU and CPU are different in functioning, as we will see later.
- Ability to communicate with other components and regulate resource allocation: CPUs are a component of the larger platform. They must interact with the external surrounding world. It uses data buses (data-carrying circuits) to interact with the chipsets on the motherboard. The rate of this transmission is referred to as its bandwidth.
- Use of small amounts of built-in memory: Cache refers to the minute quantities of rapid memory integrated directly into the CPU core. Level 1 (L1) cache is quicker but less extensive than Level 2 (L2). Some CPUs contain a third level that is often shared by many CPU cores. Note that this memory is distinct from random access memory (RAM).
- Measurement of speed in hertz: Generally, processor performance is measured in MHz (megahertz) or GHz (gigahertz.). This number represents the frequency, in cycles per second, of the CPU’s internal clock. A 2.5GHz processor’s clock ticks 2.5 billion times every second.
- Measurement of efficiency in IPC: Efficiency, or the amount of work the CPU can perform in every clock cycle, is just as crucial as speed. IPC, or instructions per cycle, is a common abbreviation used to quantify CPU performance. It is essential to realize that IPC is not static; it varies based on the running application.
In conclusion, a CPU or central processing unit is a general-purpose processor that performs many activities. In contrast, the GPU, or graphics processing unit, is a specialized processor with greater mathematical computing capabilities, making it perfect for computer graphics and machine learning activities. Let us explore this concept in detail.
What Is a GPU?
A graphics processing unit (GPU) is an electrical circuit or chip that can display graphics on an electronic device. GPUs are primarily of two types:
- Integrated graphics processing units: Integrated GPUs are incorporated into the computer’s motherboard, enabling compact, lightweight, and energy-efficient laptops. They are suitable for gameplay, basic video editing, and photo editing.
- Discrete graphics processing units: A discrete GPU is a separate component inserted into a computer. They are larger and more energy-dependent than integrated GPUs but are ideal for intensive video and picture editing, graphic design, and gameplay. They are also heavily used for enterprise operations and scientific research.
GeForce 256 from NVIDIA was the first widely used GPU. It was a CPU explicitly designed for real-time graphics, an application that requires extensive arithmetic operations and a high memory bandwidth. With the evolution of real-time graphics, GPUs became programmable.
The most advanced graphics processing units (GPUs) propel modern supercomputers, and serve as the predominant foundation for deep learning. It also offers intelligence for various autonomous vehicles, robotics, and smart cameras.
How does a GPU work?
GPUs are designed specifically for rigorous mathematical and geometric computations. Graphics used in films and video games are composed of polygonal coordinates rendered into bitmaps and subsequently into signals displayed on a screen. The transformation requires a GPU with substantial computing capacity. Here are the main characteristics of a GPU:
- A large number of arithmetic logic units (ALUs): A GPU can manage vast quantities of data across several streams. This is due to their enormous amount of ALUs. As a consequence, they can perform massive quantities of arduous mathematical tasks. In addition, GPUs include hundreds of cores that can manage several processing threads simultaneously.
- Connectivity through ports: Multiple ports may be used to link a discrete GPU to the display. However, the port must be available on the monitor and the GPU device. VGA, HDMI, or DVI are examples of popular connection ports.
- The ability to perform floating-point arithmetic: GPUs can perform vector and floating-point calculations, i.e., mathematical operations on an approximate representation of real numbers. The most recent graphics cards (with integrated GPUs) can manipulate double-precision floating-point values.
- Suitability for parallel computing: GPUs are primarily intended for activities that can be parallelized significantly.
Before the introduction of GPUs in the 1990s, visual rendering was performed by the Central Processing Unit (CPU). When utilized in combination with a CPU, a GPU may improve computer speed by performing computationally intensive tasks, such as rendering, that the CPU was previously responsible for. This increases the processing speed of programs since the GPU can conduct several computations concurrently. The shift also spearheaded the creation of software with greater complexity and resource requirements.
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CPU vs. GPU: 11 Key Comparisons
The CPU and GPU processors excel at different things in a computer system. CPUs are more suited to dedicate power to execute a single task, while GPUs are more suited to calculate complex data sets simultaneously. Here are some more ways in which CPUs and GPUs are different.
1. Intended function in computing
CPU refers to the central processing unit. A CPU is a general processor critical to all modern computing systems as it executes the commands and processes needed for the computer and its operating system to function effectively. Thus, it is often referred to as the brain of the computer.
As mentioned previously, the CPU comprises an arithmetic logic unit (ALU), a control unit (CU), and memory. The control unit manages the data flow while the ALU performs logical and arithmetic operations on the memory-provided data. The CPU determines the speed at which programs can run.
GPU refers to a graphics processing unit, also known as a video card or a graphic card. GPU is a specialized processor designed and optimized to process graphical data. Thus, converting data such as images from one graphic format to another. It can also render images by creating 2D or 3D images, which is widely used in 3D printing workflows.
2. The operational focus
A CPU focuses on low latency. A computer with low latency is usually optimized to process a high volume of instructions or data transfer with minimal delays. In a CPU, latency refers to the time delay between a device making a request and the time the CPU fulfills it, and this delay is measured in clock cycles.
The latency levels in a CPU may rise as a result of cache misses and misalignments. Usually, high latency is associated with increased webpage load times and application failures.
In contrast, the GPU focuses on high throughput. Throughput refers to the maximum number of similar instructions that can be executed per clock cycle when the operands of each instruction are independent of the preceding instructions. Low throughput may result from memory bandwidth limitation, algorithm branch divergence, and memory access latency.
3. Operational functions
A CPU performs four main functions – fetch, decode, execute, and write back:
- Fetch refers to the operation in which the CPU receives instructions from program memory.
- Decode refers to converting the instructions by the instruction decoder to determine which other parts of the CPU are needed to proceed.
- Execute refers to fulfilling the instructions.
- Writeback refers to a caching technique that copies data to higher-level caches or memory.
A GPU’s primary function is to manage and improve video and graphics performance. It has features such as texture mapping, hardware overlay, decoding Moving Picture Experts Group (MPEG) files, and digital output to the screen monitor. These are designed to reduce workload and produce faster graphics. The GPU also performs calculations related to 3D and floating-point operations.
4. The use of cores
Modern CPUs have between two and 18 powerful cores, and each can perform a different job while working simultaneously. A core can be split into virtual cores known as threads through a process known as simultaneous multithreading. For instance, a CPU with four cores can be split to provide eight threads.
The efficiency of a CPU increases with an increase in the number of cores as it can run more programs simultaneously and handle a wide range of tasks. Therefore, a CPU core is optimized for serial computing and running database management systems (DBMS).
GPU cores are slower than CPUs for serial computing but much faster for parallel computing as they have thousands of weaker cores that are best suited for parallel workloads. GPU cores are specialized processors for handling graphics manipulations.
5. Serial and parallel instruction processing
In serial processing, a single task is performed at a time, while in parallel processing, multiple tasks are performed simultaneously.
In serial processing, every task is completed using the same average time. Instructions are completed using the first in, first out (FIFO) technique. CPUs are more suitable for serial instruction processing as they can use a single core to execute one task after finishing the other. A program counter determines the order of execution of the instructions.
Meanwhile, tasks are split among multiple processors in parallel processing to reduce the time to run a program. GPUs are more suitable for parallel instruction processing. The architecture of GPUs allows them to perform numerous calculations across data streams simultaneously. Thus, increasing the speed of a computer system. Parallel processing aims to enhance a computer system’s computational speed and increase its throughput.
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6. Versatility and interaction with other components
The CPU is more versatile in comparison to the GPU. It has a broader range of instructions and can execute numerous tasks. When executing instructions, the CPU also interacts with more computer components such as RAM, ROM, the basic input/output system (BIOS), and input/output (I/O) ports.
In comparison, the GPU can only receive a limited set of instructions and execute only graphics-related tasks. The GPU interacts with fewer computer components when executing instructions. Often, the GPU only requires interaction with the display and memory units when determining how to display pixels on the screen.
7. Execution of tasks
CPUs can handle most consumer-grade tasks, even complex ones, despite their relatively slow speed. CPUs can also handle graphic manipulation tasks with much-reduced efficiency. However, CPUs outdo GPUs when it comes to 3D rendering due to the complexity of the tasks. Additionally, CPUs have more memory capacity, so users can quickly expand up to 64GB without affecting performance.
GPUs mainly enhance images and render graphics significantly faster than CPUs. Combining GPUs with high-end computer components can render graphics up to 100 times faster than CPUs. Despite their high speeds, GPUs are usually designed to perform simple and non-complex tasks. Additionally, GPUs have a limited graphics card memory of up to 12GB which does not stack and cannot be easily expanded without causing a drop in performance and bottlenecks.
8. Hardware limitations
CPU manufacturers face a significant roadblock due to hardware limitations. In 1965, Moore’s Law was created based on observations and projections of historical trends, setting the pace for the modern digital revolution. This law states that the number of transistors on a silicon chip doubles every two years while the cost of the computers is halved. However, 57 years later, his observations are probably nearing the end. Today, there is a limit to the number of transistors that can be added to a piece of silicon. Nevertheless, manufacturers have sought to overcome these hardware limitations using distributed computing, quantum computers, and silicon replacements.
On the other hand, GPU manufacturers face no hardware limitations so far. Huang’s law observes that the rate of GPUs advancement is much faster than that of CPUs. It also states that the performance of GPUs doubles every two years.
9. API limitations
An application programming interface (API) is a software interface that provides a way for computer programs to communicate with each other. CPU manufacturers have no API limitations on their CPUs. Data APIs work seamlessly with the CPU without limiting functionalities. However, GPUs have limited graphics APIs; these are also hard to debug, further limiting their applications.
OpenCL and Compute Unified Device Architecture (CUDA) are the most popular graphics rendering APIs for GPUs. OpenCL is an open-sourced API that works well with AMD GPU hardware but is very slow on Nvidia hardware. CUDA is a proprietary API owned by Nvidia and is optimized for use with Nvidia GPUs. However, the specificity design of CUDA users in their ecosystems makes it difficult to change in the future.
10. Context switch latency
Context switch latency refers to the time it takes for a processing unit to execute a process. When a request with instructions is made, a dependency chain is automatically initiated, where one process depends on the previous one until the request is fulfilled. A CPU switches more slowly between multiple threads as a result of storing information in registers. In contrast, GPU tasks are executed simultaneously. This means there is no inter-warp context switching, where registers must be saved to the memory and restored.
11. The caching approach
The CPU retrieves data from memory efficiently using a cache to save time and energy. The cache is a smaller and faster memory closer to (and often embedded in) the CPU that stores copies of data from frequently used main memory locations. The CPU cache consists of multiple levels, often up to level 3 and sometimes level 4. Each level decides whether a particular memory should be kept or deleted based on how frequently it is accessed. Modern CPUs automatically perform cache management.
Notably, the structure of the local memory of the GPU is similar to that of the CPU. However, the GPU memory has a non-uniform memory access architecture, allowing programmers to select which memory to keep and which to delete from GPU memory. This enables better memory optimization.
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The central processing unit (CPU) and graphics processing unit (GPU) are two of the most critical components of PCs and mainframes, without which modern computing cannot function. Most advancements in this field – from artificial intelligence and supercomputers to predictive analytics – depend on these two foundational building blocks. By understanding the differences between CPU and GPU, individual power users and IT decision-makers can better use their infrastructure and endpoint environments to unlock better outcomes.