What’s the Difference Between GPUs and CPUs?

A CPU, or central processing unit, is a hardware component that is the core general-purpose computational unit in a server. It handles all types of computing tasks required for the operating system and applications to run. A graphics processing unit (GPU) is a similar hardware component but more specialized, due to its parallel architecture. It can more efficiently handle complex mathematical operations that run in parallel than a general CPU. While GPUs were initially created to handle graphics rendering tasks in gaming and animation, their uses now extend far beyond that and are the primary chip in machine learning inference.

A central processing unit (CPU) is the processor that coordinates and processes most general computing tasks on a device. The CPU communicates with different segments, such as storage and memory, while also interpreting and managing instructions from applications running on the computer. Most CPUs are silicon-based microprocessors, with multiple cores supporting multithreading, which lets them process instructions across multiple tasks at once. These have developed from the original design, a multi-chip configuration, to now have the entire CPU on a single IC computer chip.

A CPU is mainly optimized for sequential processing, which helps with tasks that involve system coordination, data processing, or complex logic. While CPUs contain fewer cores than GPUs, every core is powerful and able to handle its tasks efficiently.

A graphics processing unit (GPU) is a specialized type of processor that mainly focuses on tasks that need parallel processing. Originally, these workloads were mainly for graphics (hence the name), as the processor on a video card or graphics card. However, more modern GPU hardware is used to process lots of data simultaneously, making it a useful addition for workloads like image processing and video rendering.

A GPU contains more cores than a CPU, meaning it can divide up a complex task into many smaller chunks and run them in parallel. Due to this architecture, its parallel processing capabilities are better than a CPU, making it useful for tasks such as artificial intelligence workloads, including machine learning and deep learning. Anything that needs to analyze parallelizable large data sets or train neural networks can benefit from its processing capabilities.

You’ll commonly find GPUs in data centers to help speed up the training and inference processes for AI models.

Both CPUs and graphics processing units (GPUs) are hardware units that make a computer work. You can think of them as the brain of a computing device. Both of them have similar internal components, including cores, memory, and control units.

Core

Both GPU and CPU architectures have cores that run all computations and logical functions. The core pulls instructions from memory in the form of binary-encoded data. It decodes the instructions and runs them through logical gates in a time frame called an instruction cycle. In the mid-2000s, CPUs had a single core, but today multi-core CPUs and GPUs are common.

Most modern GPUs and CPUs include many processing cores so that they can handle multi-task workloads at once. While a CPU typically has fewer cores that are optimized for sequential processing, a GPU has more cores and instead uses parallel processing. Despite having different designs, both use these cores to process workloads and support active applications.

Memory

Both CPUs and GPUs can complete billions of calculations every second and use internal memory to improve processing performance. The cache is the built-in memory that facilitates quick data access. In CPUs, the labels L1, L2, or L3 indicate cache arrangements. L1 is the fastest, and L3 is the slowest. A memory management unit (MMU) controls virtual to physical memory mapping between the CPU core, cache, and RAM in every instruction cycle. GPUs use dedicated VRAM for memory with an L1 and L2 cache.

These memory hierarchies help processors move data at scale while also managing a huge number of tasks. Many modern workloads, like machine learning tasks, artificial intelligence applications, and video rendering, all rely on fast memory, meaning CPU and GPU workloads must be able to coordinate demanding operations without delay.

Control unit

The control unit synchronizes processing tasks synchronized to the frequency of electric pulses that the processing unit generates within a clock cycle. CPUs and GPUs with higher frequencies provide better performance, although parallelism matters more for performance. However, the design and configuration of these components differ between a CPU and a GPU, so the two are useful in different situations.

A control unit is the central part that directs internal units such as the cache and registers when managing high-demand workloads like training neural networks, deep learning, or anything involving large data sets. Effective coordination ensures the system never gets overwhelmed.

The arrival of computer graphics and animation resulted in the first compute-intensive workloads that consumer CPUs were simply not designed to handle. For example, video game animation requires applications to process data to display millions of pixels—each with its own individual color, light intensity, and movement. Geometric mathematical calculations for 3D rendering on CPUs at the time led to performance issues.

Hardware manufacturers began to recognize that offloading common multimedia-oriented tasks could relieve the CPU and increase performance. Today, graphics processing unit (GPU) workloads handle several compute-intensive applications, such as machine learning and artificial intelligence, more efficiently than CPUs.

Function

The main difference between a CPU and a GPU lies in their functions. A server or other machine cannot run without a CPU. The CPU handles all the tasks required for all software to run correctly. A GPU, on the other hand, supports the CPU by performing concurrent calculations. A GPU can complete simple and repetitive tasks much faster because it can break the task down into smaller components and finish them in parallel.

Design

GPUs excel in parallel processing through several cores or arithmetic logic units (ALUs). GPU cores are typically less powerful than CPU cores and have less memory, although advanced GPUs have expansive VRAM. While CPUs can switch between different instruction sets rapidly, a GPU takes a high volume of the same instructions and pushes them through at high speed. As a result, GPU functions play an important role in parallel computing.

Example of the differences

To understand better, consider the following analogy. The CPU is like a head chef in a large restaurant who has to make sure hundreds of burgers get flipped. Even if the head chef can do it personally, it’s not the best use of time. All kitchen operations may halt or slow down while the head chef is completing this simple but time-consuming task. To avoid this, the head chef can use junior assistants who flip several burgers in parallel. The GPU is more like a junior assistant with ten hands who can flip 100 burgers in 10 seconds.

It’s important to note that the choice between CPUs and graphics processing units (GPUs) is not an either-or decision. For example, every server or server instance in the cloud requires a CPU to run. However, some servers also include GPUs as additional coprocessors. Specific workloads are better suited to run on servers with GPUs that perform certain functions more efficiently. For example, GPUs can be great for floating-point number calculations, graphics processing, or data pattern matching.

Here are some applications where it may be useful to use GPUs over CPUs.

Deep learning and machine learning

Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way inspired by the human brain. For example, deep learning algorithms recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions. GPU-based servers provide high performance for machine learning, neural networks, and deep learning tasks.

High-performance computing

The term “high-performance computing” refers to tasks that require very high computing power. Here are some examples:

• You need to run geoscientific simulations and seismic processing at speed and scale
• You need to project financial simulations to identify product portfolio risks, hedging opportunities, and more
• You need to build predictive, real-time, or retrospective data science applications in medicine, genomics, and drug discovery

A GPU-based computer system is better suited for parallelizable high-performance computing tasks like these.

Autonomous vehicles

To develop and deploy advanced driver-assistance systems (ADAS) and autonomous vehicle (AV) systems, you need highly scalable computing, storage, networking, and analytics technologies. For example, you require capabilities for data collection, labeling and annotation, map development, algorithm development, simulations, and verification. Such complex workloads require the support of GPU-based computer systems to function efficiently.

Media processing and computer graphics

Another scenario where GPUs are more useful than solely relying on a CPU is in any real-time graphics workload. Video editing or large-scale streaming media encoding and transcoding all benefit from a GPU’s parallel processing capabilities. As a GPU can manage multiple calculations at once through parallel processing, it can process visual data much faster and with greater efficiency than a CPU.

	CPU	Graphics processing unit (GPU)
Function	A generalized component that handles the main processing functions of a computer	Specialized component that excels at parallel computing
Processing	Designed for serial instruction processing	Designed for parallel instruction processing
Design	Fewer, more powerful cores	More cores than CPUs, but less powerful than CPU cores
Best suited for	General-purpose computing applications	High-performance computing applications, gaming and rich media, machine learning, and artificial intelligence

Amazon Web Services (AWS) offers Amazon Elastic Compute Cloud (Amazon EC2), the broadest and deepest compute platform. It has more than 500 instances and your choice of the latest processor, storage, networking, operating system, and purchase model to help you best match the needs of your workload.

Here are some highlights of what Amazon EC2 offers:

• General-purpose instances provide a balance of computing, memory, and networking resources. You can choose between configurations with 2-192 virtual CPUs.
• GPU-based instances provide graphics processing units (GPUs) for extra computing power. You get up to eight GPUs in each instance.
• Specialized AI accelerators such as Trainium and Inferentia might be better for machine learning workloads than regular GPU instances.

Get started with server instances on AWS by creating a free account today.