Modern computing demands have skyrocketed due to the rising popularity of industries like deep learning, 3D modeling and rendering, VR gaming, and cryptocurrency mining.
To meet this demand, the hardware elements responsible for delivering computing power evolved over time to keep pace with the requirements of the industries.
In some cases, these components have evolved to the point where it is sometimes challenging to distinguish between their functions within a computer system.
We’ll go into a detailed comparison between the CPU and GPU, the two primary computing engines in any system, to highlight the important points in the CPU vs GPU discussion. Let’s start with the definitions.
Table Of Content
- What is A CPU?
- What is A GPU?
- Difference Between CPU and GPU
- How do CPU and GPU Work Together?
What is A CPU?
The Central Processing Unit (CPU) is typically responsible for executing computing instructions. The CPU, connected to the motherboard by a CPU socket, waits for input from software programs or devices like a keyboard, mouse, and touchpad. The output is then sent to peripherals or stored in memory after being interpreted and processed by the computer.
What is A GPU?
The Graphics Processing Unit (GPU) is a specialized graphics processor designed to handle thousands of operations simultaneously. For example, demanding 3D applications need parallel texture, mash, and light processing to keep images scrolling across the screen smoothly. GPU’s architecture is optimized for processing all these details so that these computations don’t overburden the main CPU. The original goal of adding a GPU to a system was to accelerate graphics rendering.
Here’s a summary of the key comparison points between CPU and GPU
Economical for lighter workloads.
Economical for heavy workloads.
Automatic management of caches.
It allows manual memory management
Minimal instructions per clock
Maximum instructions per clock
Smaller cores in a greater number.
Larger cores in a greater number.
Latency is low
Throughput is high
Serial processing optimization
Parallel processing optimization
For use in complex program execution.
Designed to perform simple and repetitive tasks
Difference Between CPU and GPU
Although both CPUs and GPUs are silicon-based processing chips, their architecture and use cases are very different.
To highlight the key points in the CPU vs GPU debate, we’ll compare the two on several key factors.
This is the fundamental point in this CPU vs GPU.
Let’s start with the CPU.
A CPU has logic gates produced by the billions of transistors interconnected to form functional blocks. A typical CPU has the following three parts:
- The arithmetic and Logic Unit (ALU) has circuits that perform mathematical and logical operations.
- The Control Unit retrieves instructions from the input and sends them to ALUs, Cache, RAM, or peripherals.
- Cache aids in keeping track of subroutines and functions in the program or stores intermediate values required for ALU computations.
CPUs can have multiple cores, each with its own ALU, control unit, and cache.
When it comes to the GPU architecture, you’ll be surprised to learn that it has the same components, only in a much higher density of numerous smaller, more focused cores. That’s why a GPU can execute numerous parallel computing operations by distributing the workload on these multiple cores.
Image rendering is an important application point in the CPU vs GPU discussions.
GPUs are much faster at rendering than CPUs because they are designed primarily for manipulating graphics. GPU rendering can be up to a hundred times faster than CPU rendering, depending on the quality of the hardware and the nature of the workload.
However, rendering quality is not just about speed. For instance, consider the scenario where 3D graphics calls are used for juggling several complex tasks while maintaining data synchronization. Since CPUs are built for complexity and GPUs are made to perform more effortless and more straightforward tasks, CPUs typically perform better in 3D rendering.
Additionally, GPUs can only use the onboard graphics card memory (typically up to 12 GB), which does not stack and can only be increased by slowing down performance. On the other hand, the CPU uses the main system memory, which can be easily expanded to 64 GB.
The CPU uses a cache to reduce the time and power required to retrieve data from memory. The cache is intended to be faster, more compact, and close to the other CPU parts than the main memory.
There are many layers in the CPU cache. All CPU cores share the level furthest from the core, while the level nearest the core is used only by that core.
Contemporary CPUs automatically do cache management. Each layer determines whether the memory fragment should be retained or removed based on the frequency of usage.
The GPU local memory is similar to the CPU cache in terms of structure. However, the most crucial difference is that the GPU memory features non-uniform memory access architecture. It allows programmers to decide which memory pieces to keep in the GPU memory and which to evict, allowing better memory optimization.
When it comes to deep learning, GPUs outperform CPUs by a wide margin. The following significant factors are behind the growing use of GPU servers in deep learning.
- Parallelism: GPUs use thread parallelism (the concurrent use of multiple processing threads), to address the latency issue brought on by the size of the data.
- Memory Bandwidth: Originally intended to speed up the 3D rendering of textures and polygons, GPUs were built to handle sizable datasets. GPUs have wider and faster memory buses because the cache is too small to hold the volume of data that a GPU processes repeatedly.
- Large Dataset Processing: Deep learning models generally have huge datasets. GPUs are a sensible option because of how well they manage computations with high memory requirements.
- Cost Efficiency: Neural network workloads can require a lot of hardware power. GPU-based systems give you much more resources for a lot less money when compared to similar CPU-based systems.
GPUs have a higher hash rate than CPUs, even though it is typically more expensive to mine with them. Since GPUs can process up to 800 times more instructions per clock as CPUs, they are more effective at solving the complex mathematical problems involved in mining. Additionally, GPUs require less upkeep and are more energy-efficient.
How Do CPU and GPU Work Together?
When contrasting the two, it’s critical to remember that GPUs weren’t made to replace CPUs but were made to complement them. The GPU and CPU process data more quickly and at higher rates.
A computer system’s GPU cannot take the place of its CPU. Controlling how tasks are carried out on the system requires the CPU. The CPU can, however, assign specific repetitive workloads to the GPU and free up its own resources necessary for maintaining the stability of the system and the running programs.
We highlighted the major points in the CPU vs GPU debate in this article. We started with a brief overview of how these components are structured and then dived deeper into the important areas where CPU and GPU are compared.
We hope that you now know enough about the debate to understand the major ideas and pick the right component for your next project.
Frequently Asked Questions
1- What can a CPU do better than a GPU?
CPUs are excellent for various tasks, including high-definition, 3D, and non-image-based deep learning on language, text, and time-series data. CPUs can support much more memory than even the best GPUs for complex models or deep learning applications.
2- Is it better to use your CPU or GPU?
The primary distinction between the two is the smaller, simpler control units, ALUs, and caches that GPUs have in abundance. Consequently, a GPU can finish specific tasks quickly, whereas a CPU can handle heavier workloads.
3- What makes a GPU different from a CPU?
The primary distinction between CPU and GPU architecture is that while a CPU can execute multiple tasks concurrently, the number of concurrent tasks that can be performed is constrained by CPU clock speed. A GPU is made to render high-resolution images and video quickly and simultaneously. As such, it can handle a large number of concurrent tasks.
4- Can a GPU replace a CPU?
CPUs are more practical than GPUs for some tasks. Because of this, GPU cards can only partially replace the CPU. CPUs are suitable for sequential processing, while GPUs are good for parallel processing.