While CPUs have and still own general-purpose computing, GPUs have surpassed CPUs as the hardware of choice for specific use cases and workloads like mining, gaming, and machine learning.
Our comprehensive guide will first explore the key difference between a CPU and GPU, and then do a comprehensive GPU vs CPU performance comparison by highlighting the use cases, architecture, and limitations of each.
What is CPU and GPU?
A Central Processing Unit (CPU) is a latency-optimized general purpose processor that is designed to handle a wide range of distinct tasks sequentially, while a Graphics Processing Unit (GPU) is a throughput-optimized specialized processor designed for high-end parallel computing.
Difference between GPU vs CPU fundamentals
Before we delve into details like CPU and GPU architecture or compatibility, the below table gives a quick overview of the main differences between CPU and GPU.
|A few heavyweight cores
|Many lightweight cores
|High memory size
|High memory throughput
|Many diverse instruction sets
|A few highly optimized instruction sets
|Explicit thread management
|Threads are managed by hardware
What is a CPU?
A Central Processing Unit (CPU) is the brains of your computer. The main job of the CPU is to carry out a diverse set of instructions through the fetch-decode-execute cycle to manage all parts of your computer and run all kinds of computer programs.
A presentation about a modern CPU manufacturing by Intel
What is a GPU?
A Graphics Processing Unit (GPU) is a specialized processor whose job is to rapidly manipulate memory and accelerate the computer for a number of specific tasks that require a high degree of parallelism.
GPU vs. CPU demo by Adam Savage and Jamie Hyneman from Nvidia
CPU vs GPU architecture
Whereas CPUs have a few powerful cores for fast sequential operation, GPU architecture contains thousands of smaller, more power-efficient cores for parallel workloads, among other dissimilarities. Let’s do a little deep dive into other key differences between CPU vs GPU architecture.
A CPU is very fast at processing your data in sequence, as it has few heavyweight cores with high clock speed. It’s like a Swiss army knife that can handle diverse tasks pretty well.
The CPU is latency-optimized and can switch between a number of tasks real quick, which may create an impression of parallelism. Nevertheless, fundamentally it is designed to run one task at a time.
As the GPU uses thousands of lightweight cores whose instruction sets are optimized for dimensional matrix arithmetic and floating point calculations, it is extremely fast with linear algebra and similar tasks that require a high degree of parallelism.
As a rule of thumb, if your algorithm accepts vectorized data, the job is probably well-suited for GPU computing.
Architecturally, GPU’s internal memory has a wide interface with a point-to-point connection which accelerates memory throughput and increases the amount of data the GPU can work with in a given moment. It is designed to rapidly manipulate huge chunks of data all at once.
When to use GPU vs CPU?
CPU and GPU are often used together for optimal performance. However, there are specific use cases where one or the other may need to be prioritized or more powerful. GPUs are best for workloads requiring high parallelization, like computer graphics, machine learning, or crypto mining. On the other hand, CPUs are good for tasks like text editing, web browsing, emails, and programming - workloads that don't rely on graphics.
GPU vs CPU for gaming
A good gaming setup can enhance the gaming experience via better performance, higher-quality visuals, and immersion. Assuming you've decided to take your gaming setup from an outdated laptop to the next level, it's time to understand how the two crucial components, the CPU and GPU, play a role in gaming.
CPU and GPU perform different functions, and you need both for an optimal gaming experience. The importance varies based on the game type and the level of graphical details you need. You can technically get by with just a CPU with integrated graphics; however, the performance will likely be subpar compared to having a dedicated GPU.
CPU for gaming:
- CPU in gaming is for processing the computer’s instructions (the “brain” of the computer);
- Performs essential tasks - running the games’ logic, AI, simulations, and game mechanics;
- The CPU sends instructions to the GPU about what to render.
Note: Games with complex simulations or many non-graphical calculations (e.g., real-time strategy games, simulation games, etc.) may put more demand on the CPU. However, most modern games offload as much work as possible to the GPU.
GPU for gaming:
- Renders the visuals of the games;
- GPU takes instructions from the CPU and renders the images, textures, and effects seen on the screen;
- Handles calculations required specifically for rendering graphics;
- GPU determines the level of detail and resolution;
Note: Modern games are graphically intensive and put an increased demand on the GPU. That is why games with high-quality graphics, 3D environments, and visual effects like shadows and reflections require a powerful GPU.
Both are necessary when comparing CPU and GPU performance for gaming, but for most modem games, a powerful GPU is more important, paired with a CPU good enough to keep up with it.
CPU vs GPU rendering (computer graphics)
Rendering is the process of generating an image (or animation) from a 3D model or scene data by simulating how light interacts with objects. The two main things used for rendering are CPU and GPU.
CPU and GPU rendering each has advantages and disadvantages, and which is more important comes down to the specific rendering task. Complex scenes with a lot of logic favor CPUs, while simple scenes with lots of polygons and real-time rendering favor GPUs. For example, utilizing both the CPU and GPU provides the most optimal performance for gaming.
- The CPU does rendering;
- CPU rendering uses standard CPU processing cores;
- It tends to be slower than GPU rendering;
- CPUs are better at rendering complex scenes requiring a lot of logic and calculations;
- Better for rendering individual frames or animations and is less optimized for graphics tasks;
- More flexible and easier to program.
- The GPU does rendering;
- It is harder to program;
- GPU rendering is typically faster than CPU rendering;
- It tends to be less flexible than CPU rendering;
- Has lower latency;
- GPU rendering is best for and highly optimized for graphics tasks.
GPU vs CPU machine learning
For most machine learning workloads, both GPU and CPU together are ideal to maximize performance. The CPU performs tasks that require sequential processing, such as data cleaning, feature engineering, normalization, etc., on raw datasets before training models. Once this data is pre-processed, the CPU sends it to the GPU for parallel training/inference. After which, the GPU accelerates parallelizable math operations during training. Hence, both are necessary for high-performance machine learning.
CPU machine learning:
- Easier programming;
- More flexibility for non-ML tasks;
- Data preprocessing and transformations;
- Code execution - the CPU executes all the code for model defining, data preprocessing, training loops, evaluation metrics, etc.;
- Hosting - most leading deep learning frameworks like TensorFlow, PyTorch, and Keras are hosted primarily on the - CPU for tasks like initializing models and passing data between CPU & GPU;
- IO operations are more efficient on CPUs vs GPUs.
GPU machine learning:
- More power efficient than CPUs;
- GPUs have parallel processing capabilities, which makes them well-suited for the matrix operations common in ML algorithms, a great advantage compared to CPUs;
- Deep learning algorithms that involve large neural networks are the primary application area for GPUs;
- CUDA and OpenCL frameworks allow machine-learning code to use GPUs;
- GPUs are inefficient for serialized CPU tasks like I/O, preprocessing, post processing, and evaluation metrics.
- GPU memory might not be enough for larger datasets, requiring multi-GPU setups or CPU+GPU;
- Cloud GPU-accelerated servers on AWS, GCP, Azure, or Cherry Servers provide customizable and scalable GPU power for training large models without upfront hardware costs.
Note: GPUs are generally a better fit than CPUs for machine learning due to parallel processing capabilities, fast graphics memory, and better power efficiency, but GPU and CPU together are ideal to maximize performance.
CPU vs GPU mining
Crypto mining is a process of verifying transactions and adding new blocks to the blockchain using specialized computer hardware. Miners - people who perform complex cryptographic hash calculations - validate a block of transactions. This process uses GPU or CPU hardware to perform these calculations. People who solve these calculations (miners) are rewarded with crypto tokens.
Even though small-scale hobbyists might still use a CPU to mine cryptocurrencies, CPU mining is not considered efficient or profitable compared to GPUs or ASICs (application-specific integrated circuits) because it is not designed for deep parallel hashing that modern crypto mining algorithms demand. Therefore, overall, GPU mining is preferred over CPU mining.
- CPU mining has a lower hash rate;
- Lower electricity costs;
- Lower hardware costs;
- CPU mining is less scalable compared to GPU mining;
- CPU mining produces less heat and noise;
- CPU mining is now only used mainly by small-scale miners.
- GPU mining has got a much higher hash rate;
- However, also higher electricity and hardware costs;
- GPU mining tends to produce more heat and noise;
- More efficient than CPU mining;
- GPU mining is more scalable.
Note: GPUs have thousands of processing cores that can perform hash calculations in parallel, making them more efficient than CPUs for mining.
GPU vs CPU performance comparison
The CPU and GPU performance and limitations boils down to the exact use case scenario. In some cases, a CPU will be sufficient, while other applications may benefit from a GPU accelerator.
Let’s now uncover some general weak spots of CPU and GPU processors to help you decide on whether you need both of them or not.
CPU performance limitations
Heavyweight Instruction Sets
A tendency to embed increasingly complex instructions into CPU hardware directly is a modern trend that has its downside.
In order to execute some of the more difficult instructions, a CPU will sometimes need to spin through hundreds of clock cycles. Although Intel uses instruction pipelines with instruction-level parallelism to mitigate this limitation, it is becoming an overhead to overall CPU performance.
Context Switch Latency
Context switch latency is time needed for a CPU core to switch between threads. Switching between tasks is quite slow, because your CPU has to store registers and state variables, flush cache memory and do other types of clean up activities.
Though modern CPU processors try to facilitate this issue with task state segments which lower multi-task latency, context switching is still an expensive procedure.
The notion that the number of transistors per square inch on an integrated circuit doubles every two years may be coming to an end. There is a limit to how many transistors you can fit on a piece of silicon, and you just cannot outsmart Physics.
Rather, engineers have been trying to increase computing efficiency with the help of distributed computing, as well experimenting with quantum computers and even trying to find a silicon replacement for CPU manufacturing.
GPU performance limitations
Less Powerful Cores
Although GPUs have many more cores, they are less powerful than their CPU counterparts in terms of clock speed. GPU cores also have less diverse, but more specialized instruction sets. This is not necessarily a bad thing, since GPUs are very efficient for a small set of specific tasks.
GPUs are also limited by the maximum amount of memory they can have. Although GPU processors can move a greater amount of information in a given moment than CPUs, GPU memory access has much higher latency.
The most popular GPU APIs are OpenCL and CUDA. Unfortunately, they are both renowned for being hard to debug.
Although OpenCL is open source, it is quite slow on Nvidia hardware. CUDA, on the other hand, is a proprietary Nvidia API and is optimized for Nvidia GPUs, but it also automatically locks you in their hardware ecosystem.
How to increase GPU performance?
There’s always a bottleneck somewhere in your system. Using a GPU accelerator can increase GPU performance for certain workloads compared to just using the GPU that's integrated into a regular desktop/laptop.
Do you need a GPU accelerator?
Whether or not you need a GPU accelerator is always related to the specifics of the problem you are trying to solve. Both CPU and GPU have different areas of excellence, and knowing their limitations will leave you better off when it comes to choosing optimal hardware for your project.
Here at Cherry Servers we provide dedicated GPU Servers with modern Intel or AMD hardware and high-end Nvidia GPU accelerators. If you are wondering, what would be an ideal server for your specific use case, our technical engineers are eager to consult you 24/7 via Live Chat.