The GPU, the graphics processing unit, has rapidly assumed as much significance as the CPU, or the central processing unit, occupying centerstage as a decision-maker for purchasing a new computer or a smart gadget.
A CPU is a silicon-based chip made from billions of transistors. It is the core computational unit to handle the operating system and general-purpose applications.
In contrast, a GPU is more specialized. It can perform mathematical operations while operating on multiple datasets in parallel. It is helpful for image processing, gaming, and artificial intelligence applications. This ability beats the serial processing of a CPU in speed and efficiency.
How Do CPUs and GPUs Work?
The fundamental operations of both these processing units are similar, involving these components:
Cores (single or multiple)
Cores are also called arithmetic logic units (ALU). The CPU and the GPU receive instructions from memory in bits. Then, they decode these instructions and execute the corresponding mathematical or logical operation. It is called an instruction cycle, and several million such calculations run every second.
Ongoing research improves the capabilities of cores. For instance, the 14th Gen. Intel Core processors comprise P-cores (Performance) and E-cores (Efficiency). They can have Intel Iris Xe graphics or use an Intel Arc GPU. The former uses the Xe-HPG architecture, or High Performance Graphics. Some modern NVIDIA GPUs have Tensor cores to speed up large-matrix operations.
Memory
Internal memory enhances the processing performance of the CPU and the GPU. This inbuilt memory (cache) can make data access faster. CPUs have three caches — L1, L2, and L3 — with decreasing speeds. A memory management unit controls the data movement between the cores, cache, and memory (RAM).
Control units
As the name suggests, this component syncs processing tasks. It controls the frequency of the electric pulse generated. Generally, a higher frequency generates better performance.
A CPU switches between different instruction sets quickly, delivering low latency. However, a GPU has high throughput. It can operate on a high volume of instructions at excellent speeds. It breaks down a complicated instruction into several smaller tasks and works on them consecutively. Imagine the ease it brings in rendering shadows, varied lighting, and transitions in contemporary gaming titles.
The way a GPU can divide tasks helps to accelerate performance at a time when Moore’s Law has run into limitations. (The generalization states that the number of transistors in an integrated circuit will double every two years.)
Primary Differences between CPU & GPU
| Feature | CPU | GPU |
| Functionality | General-purpose computing tasks | Specialized for parallel graphics and computation tasks |
| Architecture | Few powerful cores for sequential processing | Numerous smaller cores for parallel processing |
| Parallel Processing | Suited for concurrent tasks, less parallel efficiency | Highly efficient for parallel processing tasks |
| Task Types | Complex decision-making, system management, multitasking | Repetitive mathematical operations, parallelizable workloads |
| Cache Memory | Larger and faster cache for quick access to data | Emphasizes more cores over extensive cache |
| Energy Efficiency | Designed for lower power consumption, versatility | Power-hungry but achieves high computational throughput per watt |
Besides their mode of operation (serial vs parallel), these two components also have other significant differences.
Their origins are different: General vs ASIC
Graphics processors originated as Application-Specific Integrated Circuits or ASICs. Their original aim was to improve graphics rendering for more realistic gaming and multimedia pursuits. It contrasted with the more general computing of a CPU, like running the operating system and databases. With time, GPUs have evolved into areas like machine learning.
GPUs have more but less powerful cores
The design of a graphics processing unit encompasses many cores for the required computations. However, these cores are less powerful and have less memory than CPU ones.
GPU caters to specialized functions
A graphics processing unit is an excellent choice for deep learning applications that use patterns to collect insights and make predictions. Nowadays, some processors have neural networks. In the automotive world, advanced driver-assistance and autonomous vehicle systems (ADAS and AV) require GPUs for complex simulations. These units have become indispensable in high-performance computing for geoscientific applications, medicine, finance, and robotics.
Although NVIDIA is an established name in the GPU business, Intel offers several commendable offerings, like the Intel Data Center GPU for simulations and analytics.
GPUs offer many customization options
The release of NVIDIA CUDA, a platform for parallel processing, made GPU programming more accessible to developers. The rise of open-source systems like Kubernetes and cloud service providers has helped GPUs cater to diverse needs.
Combining a CPU & GPU: Discrete vs Integrated
While looking for a new computer, you must decide if you need to prioritize a CPU or a GPU. For instance, an NVIDIA RTX 40 Series graphics card can facilitate engaging gameplay with features like ray tracing that imitates the behavior of light in the real world.
However, investing in a discrete graphics card can be overkill if your usage needs don’t involve gaming, multimedia editing, or artificial intelligence.
In these cases, opting for integrated graphics can be more cost-efficient. Here, the GPU is built into the CPU, offering greater portability and energy efficiency — a popular combination for compact laptops.
A Future Ready Setup: CPU, GPU & NPU
As technology advances, users can opt for NPUs or neural processing units to assist with artificial intelligence tasks.
CPUs with inbuilt NPUs become AI-accelerated and adept at inferencing or formulating predictions.
It seems likely that a combo of CPU, NPU, and GPU will become more common in future systems to speed up deep learning and high-performance computing in general.