If you've ever delved into the intricacies of PC building or taken your first steps in an introductory Computer Science class, chances are you've encountered the ubiquitous term – GPU. For many gaming enthusiasts, myself included, GPUs are the magic component that gives you more frames in your favorite FPS game, while CPUs are the component where our code finds its execution space. However, GPUs have evolved from their humble beginnings of graphics processing, and GPUs do a lot more than just run code.
In this blog, we'll explore the evolving strengths of each component, discuss how they complement each other, and wrap up with some emerging use cases for each of them.
An Introduction to CPUs
The Evolution and Function of CPUs
Short for the central processing unit, the CPU is an essential component of a computer, enabling it to execute instructions and store data. The Intel 4004 is widely recognized as the first commercially available CPU. It was a 4-bit processor with 640 bytes of RAM, a far cry from the 32 and 64-bit architectures we have today. The number of bits a CPU can process dictates the size and complexity of calculations it can handle.
The CPU runs programs by fetching instructions from RAM, decoding them to determine the required operation, and then executing them. This fetch-decode-execute cycle repeats continuously, processing billions of instructions per second in a modern chip. For example, the CPU might fetch an instruction to add two numbers, decode that this requires retrieving those values from registers and applying the addition operation, and then, in the execute stage, add them and place the result in another register. Registers are small locations in memory that the CPU uses to load and store data.
As computational demand grew exponentially across applications like multimedia, 3D graphics, and scientific computing, a single processing unit could only handle so many instructions per second. Whereas early CPUs contained one processing unit, a multi-core CPU packs two or more complete processors, referred to as “cores.” These cores can operate independently, enabling a processor to execute multiple instructions simultaneously.
Fast forward to the present day, and modern cloud native tools like Kubernetes have done an excellent job of abstracting infrastructure complexities, including resource allocation. However, Kubernetes still provides granular controls for fine-tuning resource allocation through CPU requests and limits. This is powerful for ensuring optimal resource management and keeping costs down.
Advanced Features and Real-world Applications
CPU improvements have allowed IoT and edge devices to evolve beyond simple data collection roles. With added processing power, smart home hubs can now analyze usage patterns to optimize energy efficiency. Industrial IoT sensors can run real-time analytics to spot anomalies and trigger preventative maintenance. Wearables monitor bio-metrics and provide actionable health insights. More advanced CPU architectures even allow some edge devices to run machine learning inferences locally, enabling real-time anomaly detection without cloud connectivity.
On the mobile device front, annual CPU advancements have transformed pocket-sized smartphones and tablets into remarkably powerful productivity hubs. Apple’s latest iPhone sports up to a 6-core CPU based on the A17 chipset with over 15 billion transistors. Combined with an advanced GPU and neural engine, this silicon enables console-quality gaming, professional-grade photo and video editing.
Other integral CPU features working behind the scenes include:
| Feature | Description | Application |
|---|---|---|
| Multi-threading | Divides program execution into independent threads for concurrent processing. | Improves responsiveness in web servers handling multiple client requests simultaneously. |
| Cache | Fast memory buffer storing frequently accessed data for quicker retrieval. | Increases performance by minimizing access time to slower RAM. |
| CPU Requests (Kubernetes) | Pods reserve necessary CPU resources based on profiled needs. | Prevents resource starvation and ensures smooth performance in microservices architectures. |
An Introduction to GPUs
The Evolution and Function of GPUs
Originally created for rendering graphics and accelerating video processing, the graphics processing unit (GPU) was developed to offload the substantial number of operations needed for real-time 3D graphics from the CPU. NVIDIA is recognized for creating the first commercially available GPU with the release of the GeForce 256 in 1999.
The architecture of a GPU typically consists of numerous smaller cores, each capable of executing its own set of instructions in parallel. This method of processing enables GPUs to process vast amounts of data simultaneously. Much like multi-core CPUs, GPUs leverage multiple processing units, often referred to as "stream processors" or "CUDA cores". Each stream processor or CUDA core acts as a tiny execution unit that can carry out instructions parallel to one another.
Advanced Features and Real-world Applications
Gaming remains a key application for consumer graphics cards, with each generation of GPU further enhancing immersion and realism.
Modern deep learning fundamentally relies on GPU acceleration during training. The immense parallel processing power of GPUs allows researchers to experiment with ever-growing datasets and neural network sizes, advancing fields such as image recognition, natural language processing, recommendations, and more.
Scientific simulations and high-performance computing clusters dedicated to modeling phenomena rely on scale-out GPU servers. A notable example of this is how NASA uses NVIDIA GPUs to visualize landing on Mars.
On the day-to-day front, consumer laptops with integrated GPU designs handle everything from video streaming to creative workflows. Convenient access to accelerated encoding/decoding, and editing tools.
| GPU Applications | Description |
|---|---|
| Deep Learning | Training and inference in large language models and generative AI due to efficient handling of matrix operations. |
| Graphics Rendering | Processing and displaying complex 3D worlds in real-time through manipulation of numerous polygons. |
Key Differences Between CPU and GPU
While CPUs and GPUs both enable critical computing functions, their architectural approaches and ideal workloads differ significantly. In the following section, we will highlight some of the most crucial differences.
| Aspect | CPU | GPU |
|---|---|---|
| Processing Approach | Serial processing approach is optimized for quickly executing sequential tasks one after another. | Parallel architecture specializes in simultaneously carrying out repetitive operations across thousands of tiny cores. |
| Core Count | Lower core counts, ranging from just a few cores in mobile devices up to about 64 cores in high-end server chips. | Hundreds or even thousands of slimmed-down cores packed together on a die to support gigantic parallel workloads. |
| Main Tasks | Focuses on general-purpose computations like running application logic, operating systems, databases, and game physics. | Handles graphics rendering pipelines, matrix math for machine learning, simulations with many elements, and neural network training/inferencing for AI applications. |
| Efficiency at Complex, Repetitive Tasks | Lower efficiency in executing workloads consisting of repetitive computations like rendering pixels or running AI model inferences due to sequential processing. | Extremely high efficiency in handling repetitive tasks due to its parallel design. |
| Monitoring in Kubernetes environments | Defining precise resource requests, monitoring for contention, and scaling out rather than overprovisioning. | GPU requests guarantee device access. Monitoring metrics like CPU saturation, and GPU memory consumed. |
Complementary Functions of CPU and GPU
Modern computing systems utilize CPUs and GPUs working in tandem, applying their specialized capabilities to suitable segments of the current workload. The CPU handles general-purpose sequential logic like I/O, OS kernels, and overall program flow. A GPU accelerator provides parallel lifting capacity through its streamlined cores to rapidly process graphical, ML or scientific workloads.
In cloud and Kubernetes environments, striking the right balance between CPU and GPU resources allocated to applications can be critical for performance and cost optimization. If CPU capacity is insufficient, the GPUs cannot be properly utilized as they rely on the CPU for scheduling and data preparation. Conversely, overprovisioning expensive GPU resources without adequate CPU support squanders capabilities.
Many laptop processors integrate basic graphics functionality directly onto the CPU die itself for convenience and economy. For example, an AMD Ryzen 7700X CPU contains Radeon graphics sharing silicon space. While performant, integrated GPUs trade some acceleration for compact packaging. Dedicated, or discrete, GPUs like the NVIDIA RTX 4080 have separate on-board memory and cooling.
In a modern e-commerce application, the CPU processes the web application’s central logic and databases. Customer cart updates trigger a GPU-powered recommendation model to suggest related products. The GPU greatly expedites the math required to serve a recommendation, freeing CPU cycles for app functionality.
The Future of CPUs and GPUs
As software grows more ambitious across domains like generative AI, Augmented Reality, and Gaming, the demand for specialized hardware rises exponentially. CPUs must balance general-purpose capabilities while innovating on efficiency to meet ever-growing scale requirements.
The recent success of large language models like GPT-3 and ChatGPT is sparking an AI arms race. Increasing numbers of startups are now moving to offer proprietary conversational models. Giants like Shopify are providing AI assistants on their platforms, while on the other end, companies like Samsung are integrating AI natively in their latest flagship devices.
What is Civo doing for the future of GPUs?
In response to the impending demand for GPU-based compute resources, cloud providers like Civo are stepping up to offer these resources at competitive prices. Civo announced the release of high-performance cloud GPUs, specifically tailored for machine learning, scientific computing, and generative AI. These GPUs are designed to streamline projects from start to finish with just a few clicks, offering seamless integration into existing infrastructure with zero vendor lock-in. This ensures that you can focus on your projects without worrying about compatibility issues.
Additionally, Civo is going green with Deep Green GPUs. In partnership with Deep Green, Civo is offering an eco-friendly approach to cloud computing. These sustainable GPUs are run on systems powered entirely by renewable energy, with 90% of server heat being repurposed for community projects. This ensures zero carbon heat emission while maintaining cost-efficiency. This approach uses waste heat for both cloud services and community benefits, contributing positively to the environment.
If you want to get started with Civo’s cloud GPUs, check out these resources:
Summary
Understanding the capabilities and limitations of CPUs and GPUs is essential for building high-performance systems. While CPUs excel at general-purpose sequential tasks critical for program flow, GPUs provide massively parallel processing, optimized for workloads in graphics, AI, and scientific computing.
Choosing the right processor for your specific software needs and balancing implementations across devices is crucial. An application might utilize CPU power for logic and offload math-intensive tasks to GPU cores, which excel in such operations.
If you want to learn more about CPUs and GPUs, check out these resources:
