The Evolution of the GPU: How It Became the Heart of AI and ML

‍Reading this article, you will get the sense that the graphics processing unit (GPU) is a pretty important piece of computer hardware. It turns out GPUs are at the forefront of artificial intelligence (AI) and machine learning (ML) as well.

It has been around for years, but only recently have these chips revved up to perform new types of tasks.

Now they’re a crucial cog in AI and ML algorithms.

GPUs are uniquely suited to handle the massive datasets and complex computations involved in training AI algorithms.

GPU has also emerged as a standard piece of hardware for developers looking to build these capabilities into their applications.

This article explains how GPU cloud servers have grown from primarily used for gaming to becoming essential for any application requiring fast parallel processing.

This blog highlights GPUs’ evolution, how they are different from previous generations, why GPUs are so effective for AI and ML applications, and the next era of GPU innovation that has made it indispensable now.

Continue reading…

What is a GPU?

GPU is a specialized computer designed to rapidly transform and interact with large amounts of data. GPUs support a wide range of computing applications, from graphics and image editing to artificial intelligence and high-performance computing applications.

GPU cloud servers are used in embedded systems, such as autonomous vehicles, mobile phones, and robots.

It was first introduced in the early 1990s as a way to improve the graphics of video games. Today, GPU cloud servers are used for a wide range of computationally intensive tasks to increase the performance of hefty parallel systems, including computer clusters, artificial intelligence systems, and deep learning.

In fact, GPUs have become so essential to these fields that they are often referred to as the “heart of AI and machine learning.”

GPUs are used in various applications, from small embedded systems to large cloud servers. They are also used in a variety of industries, from automotive to healthcare.

As per reports, the GPU market exceeded USD 23 billion in 2021 and will cross USD 130 billion mark by 2027. The fast deployment across industries is contributing to the increasing expansion of this market.

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Also Read: Why GPU for Deep Learning?

Early Days of the GPU

The story of how GPU cloud servers became mainstream can be traced back almost 60 years ago.

In 1968, computer graphics were just in their infancy. Most graphics were used to display charts and simple images on a monitor. Researchers began exploring the idea of speeding up the graphics card.

FYI, a graphics card is a special computer chip that generates the images on the screen when you play a video game or watch a movie. A graphics card used in a machine or a computer in the 1970s differed from the computer graphics cards used today.

Today, GPUs are well-suited for deep learning tasks because they can perform a high number of floating-point operations per second (FLOPS). This allows them to train deep neural networks quickly and accurately.

In addition, GPU cloud servers can be combined to form powerful cloud servers that can handle large-scale computing tasks.

The evolution of the GPU is an important story to know because it illustrates how advances in technology can lead to new and unexpected applications. GPUs are now at the heart of AI.

The GPU has come a long way from its humble roots as the graphics card in your computer! However, its evolution hasn’t been linear–it wasn’t the evolution of processors that propelled this technology forward.

Also Read: How to Find Best GPU for Deep Learning

How was the GPU Developed?

In the 1980s, researchers began exploring ways to make computer graphics faster by using a parallel computer architecture.

Parallelism is the simultaneous execution of two or more parts of a program. This approach, combined several smaller computers with large memory banks and high-speed processing to create one super-fast computer or a supercomputer.

That first generation of graphics cards operated in a parallel fashion. But it was parallelism that was limited to just within the graphics card, as opposed to what would come next.

The supercomputer approach was used to build extremely powerful machines that were usually reserved for government agencies and large corporations. These machines were used in fields like weather forecasting and scientific research.

But the supercomputers were too expensive and complex for the average person to use. Computer designers wanted to find a way to put the parallel supercomputer architecture into a smaller, less expensive, less powerful computer that a wider range of people could use.

Also Read: The New Wave of Cloud GPUs

How is Today’s GPU Different from Previous Generations?

The graphics card of today is built upon a single chip that’s crammed with hundreds (or even thousands) of GPUs. These GPUs are designed to execute many programmed instructions at the same time. They can be programmed to perform various computer graphics and general-purpose tasks quickly.

Honestly, they are the key to unlocking the power of technologies and are essential for driving innovation in these fields.

GPU’s parallel architecture

GPU’s parallel architecture makes it a powerful tool for solving computationally demanding problems. A GPU is designed for executing many instructions at the same time.

Each GPU contains many small computing units that perform a very simple operation on the pixels of a picture, such as adding two numbers together or reading a pixel from a memory location and writing it to another.

This parallel architecture makes the GPU an ideal tool for solving many of the challenges that scientists, engineers, and programmers face on a regular basis.

What is a General Purpose GPU?

Some people refer to GPUs as general purpose GPUs (GPGPUs). The term GPGPU refers to a computer architecture where the graphics processing unit is used for non-graphical tasks.

The GPGPU architecture is a massively parallel computer architecture optimized for graphics processing and visualization. Because GPUs were designed to solve many simple computations at the same time, they are extremely good at performing the type of parallel computation needed for some scientific applications.

The “general purpose” in the GPGPU definition comes from the OpenCL programming language. OpenCL provides a standardized way for programmers to write programs that run on many different types of parallel computers. OpenCL is often used to write programs that run on GPUs.

Why Are GPUs so Effective for AI and ML Applications?

GPUs are incredibly good at doing lots of simple computations simultaneously. This is the perfect skill set for performing large numbers of computations when training a neural network or performing a convolutional neural network (CNN) analysis.

For clarity, it is important to define a few terms upon which GPU cloud servers rest:

  • Machine learning (ML)
  • Artificial intelligence (AI)

ML: In simplistic terms, machine learning refers to a system’s ability to automatically detect patterns in data it has never seen before without human input.

This allows robots and other devices to talk to each other, sense their surroundings and make decisions much like humans would do.

AI: Artificial Intelligence is about creating intelligent machines based on computer software systems that exhibit intelligent behavior or decision-making capability by completing complicated tasks.

CNNs and other algorithms rely on massively parallel computation to train quickly and efficiently. The ability to squeeze more work into the same amount of time makes GPUs ideal, especially for training deep neural networks.

How GPU Became the Brain that Drives AI and ML?

Because GPU cloud servers are able to handle large amounts of data, it makes them ideal for working with big data sets.

Thus, GPUs have become so important to AI and ML  that many companies are now designing their own custom GPUs. This allows them to optimize their hardware for these specific workloads and gives them a competitive advantage.

The future of AI and ML is exhilarating, and GPUs are driving it. They are the key to unlocking the power of these technologies, and will continue to be essential for driving innovation in these fields.

The rapid development of the GPU can be traced to one critical moment in the late 1990s.

This parallel computer architecture was a GPU. The graphics card company that developed the first GPU made it a standard feature of their product line.

This was a massive step for the graphics card industry.

The graphics card industry had traditionally been driven by gamers and other people who were interested in using computer graphics for entertainment purposes. The inclusion of the parallel GPU as an industry standard changed the focus from entertainment to parallel processing.

This shift led to the adoption of GPUs by the scientific community.

Parallel processors are incredibly good at performing the type of computations used in scientific research. This inclusion in the graphics card industry also opened the door for the GPU to be used in AI research.

Who Develops GPUs Today?

Graphics cards are used in a number of industries to speed up computations and make visualizations easier.

As per a report in Statista, Intel occupied 60% of the market share of PC GPU for the first quarter of 2022.

GPU cloud servers have evolved from being used only in computer gaming to now being a part of every niche business, from weather forecasting, and stock analysis.

There are even machines that put GPUs to work in the cloud. The growing demand for GPUs to perform complex computations has led to a surge in GPU computing. It’s also led to a surge in the number of companies that build GPUs.

GPU manufacturers are now on par with traditional computer hardware companies like Intel.

The rise of AI and ML has been a boon for GPU manufacturers. These new types of computing are incredibly demanding on hardware. They require a lot of processing power and more memory than traditional types of computing.

The need for this type of hardware has driven demand for GPUs. The industry for GPUs has overgrown, and the race for computer engineers who specialize in building GPUs is now fierce.

Where Are GPUs Used Today?

One of the unique aspects of GPU cloud servers is that they can be used for a wide variety of different tasks.

For example, GPUs are ideal for solving optimization problems and identifying solutions to large-scale problems. The designs of some of the most advanced AI and ML systems today were originally developed with GPUs as the main computing platform.

They are used for data analytics, scientific computing, and machine learning. In particular, GPUs have become essential for deep learning due to their high performance and efficiency. This demand for lower-code analysis, optimization, and execution has grown with AI and ML.

AI Challenges that Will Drive the Next Era of GPU Innovation

Learning from past performance, AI has begun to extend beyond the confines of static data and application-specific training.

Continuous evolving

With the ability to create synthetic datasets to test new ideas and continuously evolve its models, AI can quickly become incredibly efficient at solving complex problems.

As a result, AI-based decision-making can be expected to learn from past performance and make improvements in the future only as a by-product of using this new and more efficient methodology.

Efficiency in the short term

But it is to be noted that while AI is efficient in the short term, the amount of AI that can be used at any given moment will be limited. At the same time, it is important to remember that AI is a rapidly growing field with a wide range of applications.

Greater adaptability to change

As new algorithms are created and tested, current models are susceptible to change. This type of change is difficult for AI to sustain and can drive it out of business.

At the same time, you must acknowledge that AI is not a static field. It is constantly adapting to new challenges and trying to meet them head-on.

This means that the field can be expected to grow at a healthy pace. It all comes back to the economics of AI and how it is managed.

As the adoption of AI and ML grows, you can expect the same growth in AI prices that have been observed in robotics and artificial intelligence.

While AI may not be able to match the price of current AI gadgets, it can easily catch up soon.

The Future of GPUs in ML and AI

You have seen that AI and ML are now being used to solve complicated problems, such as learning from past performance, identifying solutions to large-scale problems, and performing optimization.

However, what about AI that does not use advanced learning or algorithm development?

What about AI that does not change too quickly?

How are GPUs used in AI and ML applications?

It should be noted that data generated by AI and ML applications is not intended to be treated as if it were data from a human source.

This is because AI and ML are becoming more and more data-driven. With this, there is no longer any place for static data.

With AI and ML data, there is now a growing need to store and act upon data very quickly.

One way to tackle this is to partner with respected hardware manufacturers like NVIDIA, Intel, and AMD to create GPU cloud servers with long-term availability.

This means that the best solution will be able to meet the needs of the customer at a moment’s notice. Customers can wait for the latest products to arrive and have them ready when they are needed.

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Many researchers regarded GPU as the “golden child” of computing, and rightly so. As computing uses expanded, AI and ML broadened their use cases as well.

You can see that the field of AI and ML is still in its infancy. Though modernization is at its optimum, at the same time, new tasks and technologies are constantly being proposed.

It is very unlikely that you will see GPU systems that are completely reliant on functionality provided by AI or ML.

However, rather than being content with the state of affairs, you can take some hope for the future by reaping the benefits of AI and ML in your systems.

We hope this article has given you a better understanding of the evolution of GPU cloud servers and how it has become the heart of AI and ML.

If you have any questions, please reach out to us.

About Nolan Foster

With 20+ years of expertise in building cloud-native services and security solutions, Nolan Foster spearheads Public Cloud and Managed Security Services at Ace Cloud Hosting. He is well versed in the dynamic trends of cloud computing and cybersecurity.
Foster offers expert consultations for empowering cloud infrastructure with customized solutions and comprehensive managed security.

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