As they became more sophisticated, video games started to require more than a central processing unit (CPU) to render their graphics and run real-time simulations. Hardware developers realized this and developed graphics processing units (GPUs) to assist with these tasks.
Although they initially designed these pieces of hardware for gaming and graphics rendering, they have become much more than that in recent years. Specifically, they have become artificial intelligence and machine learning powerhouses and the backbone of an entire industry.
Graphics and Parallel Processing
To understand why GPUs have become crucial in machine learning and related tasks, we must first understand parallel processing.
The best way is to imagine a task with 100 smaller sub-tasks. If one person handles all these tasks, that could take a very long time depending on their requirements and complexity.
However, with 100 people each working on a single task, the task will take a lot less time. This is the simplest form of parallel processing.
GPUs have 100 more cores than their CPU counterparts which typically have up to 64 cores and 128 threads. Granted, server-grade CPUs have more cores, but they are still fewer than those found in typical GPUs, especially specialized GPUs created for machine learning and related tasks.
CUDA and the Genesis of GPU Computing
NVIDIA is synonymous with GPUs in both the personal and enterprise space. The company launched CUDA in 2017, and this is thought to have sparked the rise of using GPUs in modern computing and processing.
NVIDIA created CUDA as a parallel computing platform that helped developers use GPUs for general-purpose processing.
At the time, the artificial intelligence community was clamoring for more computational power. The entry of CUDA into the market was exactly what they were looking for. The unique architecture of graphics cards with CUDA meant developers could run machine learning algorithms faster and more efficiently. This is one of the reasons for the sophistication and growth of the machine learning models we see today.
GPUs and Deep Learning
Deep learning and machine learning rely on highly sophisticated neural networks that must be trained on massive datasets. Such networks benefit from parallel processing, where GPUs outperform CPUs designed for sequential tasks.
The development of CUDA meant AI engineers and researchers could leverage the parallel computing capabilities of GPUs for deep learning tasks. Additionally, CUDA made CPU-accelerated computing more accessible and acceptable to a much wider audience.
NVIDIA also went further by developing frameworks and tools like RAPIDS and NVIDIA CUDA X AI that simplified deep learning development. This further reduced the need for in-depth hardware expertise among developers and engineers looking to get into deep learning and machine learning.
By doing the above, NVIDIA lowered the barrier of entry into machine and deep learning. This way, the company is directly credited with ensuring a new wave of innovations in the AI space and across industries that rely on these technologies.
GPU Developments in the Machine Learning Space
With NVIDIA seeing the massive demand for its GPUs with the growth of AI, machine learning, and related disciplines, the company has continued to evolve and improve its graphics cards. The most obvious improvement is the addition of GPU cores.
Today’s graphics cards, especially those used in machine learning and AI-related tasks, sport many more cores than those that came before them. Another improvement is in power consumption alongside an increase in computational power, i.e., efficiency per GPU unit.
With companies requiring hundreds of GPUs for some of the most intensive machine learning and scientific computing tasks, the power required can grow exponentially. So, how does NVIDIA help those who need such GPU clusters keep their energy consumption as low as possible? By making their GPUs more efficient.
Two very commonly used GPUs in the machine learning space are the NVIDIA H100 and the A100 models. Independent reports say the H100 is about twice as fast as the A100. This means that because it completes tasks twice as fast, it consumes less power for similar tasks.
That said, the H100 models are almost twice as expensive as their A100 counterparts, which is why the Gcore A100 GPU cloud solutions are still an excellent option for those who need the power while saving money on their computational tasks.
Real-life Applications and Breakthroughs
Understanding why GPUs are used in machine learning and how this came to be, it’s also vital to look at the different areas and industries where they are used.
One industry that has benefited massively from various AI breakthroughs is healthcare. Here, engineers are using machine learning and artificial intelligence for drug research and discovery, early cancer detection, protein synthesis, different types of healthcare mapping, and much more. For this reason, deep learning models powered by GPUs like the A100 and H100 have become an integral part of medical revolution and advancements.
Another industry benefiting from GPU developments and machine learning is finance. The finance industry analyzes a lot of data for different use cases every second. These can include stock market analysis, credit score analysis, stock market predictions, modeling opportunity growth, and much more.
Modern GPUs allow players in the industry to run advanced algorithms that can analyze this data, break it down, provide insights, and help with data-based decisions.
Advanced GPUs and machine learning are also used in the entertainment industry. There are numerous streaming services now, each with hundreds of thousands or millions of customers and thousands of shows.
Streaming platforms need a way to understand their customer’s preferences so they can recommend the shows and movies they should watch next. Machine learning powered by powerful GPUs has become crucial for ensuring customers can always find a show or movie that matches their mood.
GPUs have already come a long way from when they were only used for graphics rendering. They are now some of the most powerful computational units for companies, organizations, and individuals who need a lot of computing power. Current GPU technology is already great as it is, but the future is promising ups even more. Companies like NVIDIA and AMD are going all in on machine learning, and engineers are already developing more complex models that leverage this hardware.
With over a decade in the tech field, Bogdan blends technical expertise with insights on business innovation in technology.
A regular contributor to TMS Outsource's blog, where you'll find sharp analyses on software development, tech business strategies, and global tech dynamics.
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