GPU Computing 101: Everything You Need to Know

GPU computing is an innovative paradigm in computing that harnesses the huge processing power of Graphics Processing Units (GPUs) for all kinds of tasks. GPU use cases go much beyond the traditional graphics rendering ones.

Initially, GPUs were designed for accelerating graphics rendering in gaming and multimedia apps, but have since then evolved into versatile parallel processors capable of handling complex computational workloads with efficiency. Compared to CPUs, which excel at sequential processing, GPUs excel at parallel processing, which enables them to execute thousands of tasks at the same time. This ability makes GPUs suitable for data-intensive use cases like machine learning, scientific simulations, financial modeling, deep learning, and more.

In this article, we will go through everything you need to know about GPU computing and GPU processing. Also, we’ll cover many GPU use cases, such as graphics rendering, deep learning & machine learning, and other GPU intensive tasks. Lastly, we’ll talk about using a dedicated server with a GPU accelerator. So, let’s get this show on the road!

What Are GPUs?

GPUs (Graphics Processing Units), also called graphics cards, are specialized electronic circuits designed to accelerate graphics rendering in gaming consoles, computers, and other devices. They consist of thousands of smaller processing cores that are optimized for parallel computation, allowing GPUs to process multiple tasks simultaneously.

GPUs are powerful enough to complete computationally intensive tasks. Some of those GPU intensive tasks include rendering high-resolution graphics, video processing, and scientific simulations. However, GPUs have a wide range of use cases, which we will cover in depth below. Before we do that, let’s learn what GPU computing and GPU processing are.

GPU Computing and GPU Processing

GPU computing, or General-Purpose computing on Graphics Processing Units (GPGPU), is the use of GPUs for performing non-graphics computation. In GPU computing, tasks that are traditionally executed on Central Processing Units (CPUs) are offloaded to the GPU. This allows for completing tasks like simulations, cryptography, data analytics, and machine learning.

This process relies on specialized programming frameworks, libraries, and languages like CUDA (Compute Unified Device Architecture) or OpenCL (Open Computing Language). It uses them to enable developers to write code that can be executed efficiently on GPUs. All in all, GPU computing has revolutionized high-performance computing by significantly reducing processing times and enabling researchers, developers, and scientists to tackle complex problems.

Now, onto GPU processing. Graphics Processing Unit (GPU) processing is the use of specialized hardware (GPUs) to perform different computational tasks. Similar to GPU computing, GPU processing speeds up computations, leading to improvements in performance and efficiency for a wide range of use cases.

GPU Use Cases

Now, let’s get through all the use cases GPU intensive tasks that you can complete thanks to GPU computing:

Graphics Rendering

Graphics rendering is the primary function for which GPUs were designed. GPUs are excellent for rendering graphics for video games, movies, virtual reality (VR), and augmented reality (AR) applications. They also efficiently handle tasks like rendering complex 3D scenes, applying textures and lighting effects, and simulating realistic physics.

Deep Learning & Machine Learning

GPUs are indispensable in the fields of deep learning & machine learning due to their ability to accelerate training and inference processes. For instance, deep learning algorithms, especially neural networks, involve heavy matrix operations that can be parallelized and executed efficiently on GPUs. As for machine learning, frameworks like TensorFlow, PlyTorch, and Keras provide GPU-accelerated implementations of different machine learning models, which enable researchers and developers to train models faster and at scale.

Data Analytics & Big Data Processing

Analyzing large datasets efficiently is essential in sectors like finance, marketing, healthcare, and scientific research. GPUs accelerate data processing tasks such as transformation, data cleaning, statistical analysis, and aggregation. Technologies like NVIDIA CUDA and cuDNN provide libraries and APIs that enable developers to use GPU acceleration for data analytics and big data processing tasks.

Scientific Computing

Many researchers and scientists use GPUs for scientific simulations and computations in fields like chemistry, physics, biology, and engineering. Also, simulations involving things like fluid dynamics, molecular dynamics, computational chemistry, climate modeling, and astrophysics benefit from GPU acceleration due to their parallel nature.

Computer Vision

Computer vision applications, which include things like detecting objects, classifying photos, recognizing faces, and self-driving cars, rely on processing large amounts of visual data in real-time. GPUs play a huge role here because they enable rapid inference for deep learning-based computer vision models, allowing for fast and accurate analysis of images and videos. This has applications in security surveillance, medical imaging, industrial automation, and self-driving vehicles like Teslas.

Cloud Computing and Virtualization

GPUs are essential for providing virtualized graphics and computing resources in cloud computing environments. Cloud service providers offer GPU instances that allow users to offload intensive computational tasks to remote servers equipped with powerful GPUs. This enables organizations to scale their computing resources dynamically and run complex apps without investing in more hardware.

Rendering and Visual Effects in Film and Animation

The film and animation industry uses GPU-accelerated rendering to create high-quality visual effects, animations, and computer-generated imagery (CGI). So, the cool effects we see in movies are all done thanks to GPUs. Professionals use GPUs to render complex scenes with realistic lighting, textures, and particle effects, all of which require massive computational power. Software suites like Autodesk Maya, Blender, and Adobe After Effects offer GPU-accelerated rendering options for animators and artists.

Virtual Reality (VR) and Gaming

GPUs have been at the core of the gaming industry for quite a while now. They power realistic graphics, immersive environments, and smooth gameplay experiences. With the rise of virtual reality and augmented reality technologies, GPUs play a crucial role in delivering immersive VR/AR experiences by rendering stereoscopic 3D graphics and tracking head and hand movements in real-time.

Designing Drugs and Medication

Since GPU computing is so powerful, many believe that it can lower the cost of discovering a new drug, which soars every year. Right now, NVIDIA is building Cambridge-1, which will be dedicated to AI research in drug design and healthcare. Cambridge-1 will also be the most powerful supercomputer in the United Kingdom.

Seismic Imaging

The oil and gas industry uses seismic imaging to discover the Earth’s subsurface structure and detect oil reservoirs. There’s a huge demand for additional computing power in this industry as the algorithms used in seismic data processing are evolving quickly, and GPUs can be used to accelerate the whole process.

Using a Dedicated Server With a GPU Accelerator

Using a dedicated server with a GPU accelerator offers several benefits for all kinds of tasks, especially those that require intense parallel processing. Here are some of them:

Accelerated Processing: The primary advantage of using a dedicated server with a GPU accelerator is the ability to offload computer-intensive tasks to the GPU, which can significantly speed up processing. Tasks like machine learning training, scientific simulations, and data analytics can see substantial performance gains when executed on GPUs compared to traditional CPU-only systems.
Parallel Processing Power: GPUs are designed with thousands of cores optimized for parallel processing. This makes them well-suited for tasks that can be divided into smaller, independent units of work, such as matrix operations in deep learning or simulations in scientific computing.
Improved Efficiency and Performance: GPU accelerators are very good at performing floating-point calculations, making them ideal for workloads that require intensive numerical computations. Also, GPU-accelerated servers can deliver higher performance per watt compared to CPU-only systems. This leads to improved energy efficiency and reduced costs.
Scalability: Dedicated servers with GPU accelerators can be scaled up and down to meet changing demands. By adding additional GPUs to your server infrastructure, you’ll be able to do all kinds of tasks, from training larger machine learning models to processing bigger datasets.
Handling Specialized Workloads: If you engage in tasks like rendering 3D graphics, running complex simulations, or training deep neural networks, you will benefit greatly from using a dedicated server with a GPU accelerator. By using such a server, you can optimize your infrastructure and achieve better performance and faster time-to-insight.
Advanced Visualization: GPU accelerators are essential for advanced visualization tasks, including rendering high-resolution images, processing large-scale datasets, and driving immersive virtual reality experiences. Whether you’re visualizing complex data in research, creating realistic graphics in video games, or designing architectural models, GPUs will give you the boost you need to deliver compelling visual experiences.

Conclusion

GPUs and GPU computing have revolutionized a wide range of industries, from gaming and movies to finance and healthcare and everything in between. They are robust and capable of handling complex tasks, and many believe that they will continue to improve different sectors in years to come. Also, GPUs can boost dedicated servers significantly and make them even more powerful. Using the two together can help you handle virtually anything you want at greater speeds and efficiency.

In this article, we went in-depth on all things GPUs and showed you their immense use cases in all kinds of fields. So, by doing that, we hope that we have helped you understand GPUs better than you did before. Happy computing!