Graphics processing units (GPUs) were initially conceived to accelerate 3D graphic rendering in video games. However, more recently, they have become popular in artificial intelligence and machine learning (ML) contexts. In fact, GPUs are critical components of AI Supercomputers, like Azure, which are powering up the current AI revolution.
GPUs are specialized processing cores that accelerate computational processes. Initially designed to process the images and visual data from video games, they have now been adapted to enhance the computational processes inherent to AI.
GPUs are effective in AI because they use parallel computing to break down a complex problem into smaller, simultaneous calculations. These calculations are distributed among a vast number of processor cores and are well-suited to machine learning and big analytics. Engineers sometimes refer to this type of computing as General Purpose GPU or “GPGPU”.
As the use of GPUs continues to evolve, research firm JPR predicts the GPU market will grow to reach a total of 3,318 million units by 2025. This represents a compound annual growth rate (CAGR) of 3.8%.
The benefits of GPUs for deep learning
Why would an engineer not choose a fast, powerful CPU (central processing unit) over a GPU to support AI and ML operations? The answer can be found when one considers how each works:
- Since CPUs handle most of the tasks for a computer, they need to be fast and versatile. They must also be able to switch between multiple tasks rapidly to support the computer’s general operations.
- GPUs, on the other hand, were created to render images and graphics from scratch. This task does not require much context switching and, as we mentioned earlier, relies on breaking complex tasks into smaller subtasks.
While the power of a CPU doubles theoretically every two years, GPUs instead focus their resources on a specific problem. This parallel computing strategy is known as the Single Instruction, Multiple Data (SIMD) architecture and enables engineers to efficiently distribute tasks and workloads across GPU cores.
In essence, GPUs are a more suitable choice since ML requires the continuous input of vast amounts of data to train models. The more data that is incorporated, the better such models can learn. This is particularly relevant in deep learning and neural networks where parallel computing is used to support complex, multi-step processes.
GPU examples for deep learning
Well-known GPU manufacturers such as AMD and Intel are present in the industry, but Nvidia is by far the dominant player.
Nvidia is a popular choice because its libraries (known as the CUDA toolkit) enable users to easily set up deep learning processes and access a dedicated ML community. The company also provides libraries for popular frameworks such as TensorFlow and PyTorch.
Some popular Nvidia GPUs for AI and ML include:
- Nvidia Titan RTX – powered by Nvidia’s Turing architecture, the Titan RTX is one of the best GPUs for “entry-level” neural network applications.
- Nvidia A100 – powered by Nvidia’s Ampere architecture, the A100 Tensor Core GPU offers unmatched acceleration at scale to power data centers in high-performance computing (HPC), AI, and data analytics. The latest version of the A100 offers 80GB of memory and the world’s fastest memory bandwidth of over 2 terabytes per second.
- DGX A100 – an enterprise-grade solution designed specifically for ML and deep learning operations. The DGX A100 offers two 64-core AMD CPUs in addition to 8 A100 GPUs for ML training, inference, and analytics. Multiple units can be combined to create a supercluster.
- Graphics processing units (GPUs) were initially conceived to accelerate 3D graphic rendering in video games. However, in more recent times, they have become popular in artificial intelligence contexts.
- GPUs are effective in AI because they use parallel computing to break down a complex problem into smaller, simultaneous calculations.
- Nvidia is not only the only GPU manufacturer, but it is the dominant player. These GPUs are a popular choice because the company’s CUDA toolkit enables users to easily set up deep learning processes and access a dedicated ML community.
GPU Advancements in AI
- Introduction: GPUs initially designed for video games, now widely used in AI and ML.
- Parallel Computing: GPUs leverage parallel computing for faster processing.
- Optimized for ML: Ideal for machine learning tasks due to their parallel architecture.
Nvidia’s Dominance in AI GPUs
- Nvidia’s Leadership: Nvidia is the primary player in the AI GPU market.
- CUDA Toolkit: Nvidia’s CUDA toolkit simplifies deep learning setup.
- Framework Support: Provides libraries for popular frameworks like TensorFlow and PyTorch.
Advantages of GPUs in Deep Learning
- Efficient Processing: GPUs focus on specific tasks, enabling efficient data processing.
- SIMD Architecture: Utilizes Single Instruction, Multiple Data for parallel computations.
- Continuous Input: Well-suited for ML’s need for continuous data input.
Popular Nvidia GPUs for AI/ML
- Nvidia Titan RTX: Excellent choice for entry-level neural networks.
- Nvidia A100: Offers unmatched acceleration for data centers and AI.
- DGX A100: Designed for ML and deep learning operations; scalable supercluster solution.
Growth of GPU Market in AI
- Market Prediction: GPU market to reach 3,318 million units by 2025.
- CAGR: Compound annual growth rate projected at 3.8%.
GPU Impact on AI Advancements
- AI Revolution: GPUs powering the current AI revolution.
- Real-time Performance: Enables real-time AI processing.
- Wide Applications: Used across industries for various AI applications.
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