The continuous development of AI will require the best hardware that can keep pace with the amount of processing systems are expected to do. Enter GPUs and GPU clusters. But what is GPU clustering exactly? Find out here.
Graphics processing unit (GPU) clustering is the process of putting a GPU on every node of a group of computers. Using multiple GPUs accelerates computing for resource-intensive tasks like image and video processing and training neural networks and other machine learning (ML) algorithms.
While GPUs were originally designed for rendering video game graphics, they have evolved to perform complex calculations faster than central processing units (CPUs), making them invaluable in fields that require massive parallel processing power, such as deep learning, scientific simulation, and big data analysis.
If your business relies on AI, then you should start searching for the best GPU for AI. Before making your choice, though, you may wish to learn more about GPU clustering first. Let’s begin.
How Does a GPU Cluster Work?
GPU clusters rely on parallel processing, much like artificial intelligence (AI) chips, and efficient data handling.
Parallel processing divides large computational tasks into smaller subtasks. Tasks are divided based on their nature and suitability for parallel processing. In a GPU cluster, each GPU processes assigned subtasks simultaneously. As a result, processing is significantly sped up.
Parallel processing results in efficient data handling. Since the data that requires processing is distributed across the GPUs in the cluster, each GPU has enough work to do without causing bottlenecks. And since each GPU has its own memory that stores the data it is currently processing, the cluster efficiently manages memory, maximizing the throughput and performance of each GPU. The cluster also has high-speed interconnects, facilitating fast data transfer, minimizing idle time, and ensuring all units are efficiently utilized.
Imagine if you have to render a complex three-dimensional (3D) scene. To hasten the process, divide the scene into smaller parts or frames. Make each GPU render a different part of the scene simultaneously. After rendering each scene, combine the parts to form the final scene. All this happens in record time.
What Are the Types of GPU Clusters?
GPU clusters come in two major types, each discussed in more detail below.
What Is a Heterogeneous GPU Cluster?
This category uses hardware from major independent or AI hardware vendors. In some cases, it utilizes different models of the same GPU brand.
What Is a Homogeneous GPU Cluster?
This category is applied to a GPU cluster where every single GPU is identical in terms of hardware class, brand, and model.
What Are the Benefits of GPU Clustering?
GPU clustering provides many benefits in fields like scientific research, ML and deep learning, oil and gas exploration, financial modeling, computer-aided design (CAD) and rendering, and healthcare and genomics. Take a look at some of them below.
Fast Processing Speed
GPU clusters handle larger datasets more efficiently, thus speeding up computing. It enables faster model training by automatically selecting the most optimal algorithms for each dataset. It also facilitates more precise results, even for live AI solutions.
Cost Reduction
CPUs can have as many as 24 cores, but GPUs can have hundreds to thousands. So, while CPUs are less expensive than GPUs, getting the same amount of processing power from CPUs may eventually end up costing more.
Energy Efficiency
Most CPUs consume as much as 100 watts of power, while GPUs can eat up to 300. But, as mentioned earlier, it may take at least four CPUs to work like a GPU. So, using CPUs actually uses more power.
Scalability
Users can quickly scale GPU clusters up by adding more GPUs or nodes if needed. And they can also be scaled down by doing the opposite.
Fast Product Development
GPU clusters shorten development cycles, enabling faster production and even experimentation and innovation during the research and development (R&D) phase.
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While GPU clusters offer immense computational power, it also comes with challenges. But the future remains bright as we see more advancements in hardware design, interconnect technologies, and software.
