Artificial Intelligence (AI) has made significant strides in the past few years with the advancements in Deep Learning (DL) and the advent of Large Language Models (LLMs). Many powerful applications have been developed that are capable of processing enormous amounts of data. Although these innovations speed up and optimize many aspects of our work, they necessitate the use of computational powerhouses to harness their full potential. GPU Clusters are one such method by which we can fuel these memory-intensive applications and facilitate parallel processing to enable the execution of complex algorithms.
What are GPU Clusters?
A GPU Cluster is a group of computers with each node equipped with a Graphics Processing Unit (GPU), a specialized hardware for parallel computation of complex calculations. For the applications mentioned above, multiple GPUs in a cluster provide the much-needed accelerated computational power for tasks like image and video processing or training large-parameter neural networks.
GPU Clusters are based on the principle of parallel processing and efficient data handling. Large computational tasks are broken down into smaller sub-parts, and each GPU in the cluster processes its assigned task simultaneously, significantly speeding up the processing. Moreover, the data to be processed is distributed efficiently to ensure that there are no bottlenecks. Each node in the cluster has its own memory that stores the information it is processing. Additionally, data transfer in the cluster is managed through its high-speed interconnects, which ensures that all units are being used efficiently, thereby minimizing idle time.
Components of a GPU Cluster
A GPU Cluster has two main categories of components – hardware and software. Hardware components can be further divided into two types, namely homogenous and heterogenous, having identical hardware and hardware from different hardware vendors, respectively.
A GPU is the main component of the cluster that powers it. It is based on parallel computing and is used for tasks like machine learning, scientific simulations, etc. Apart from a GPU, a cluster also consists of a CPU to handle tasks not optimized for parallel processing. Additionally, networking hardware such as NICs and switches act as the mode of communication between the different nodes and for connecting the cluster to external networks.
The whole operation is run with the help of Power Supply Units (PSUs) that ensure stable power supply. Lastly, since the GPUs and CPUs generate significant heat, cooling systems are essential for maintaining the system’s operational integrity by ensuring long-term reliability.
In terms of software components, an operating system (generally Linux) is the most basic part that manages all the hardware resources and also provides an environment for running other software. Along with that, GPU drivers allow the operating system to make use of the GPU effectively. Parallel computing platforms like CUDA and OpenCL provide libraries for developers to utilize the GPUs for parallel processing. Lastly, cluster management and security software help manage and monitor the hardware systems and ensure the cluster’s protection, respectively.
Use Cases of GPU Clusters
As mentioned earlier, GPU Clusters are required when dealing with memory-intensive tasks like Computer Vision and Natural Language Processing. As such, it finds its application in fields like
- Computational Fluid Dynamics,
- Molecular Dynamics,
- Weather Modeling,
- Pharmaceutical Research,
- Drug Discovery,
- Algorithmic Trading, and many others.
Some of its real-world use cases are listed below.
Large Hadron Collider (LHC)
The LHC at CERN is one of the most powerful particle accelerator to have ever been built. It produces a significant amount of data that is generated because of the particle collisions, and GPU Clusters are used to analyze the same. It helps the researchers accelerate their code and better explore the high-energy frontier.
Weather Forecasting at NOAA
GPU Clusters are used to model climate and weather conditions at the National Oceanic and Atmospheric Administration (NOAA). They help in rapidly processing large amounts of data and allow for more accurate predictions of severe weather events.
Google Brain Project
Google Brain Project is primarily based on deep learning and AI research and is powered by GPU Clusters. Since training and inference of complex neural networks like that of image and speech recognition applications require significant memory resources, these clusters speed up the process and enhance the capabilities of Google’s services like Google Photos and Assistant.
One of the biggest issues associated with GPU Clusters is their cost, requiring high upfront investment along with maintenance, operational, and upgrade costs. Some other constraints include the physical security of hardware components against theft, tampering, or damage. Additionally, for fields like healthcare and finance, it becomes essential to ensure the confidentiality of the data being processed.
Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.