What is the GPU? From Concepts to Utilization Examples!
What is a GPU, a term that is widely mentioned and used today? Firstly, GPU is short for “Graphics Processing Unit.” This conceptual explanation may seem vague. Now, let’s specifically understand what the GPU is and explore the differences between CPU and GPU.
The commonly known Central Processing Unit (CPU) has a structure specialized in serial (sequential) processing methods that process data in the order in which commands are entered. GPUs, on the other hand, consist of thousands of cores, and process multiple commands simultaneously. Therefore, GPUs are computing resources optimized to efficiently handle large-scale data in parallel through more efficient cores compared to CPUs, taking advantage of their parallel processing capabilities.
To summarize, CPUs focus on quickly processing complex calculations sequentially, whereas GPUs are specialized in parallel processing of simple tasks in bulk. For example, displaying graphics on a screen requires processing large amounts of simple graphics data at the same time, making GPUs use more appropriate than CPUs. CPUs can calculate, of course, but they are less efficient.
A GPU server is an infrastructure that utilizes GPUs, which excel in high-volume computations, for AI, big data, and DX platforms. These servers perform parallel operations with thousands of cores, enabling fast computation processing. The main areas of computer processing include images, games, and AI, where image processing, rendering, or data analysis is typically the focus. Among these, for artificial intelligence, parallel processing tasks that handle simultaneous computations are essential. Specialized GPUs in parallel processing play a crucial role in operating artificial intelligence software effectively.
As shown in the illustration above, current GPU servers are being utilized in various services and industries such as log analysis, anomaly detection, and natural language processing, and are expected to be introduced in new fields as well. So how are these GPU servers used in practice? Here’s an example of GPU services in use.
GPUs are high-cost computing resources that are challenging to predict and plan accurately in terms of infrastructure demand. They consume up to 5 times more power compared to CPUs, and there are additional costs associated with network, space, operational, and maintenance expenses. Directly purchasing or renting GPUs to build a server can be a significant financial burden. Moreover, unlike CPUs, managing GPU resources for simultaneous use by multiple users requires pre-allocation and distribution processes, necessitating the development and operation of software solutions for effective management.
GPU-as-a-service (GPUaaS) or the GPU cloud was created to address these inconveniences and needs of users. The cloud can buy multiple GPUs as needed and use them simultaneously. Companies and developers can conveniently use GPU processing power without the need to leverage cloud technology to build advanced infrastructure.
### GPU Cloud Services
Colab provides a cloud-based Jupyter notebook environment that runs in the cloud without the need for separate configurations. It also features easy access to Python libraries, 50GB hard drive space, 12GB RAM, and free GPU offerings. This is an attractive offer for machine learning practitioners. However, even with the paid version, using Colab is not without limitations. According to Colab’s internal policies, users might not be allocated a GPU, and even if allocated, it’s uncertain how long the resource will be available. Additionally, there are limitations such as not being able to use the allocated resources continuously for more than 24 hours, regardless of how long you’ve been using them.
Paperspace is particularly renowned for its variety of high-performance GPU machines, specially designed for machine learning and deep learning applications. This company offers two main products: Core, which provides GPU-supported VMs on both Windows and Linux, and Gradient, which offers notebooks, workflows, and deployments specifically designed for machine learning users.
The advantages of Paperspace are its wide variety of instance types, simple UI, and free GPU options. However, in the free version, it’s not possible to create private projects, and since the data centers are located in the United States, there might be significant communication latency issues when used from within Korea, leading to a less smooth user experience.
Linode, acquired by Akamai in 2022, was once a pioneer of the cloud computing industry. When Linode was launched in 2003, the concepts of “cloud computing” and “cloud infrastructure” were just beginning to gain traction. Linode was one of the pioneering products at that time, making it incredibly easy to use servers in the cloud for application hosting. The biggest advantage of Linode is its low price, but it doesn’t have much GPU diversity.
Amazon Elastic Compute Cloud, or EC2, is one of the oldest products in the AWS portfolio. At the time of the 2006 public beta release, there was no GPU in the lineup, but it was released later. With the introduction of services like AWS SageMaker, the GPU lineup became more solidified. However, considering AWS’s position as a major player in the cloud computing market, users might find the GPU options supported by EC2 to be quite limited. Moreover, due to the guaranteed availability of compute resources, the pricing for EC2 instances tends to be higher compared to other solutions in the market.
Jarvis Labs is an Indian company founded in 2019 that allows fast and easy training of deep learning models on GPU computing instances. Operating data centers within India, Jarvis Labs is renowned for its extremely user-friendly and swift platform execution. While support for large-scale tasks might be limited, its popularity among students studying data science is primarily due to its straightforward interface and quick access to GPUs.
Based on the features of the GPU cloud services introduced above, the main user base is summarized as follows:
So, we’ve explored the core concepts of GPUs and specific services. However, without firsthand experience, understanding what they are can still feel difficult.
So, how about trying out the GPU solution offered by Elice? Elice provides GPU cloud services for various competitions, challenges, and projects of different institutions and companies. Additionally, customized GPU solutions are available for various purposes. If you’re interested in utilizing GPUs, consider adopting Elice right away!
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