What Is GPU Computing? {Benefits, Use Cases, Limitations}

What Is GPU Computing?

GPU computing refers to the use of graphics processing units for tasks beyond traditional graphics rendering. The GPU’s ability to perform parallel processing, which uses multiple cores of processing to execute different parts for the same task, makes this computing model effective. Nvidia’s RTX 3090 has 10496 cores, which can process tasks at once. This architecture makes GPUs well-suited for tasks that:Involve large data sets that require extensive processing.

Are dividable into smaller units of work the GPU can execute concurrently.

Are highly repetitive (e.g., matrix multiplication or convolution operations in image processing).

The main idea of GPU computing is to use GPUs and CPUs in tandem during processing. This architecture allows GPUs to be used in conjunction with CPUs for processing. Such a strategy considerably speeds up processing, making GPU computing vital in a wide range of fields, including:

• Scientific simulations (physics, chemistry, biology, etc. ).

Data analysis and mining.Deep and machine learning.Graphics rendering and 3D modeling.

• GPU computing is a standard part of high-performance computing (HPC) systems. Organizations running HPC clusters use GPUs to boost processing power, a practice that’s becoming increasingly valuable as organizations continue to use HPC to run AI workloads.
• GPUs and graphics cards are not interchangeable terms
• . A GPU is a circuit that can perform image and graphics processing. A graphics card is a piece of hardware that houses the GPU alongside a PCB, VRAM, and other supporting components.
• How Does GPU Computing Work?

The CPU and GPU work together in GPU computing. The CPU is responsible for the overall execution of programs and transfers specific tasks that require parallel processing to the GPU. Here are the types of tasks that CPUs commonly offload to GPUs:

CUDA (Compute Unified Device Architecture):

Developed by Nvidia, CUDA is a parallel computing platform that provides various Nvidia-based tools, libraries, and extensions.

• OpenCL:
• This model is an open standard for parallel programming usable for multiple brands (including AMD Intel and Nvidia).
• ROCm (Radeon Open Compute):

ROCm (R The most common models are:

CUDA (Compute Unified Device Architecture):

• Developed by Nvidia, CUDA is a parallel computing platform that provides various Nvidia-based tools, libraries, and extensions.OpenCL:
• This model is an open standard for parallel programming usable for various brands (including AMD, Intel, and Nvidia).ROCm (Radeon Open Compute):
• ROCm is an open-source platform that supports GPU computing on AMD hardware.SYCL:
• SYCL provides a single-source C++ framework for developing apps that run on GPUs.A GPU has its own memory hierarchy (including global, shared, and local memory). Data moves from the CPU’s memory to the GPU’s global memory before processing, which makes efficient memory management crucial for avoiding latency.

What Are the Benefits of GPU Computing?

GPU computing offers several significant benefits that make it a valuable tech in various fields. Here are the main advantages of GPU computing:

High processing power:

• GPUs have thousands of small processing cores that perform tasks concurrently. This parallel processing capability allows a GPU to handle a vast number of calculations simultaneously.Quicker execution of complex workloads:
• Users of GPU computing get faster results and quicker insights. GPU computing is highly scalable. All an admin needs to do to scale out is add more GPUs or GPU-accelerated clusters to a system.Machine learning and AI compatibility:
• GPU computing speeds up model training and enables organizations to develop more accurate and sophisticated artificial intelligence (AI) software.Smooth graphics rendering:
• GPU computing is essential for rendering high-quality 3D graphics and visual effects in video games, simulations, animation, and VR applications.Cost-effectiveness:
• GPUs are more cost-effective than compute-equivalent clusters that rely solely on CPUs. System power consumption is reduced and fewer pieces of hardware are needed to achieve the processing goals. A typical HPC system with GPUs and field-programmable gate arrays (FPGAs) performs quadrillions of calculations per second, which makes these systems a vital enabler in various fields.Interested in high-performance computing? Check out pNAP’s HPC servers and set up a high-performance cluster that easily handles even your most demanding workloads.
• What Is GPU Computing Used For?GPU computing is not an excellent fit for every use case, but it’s a vital enabler for workloads that benefit from parallel processing. Let’s look at some of the most prominent use cases for GPU computing.
• Scientific SimulationsScientific simulations are a compelling use case for GPU computing because they typically:

Benefit significantly from parallelism.

GPU computing enables researchers in various domains to conduct simulations with greater speed and accuracy. Here are a few examples of simulations that benefit from GPU computing:

Simulations of galaxy formations that lead to insights into dark matter and cosmic structure.

Climate models that simulate long-term weather trends and assess the impact of climate change.

• Molecular dynamics simulations that explore protein folding and protein-drug interactions.
• Material science simulations that enable researchers to study the properties of advanced materials.

Seismic simulations used in earthquake engineering and geophysics.

• Simulations of nuclear reactions and the behavior of subatomic particles.
• GPU-accelerated simulations are also leading to advances in fields like computational fluid dynamics (CFD) and quantum chemistry.
• Data Analytics and Mining
• Data analytics and mining require processing and analyzing large data sets to extract meaningful insights and patterns. GPU computing accelerates these tasks and enables users to handle large, complex data sets.
• Here are a few examples of data analysis that benefit from GPU computing:
• Fraud detection systems that use data mining techniques to identify unusual transaction patterns.

Systems that analyze stock market data, economic indicators, and trading trends to help make investment decisions.

Recommender systems that use data mining algorithms to suggest fitting e-commerce products or content to users.

Video feed analysis that enables object detection and event recognition.

Systems that analyze patient records and medical images (e.g., MRI or CT scans) to improve patient care and enhance medical research.

• Software that predicts product demand and optimizes inventory management.
• As an extra benefit, GPUs accelerate the generation of charts and graphs, making it easier for analysts to explore data. GPU computing speeds up preprocessing (cleaning, normalization and transformation). ).
• Training of Neural Networks
• Neural networks with deep learning capabilities are an excellent use case for GPU computing due to the computational intensity of training AI models. Here are some of the reasons GPU computing is a good fit for neural networks. The training of neural networks is highly parallelizable. The training time can be reduced by up to 90% when thousands of GPU cores are used simultaneously. Admins can quickly scale up systems by adding new GPU clusters or multiple GPUs. Learn about the popular deep learning frameworks, and how they can help you create neural networks using pre-programmed work flows. The two frameworks at the top of our list (TensorFlow and PyTorch) enable you to use GPU computing out-of-the-box with no code changes.
• Image and Video Processing
• Image and video processing are essential in a wide range of use cases that benefit from GPU computing’s ability to handle massive amounts of pixel data and perform parallel image processing.

Here are a few examples of using GPU computing to process video and images:

Autonomous vehicles using GPUs for real-time image processing to detect and analyze objects, pedestrians, and road signs.

Video game developers using GPUs to render high-quality graphics and visual effects on their dedicated gaming servers.

Doctors using GPU-accelerated medical imaging to visualize and analyze medical data.

• Social media platforms and video-sharing websites using GPU-accelerated video encoding and decoding to deliver high-quality video streaming.
• Surveillance systems relying on GPUs for real-time video analysis to detect intruders, suspicious activities, and potential threats.

GPUs also accelerate image compression algorithms, making it possible to store and transmit images while minimizing data size.

Financial modeling involves complex mathematical calculations, so it’s unsurprising GPU computing has significant applications in this industry. Here are a few financial use cases that GPU computing speeds up and makes more accurate:

Executing trades at high speeds and making split-second decisions in response to real-time market data.

Running so-called Monte Carlo simulations that estimate outcome probabilities by running numerous random scenarios.

• Building and analyzing yield curves to assess bond pricing, interest rates, and yield curve shifts.
• Running option pricing models (such as the Black-Scholes model) that determine the fair value of financial options.
• Performing stress testing that simulates market scenarios to assess the potential impact on a financial portfolio.
• Optimizing asset allocation strategies and making short-term adjustments for pension funds.
• Running credit risk models that assess the creditworthiness of companies, municipalities, and individuals.

Another common use of GPU computing is to mine for cryptocurrencies (i.e., using the computational power of GPUs to solve complex mathematical puzzles). Beware of crypto mining malware that infects a device and exploits its hardware to mine cryptos.

GPU Computing Limitations

While GPU computing offers many benefits, there are also a few challenges and limitations associated with this tech. Here are the main concerns of GPU computing:

• Workload specialization:
• Workloads that are heavily sequential or require extensive branching do not benefit from GPU acceleration.
• High cost:
• High-performance GPUs (especially those designed for scientific computing and AI) are expensive to set up and maintain on-prem. Many organizations find that building clusters of GPUs is too cost-prohibitive.
• Programming complexity:
• Writing code for GPUs is more complex than programming for CPUs. Developers must understand parallel programming concepts and be familiar with GPU-specific languages and libraries.
• Debugging issues:

Compatibility issues:

Not all apps and libraries support GPU acceleration. Developers must often adapt or rewrite code to ensure compatibility.

• Vendor lock-in concerns: Different vendors have their own proprietary tech and libraries for GPU computing. The challenges of GPU computing should be understood, but are not deterrents. Strategic OpEx-based renting of hardware and skilled software optimization are often enough to address most issues.
• Ready to Adopt GPU Computing?Our GPU servers, built in collaboration with Intel, Nvidia, and SuperMicro, rely on the raw power of dedicated Nvidia Tesla V100 GPUs. These GPU-accelerated servers are ideal for compute-intensive projects involving:
• Artificial intelligence.Machine and deep learning.
• High-performance computing.Inference.
• 3D rendering.
  The CUDA platform provides built-in libraries for:
• Deep learning (cuDNN, TensorRT, DeepStream SDK).Linear algebra and math (cuBLAS, CUDA Math Library, cuSPARSE CUDA Codec SDK ),

Image and video (cuFFT CUDA Performance Primitives NVIDIA Codec SDK).

Parallel algorithms NCCL nvGRAPH Thrust The CUDA platform provides built-in libraries for:

Deep learning (cuDNN, TensorRT, DeepStream SDK).Linear algebra and math (cuBLAS, CUDA Math Library, cuSPARSE, cuRAND, cuSOLVER, AmgX).Image and video (cuFFT, NVIDIA Performance Primitives, NVIDIA Codec SDK).

• Parallel algorithms (NCCL, nvGRAPH, Thrust).
• Our GPU servers are a pure OpEx-based infrastructure with zero CapEx investments. Our CapEx vs. OpEx post explains how this arrangement can benefit your bottom line. As AI workloads increase and GPU computing becomes cheaper thanks to cloud computing, expect to see more companies turn to this technology.