Competitive advantage comes from making quicker, smarter, more accurate decisions and pivoting to the new opportunities faster than your competitors.
Unfortunately, the massive volume of data that must be analyzed to extract that sort of value has traditionally required long compute cycles and a physically large—and costly—computing infrastructure.
However, advances in graphics processing unit (GPU)-accelerated computing mean you can now take computing challenges once considered impossible to overcome and transform them into immediate financial gain.
What is GPU-Accelerated Computing?
GPU-accelerated computing occurs when you use a GPU in combination with a central processing unit (CPU), letting the GPU handle as much of the parallel process application code as possible. The GPU takes the parallel computing approach orders of magnitude beyond the CPU, offering thousands of compute cores. This can accelerate some software by 100x over a CPU alone. Plus, the GPU achieves this acceleration while being more power- and cost-efficient than a CPU for this type of workload.
Provided your system design team is experienced with building both CPU and GPU-based systems and the storage subsystems required for this level of data analytics, the outcome of moving to a GPU-accelerated strategy is superior performance by all measures, faster compute time, and reduced hardware requirements.
What Are the Benefits of GPU-Accelerated Computing?
GPUs have transformed how complex computational tasks are performed across various industries. By harnessing the power of GPUs, which are optimized for parallel processing, organizations can achieve unprecedented levels of performance, efficiency, and scalability. Below are some key benefits of GPU-accelerated computing.
1. Improved Computational Speed and Performance
As mentioned earlier, one of the primary advantages of using GPUs for computing is the significant boost in performance. Unlike traditional CPUs, which are optimized for single-threaded performance, GPUs are designed to handle many parallel tasks simultaneously. This makes them ideal for applications that require massive data processing, such as:
• Machine Learning and Deep Learning: Training complex neural networks and models can take weeks on a CPU but only a fraction of that time on a GPU.
• Scientific Simulations: Fields like molecular dynamics, physics simulations, and climate modeling benefit from GPUs’ ability to handle vast amounts of data in parallel.
• Video Rendering and Graphics Processing: GPUs excel at processing large datasets of pixels and visual effects, making them indispensable for video editing, 3D rendering, and animation.
2. Enhanced Efficiency and Cost Savings
Although GPUs are often seen as expensive hardware, their ability to accelerate workloads can result in cost savings in the long term with less equipment required, reducing footprint and operational costs. In addition you can experience:
• Reduced Time-to-Results: Faster computation means quicker insights, whether you're training AI models or simulating physical systems, which can save companies valuable time.
• Energy Efficiency: While GPUs may consume more power than CPUs, their parallelism allows them to complete tasks much faster, which can lead to lower overall energy consumption for large-scale operations.
• Lower Operational Costs: In high-performance computing (HPC) environments, GPUs can reduce the need for large server farms or high-end CPU-based clusters, saving on both hardware and cooling costs.
3. Scalability and Flexibility
GPUs are designed to scale, making them ideal for workloads that need to handle massive datasets or high-throughput operations. Their flexibility also allows them to be used across a range of industries:
• Cloud Computing: GPU-accelerated instances on platforms enable businesses to rent GPU power on-demand, making high-performance computing more accessible without the need for significant upfront capital investment.
• Big Data and Analytics: GPUs are invaluable for processing large datasets in fields such as genomics, financial modeling, and data mining, where fast and scalable analysis is crucial.
• Virtualization: Many cloud providers support GPU-based virtual machines, offering the performance of a physical GPU with the flexibility of cloud computing.
4. Real-time Processing and Responsiveness
GPUs excel in real-time data processing, which is essential in applications that require quick decision-making or fast feedback loops. Some examples include:
• Autonomous Vehicles: GPUs can process the data from cameras, lidar, and radar sensors in real-time to help self-driving cars make immediate decisions about their environment.
• Video Streaming and Augmented Reality (AR)/Virtual Reality (VR): GPUs are used for real-time rendering of high-definition video or immersive experiences, enabling smooth, lag-free playback and interaction.
• Financial Trading: In high-frequency trading, where microseconds matter and latency and jitter are deep concerns, GPUs are used to process enormous amounts of financial data to make decisions quickly and efficiently.
5. Advancements in Artificial Intelligence (AI) and Machine Learning (ML)
AI and ML rely on extensive computations to identify patterns, train models, and make predictions. GPUs are particularly well-suited for these tasks due to their high throughput and parallel processing capabilities. Benefits include:
• Faster Training: Training deep neural networks can take weeks on CPUs but just days or even hours on GPUs, dramatically improving the development cycle.
• Better Model Accuracy: GPUs enable researchers to experiment with more complex models, hyperparameters, and larger datasets, leading to more accurate predictions and insights.
• Support for Advanced AI Algorithms: GPUs are optimized for matrix operations, which are central to the workings of many AI algorithms like convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
6. Versatility Across Industries
GPU-accelerated computing has found applications across a diverse range of sectors, enhancing performance and enabling innovation:
• Healthcare: In areas such as medical imaging, drug discovery, and genomics, GPUs speed up the analysis of complex datasets, aiding in quicker diagnosis and better treatment planning.
• Finance: In financial modeling, risk analysis, and fraud detection, GPUs enable faster processing of large-scale financial data, helping firms stay competitive in the fast-paced market.
• Energy: GPUs support simulations for oil and gas exploration, renewable energy modeling, and even improving grid stability, enabling faster and more accurate predictions for energy systems.
7. Future-proofing with Emerging Technologies
Adding GPU-accelerated computing now prepares you for evolving technologies like quantum computing and blockchain—not to mention AI’s continued revolution, GPUs will remain a critical component for processing the immense data loads these fields will require. Their ability to scale, adapt, and perform at the cutting edge of technology means they will continue to drive innovation for years to come providing many long-term benefits.
Why GPUs? Why Now?
Companies have an insatiable appetite to build all things AI. Not surprisingly, most AI workloads in production today leverage GPU-accelerated computing. Enterprise-class GPUs are well-suited for AI because they are designed for parallel processing, making them efficient at handling the large amounts of data and complex calculations required for AI and machine learning (ML).
GPU-accelerated computing is more than just a trend—it's a transformative shift that enhances performance, reduces costs, and enables the development of next-generation technologies. By embracing GPUs, organizations can not only optimize their existing operations but also unlock new possibilities in fields ranging from artificial intelligence to high-performance scientific research. As the demand for computational power grows, GPU acceleration will continue to play a central role in shaping the future of computing.
Looking to embrace the computational power of GPUs? Penguin Solutions can be your trusted strategic partner for AI and HPC solutions. With 25+ years of HPC experience and 7+ years of designing and deploying AI infrastructure, and more than 85,000 GPUs deployed and managed since 2017, we are ready to help.
Contact the AI infrastructure experts at Penguin Solutions today to discuss your AI project needs.
