The potential power of artificial intelligence (AI) is drawing attention to how much untapped value sits in the vast quantities of data that organizations have accumulated in recent years. However, processing such large quantities of data has historically required a great deal of computing power but also a great deal of time, exactly what organizations don’t need when they seek competitive advantage. With graphics processing unit (GPU)-accelerated computing, though, the information technology (IT) industry has a new, more effective, more efficient alternative.

In the past, many data-intensive projects relied on costly, central processing unit (CPU) intensive infrastructure to extract value from data. For years, computing hardware vendors focused on increasing CPU speed (for more computing in less time). But this has penalties, including higher power use and greater heat generation.

Using GPU-accelerated computing, that is using a GPU in combination with a CPU, letting the GPU handle as much of the parallel process application code as possible, allows researchers to gain greater insights and generate actionable data more quickly and more cost-effectively than ever before. This is because a single GPU can offer the performance of hundreds of CPUs for certain workloads.

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.

Some processes are inherently sequential and achieve best results with the CPU.  However, many other, parallel application processes can benefit from GPU resources. For that reason, using CPUs and GPUs in combination takes advantage of the best of both technologies, tapping the impressive sequential processing power of the latest generation of CPU with the exponential capacity for parallel processing offered by top-performing GPUs.

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.

While this is great for now, the return on investment of using GPU-accelerated computing extends into the future. NVIDIA, a leading GPU developer,  predicts that GPUs will help provide a 1000X acceleration in compute performance by 2025. This inevitable increase on the reliance on GPUs means that early adopters will enjoy not only greater computing power over time but have a greater margin of difference over time than competitors who do not migrate to GPU-accelerated computing.

This is because the technology that makes GPU-accelerated computing desirable for current data analytics also makes it ideal for AI, which needs a great deal of computing power. On top of that, the continuous improvement in GPU technology from NVIDIA and other vendors and the massive stores of data now available to improve algorithms will allow organizations already familiar with GPU-accelerated computing to more smoothly transition into AI.

The increased efficiencies of GPU computing will also likely lead the path for edge computing.  As the coming improved networks enable a world of high-speed, low latency inference operations at the edge, the most powerful and power efficient platforms will naturally be selected for these applications.

This AI-related value is dependant on finding a system designer with experience in both GPU-accelerated computing and AI, which can be a challenge. However, once you do and you have a balanced AI system that takes full advantage the capabilities of the GPU, the returns from this exciting technology will become even stronger.

To learn more about GPU-accelerated computing visit, www.penguincomputing.com/gpu