GPU computing

What is GPU computing?

GPUs get the credit for leading the charge in supercomputing. In situations when graphics or content must be rendered at high speeds, GPUs are essential. Utilizing GPU computing benefits the internal CPU, allowing for processing and rendering graphics at an accelerated rate.

This alliance between graphics processing units and central processing units promotes a smoother processing system, achieving utilization that could not be achieved by a CPU on its own. While CPUs do have much higher processing speeds, GPUs have unmatched processing capabilities thanks to parallelism.

Acting as a companion processor to CPUs, GPUs exponentially boost the speed and processing capabilities of a system. GPUs perform computing applications concerning technical and scientific data in an accelerated way, adding efficiency when they are integrated alongside CPUs.

Another benefit to using GPUs is that they lessen the burden on the CPU by processing repetitive data in smaller chunks across several processors and enabling computing to proceed uninhibited by the limitless number of problems that it is tasked with solving.

In addition to processing power, GPUs extend memory bandwidth. Working hundreds of times faster than CPUs, GPUs make the automation and intelligence of machine learning (ML) and Big Data analysis possible, as they process massive amounts of data via neural networks. The AI then learns deeply complex tasks that no data scientist would have the language to teach or translate.

Additional benefits include but are not limited to:

• Superior processing power
• Exponentially greater memory storage/bandwidth
• Robust data analysis and analysis of AI and ML
• Rapid advances in gaming and graphics
• Easy integration into data centers

IT’s focus has shifted to reflect and support the compute demands of AI and data science. This work is done by GPUs. Applications that are being run on CPUs are accelerated using GPU compute, which optimizes performance and workload capacity.

GPU computing enables applications to run with extreme efficiency by offloading series of computational scientific and technical tasks from the CPU. GPUs process thousands of tasks in seconds through their hundreds of cores via parallel processing. Parallel processing denotes a function where data sets are funneled into a GPU’s processing cores and are all solved for simultaneously. Performance is increased as the GPU crunches and translates data while the CPU is running the remaining applications.

Insights from data analytics lead the way in problem solving and increased functionality with the use of GPU compute. GPU’s ability to quickly process and sort massive amounts of data allows for industry leaders to quickly and accurately access the insights into their data and innovate accordingly.