Big data has become the blood that pulses through our complex, modern society. But more than simple data acquisition, the challenge is leveraging so much information. From Amazon to the Food and Drug Administration, everyone is looking for ways to use data to boost productivity and improve customer support. And it’s only just begun. According to a Gartner report, more than 75% of companies are now investing or planning to invest in big data imminently.
As with most advancements in technology, necessity has been the mother of invention. The influx of data brought about by the big data revolution has created a real and serious need for technologies capable of analyzing and organizing all this informations. More important is translating this data into timely, actionable feedback and eventual ROI.
A number of technological solutions to the data overload problem have been explored. But after some trial and error, the graphics processing unit (GPU) has emerged as the front-runner. You've probably already heard of a GPU, which originally enabled your computer to deliver a faster, more enjoyable experience for gaming and watching videos. It didn’t take long for innovators to realize the same technology would also process more intense computations faster, and in a more cost-efficient way, than the CPU methods currently in use. In other words, using GPUs for big data analytics means any business can make better informed decisions and in real-time.
Realizing this potential, it was just a matter of writing a new programming language to allow direct interaction with the GPU. Now massive amounts of data could be systematically organized into usable chunks both actionable and precise. Some systems even offer users marketing suggestions and best practices advice.
Change doesn't happen overnight. Nevertheless, the GPU revolution is beginning to spread. In 2010, China’s Tianjin-based Supercomputing Center launched the Tianhe-1A, equipped with 14,336 Xeon X5670 processors and 7,168 Nvidia Tesla M2050 general purpose GPUs. It was , for a time, the fastest supercomputer on the planet. Since then, GPU-based supercomputers have gone into operation in the U.S., Russia, Switzerland, Italy, and Australia.
An exciting development has been the advent of affordable, software-optimized systems for the business world. A number of developers, Tel Aviv-based SQream, for example, use software to maximize the power efficiency of databases. The result is faster, more comprehensive analytics without the need for expensive supercomputers. From any perspective, this simply makes more sense for the average business than investing millions in hardware.
GPUs and Deep Learning
Where GPU technology really shines is deep learning. Simply put, deep learning is a type of machine learning that runs in a similar manner as the human brain. Deep learning enables computers and other machines to learn and adapt responses according to perceived (or input) behavioral patterns. While this was an option with standard CPU systems, GPUs allow machines to adapt faster and more precisely to new situations.
Big data is opening doors in ways no one would ever have believed just a few decades ago. GPU-based technologies are the keys to unlocking them. It’s an exciting time to be involved in the world of tech. One can only imagine what new developments will come. But without a doubt, big data will continue to change how business is done into the future.