GPU-accelerated databases are positively transforming the tech market.
GPU-accelerated databases are becoming increasingly popular in tech in 2017, and are being used by many Fortune 100 companies, and organizations like the US Postal Service, and U.S. Army because of their ability to store huge amounts of data, compared to the CPU, which is short-circuited and can not handle the vast amounts of big data facing today’s consumers (Ballard, 2017) (Biery and Mizell, 25-29).
“The ability of a GPU to process thousands of threads can accelerate the same software by 100x over a CPU alone.” The GPU achieves this acceleration while being more powerful and cost-efficient than a CPU (Biery and Mizell, 3).
GPU and GPU-accelerated databases are becoming mainstream technology in 2017, as they have the ability to break up tasks, and complete them efficiently, unlike the CPU. The benefit of the GPU is that it is fast and can take on multiple operations, that involve complicated processing, which can get difficult for the CPU.
GPU-accelerated technologies replacing old software in 2017 is unsurprisingly a goal that many companies have already accomplished, as the GPU was marketed last year and is pushing forward with a lot of potential to increase top tech companies’ value in 2017 (Biery and Mizell, 3). This is due to the tremendous increase in big data this year.
Big Data increased by 40% (“Is 2017 the Year of GPU Databases”, 2017), so far this year, and is projected to soon “reach $200 billion” by December. In a recent Forbes article written by Gil Press, he talks about big data’s enormous potential based on analysis. In 2020, big data will reach $203 billion “at a CAGR, compound annual growth rate of 11.7%.” (Press, 2017). The banking industry will grow tremendously.
Fortune 500s’ infrastructures, powered by GPU-accelerated databases which can process and analyze big data will help various industries reach profitable goals in the years to come.
$203 billion by 2020!
The need for systems that can analyze big data, conduct research on consumer’s ever changing needs and store massive amounts of consumer big data, is a major deciding factor for companies to use NVIDIA’s GPU-accelerated data (Biery and Mizell, 3).
For example, Apple is considering using GPU as part of their design for new iphones, which means partnering with NVIDIA, which could make the iphone even more capable of storing data-heavy applications (Dilger, 2017).
Because of the GPU’s great return-on-investment, it can now be rented or bought by any company and accessed through the cloud. (Biery and Mizell, 3).
GPU technology is successful in the current market because of it’s ability to help all kinds of leading-industry professionals from doctors working in an intensive-care unit that need real-time updates on their patients, to leading advertising companies that want to conduct an analysis on an idea behind an upcoming advertisement. (Biery and Mizell, 3). Big data has enormous capability to influence the way business people today operate.
I would not be surprised if NVIDIA buys Apple and Netflix, and the San-Francisco based video-gaming company becomes extremely popular for their flagship product here on the East Coast.
The GPU has come a long way since it’s humble beginnings at NVIDIA and is doing great! Check out NVIDIA’s performance in the stock market last week, alongside Apple, Amazon and Netflix.
Co-work at Aponia Data Solutions and meet talented people working in technology!
Ballard, John. “NVIDIA GPUs Are Powering Some Big Clouds.” The Motley Fool, 17 April 2017, https://www.fool.com/investing/2017/04/17/nvidia-gpus-are-powering-some-big-clouds.aspx. Accessed 9 October 2017.
Biery, Roger, and Mizell, Eric. Introduction to GPUs for Data Analytics: Advances and Applications for Accelerated Computing. O’Reilly Media, 2017.
Dilger, Daniel E. “Why Apple’s new GPU efforts are disruptive threat to Nvidia” AppleInsider, 4 April 2017, http://appleinsider.com/articles/17/04/04/why-apples-new-gpu-efforts-are-a-major-disruptive-threat-to-nvidia. Accessed 9 October 2017.
(Editorial Team). “Is 2017 the Year of GPU Databases?” insideBIGDATA, 8 Sept. 2017, https://insidebigdata.com/2017/09/08/2017-year-gpu-databases/. Accessed 9 October 2017.
Press, Gil. “6 Predictions For The $203 Billion Big Data Analytics Market” Forbes, 20 Jan. 2017, https://www.forbes.com/sites/gilpress/2017/01/20/6-predictions-for-the-203-billion-big-data-analytics-market/#3227c9f82083. Accessed 9 October 2017.