Graphics Processing Units
Find out what enterprise-grade Graphics Processing Units can do for your performance.
Graphics Processing Units came to the tech scene as a means to ease and improve the visual display aspect of computers; however, a few decades later, they contribute to much more than graphics. Today they count themselves amongst the critical components in the makeup of some of 2018’s fastest supercomputers. That being said, non-supercomputer enterprises stand to gain from the application of GPUs as well.
The term Graphics Processing Unit (GPU) first became popular during the 90s with the development of the Nvidia GeForce 256. This model became the first consumer-available GPU to include T&L (Transform and Lighting) hardware and contain a cache. Fast forward to the early 2000s, and the concept of the General Purpose Graphics Processing Unit (GPGPU) was in the works. This expansion meant that GPUs would no longer be considered solely for the interpretation and display of images. Instead, it became possible for many data-laden industries to appreciate and apply GPUs to tasks traditionally handled by the CPU.
While some of the benefits of applying GPUs can vary per industry, there are a few central advantages that apply.
Modern GPUs make use of caches, register files, frame-buffers, and other hardware that increases their capabilities and performance. These variances enable companies to make use of GPUs in ways that suit their particular processing needs. For example, with their large register files, GPUs not only differentiate themselves from CPUs but can also be more effective when attempting to reduce context-switching latency. The advantage of being able to access data quickly and without noticeable stalling between tasks can prove invaluable for those in the field of machine learning.
Taking the benefits described above into account, GPUs can help companies significantly lower their costs. By speeding up turnaround times, GPUs shorten the amount of time company resources are in use, leading to cheaper expenses. These quick turnaround times also mean that companies don’t have to miss out on other business opportunities, costing them potential revenue. Expenses can be reduced through the flexible, parallel architecture of GPUs as well, as they are capable of doing the work of multiple CPUs, all while consuming less energy.
Machine Learning (ML) is a growing subset of Artificial Intelligence (AI) that uses statistical techniques in order to make computer learning possible through data and without any specific programming. What this means is that ML makes use of large amounts of labeled data and processes it to locate patterns before applying what it learns about and from the patterns to its program.
Data mining has become increasingly significant with the growth of big data industries. As such, these industries are now heavily reliant on the evolution of data mining tactics and techniques keeping up with the modernization of their fields as well as their growing demand.