Data mining has become increasingly significant with the growth of big data industries. Here's how GPUs play a role in this scenario.
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 to keep up with the modernization of their fields as well as their growing demand. One tactic in particular that has been embraced is the application of enterprise-grade GPUs.
Data mining is the process of uncovering patterns within extensive data sets, while these extensive data sets can be referred to as big data. Big data is characterized by its inability to be processed through traditional means due to its level of complexity. In this way, the act of mining big data poses a particular set of challenges.
How GPUs Support Data Mining
The main difficulties that are involved with attempting to mine these large data sets have to do with the volume, the velocity, and the variety of the data. However, when factoring in the use of enterprise-grade GPUs to aid in the mining process, these problems can be greatly diminished.
Volume
The amount of data that is involved when referring to big data can be given away by its name - there are massive amounts of data to be processed. This large workload can pose a problem for many industries where budget is concerned as the undertaking of filtering it is more than a standard single CPU can manage. The purchasing of additional CPUs isn't a cost-effective option for the majority of businesses, whereas enterprise-grade GPUs offer not only a more affordable alternative, but a more effective solution as the volume intense task of processing big data isn't a roadblock for them. Through their numerous Algorithmic Logic Units (ALUs), GPUs don't require as much power as CPUs to process data and have an architecture that is dedicated to data processing as opposed to CPUs that are more inclined to data caching and flow control.
Velocity
Taking into account the large amounts of data that need to be processed, interpreted, and filtered, the timeliness with which this is done can become a burden. Certain industries are controlled by time-sensitive tasks, while the bulk of industries altogether isn't immune to time and resources costing significant funds. This means the lengthy turnaround time could result in lost business opportunities in addition to the actual expenses involved with the individuals and hardware allocated to the data processing. However, employing enterprise-grade GPUs can radically improve the lengthy process. With their many cores, they have a parallel structure that is predisposed to handling repeated instructions and are perfectly suited for speeding up the workload, solving the issue of timeliness, and reducing total costs.
