Still Using a Traditional CPU? Consider These Emerging Alternatives

by Kevin Gardner Opinon and Insights

With the increased innovations in technology, many businesses, cloud producers and individual users are now shifting from the traditional Central Processing Unit to the modern Graphics Processing Unit database. Many businesses have immensely benefited from its ability to increase querying, ingesting and a general reduction in IT costs. The following benefits will make you embrace the GPU over the Traditional CPU;


Increased Speed of Performance

Since it is made up of multiple cores, it can handle multiple threads which increase the application speed ten times that of the traditional one. While the CPU uses the cache to reduce memory access, the GPU amplifies its bandwidth with cache memory.


The GPU automatically switches to another thread whereas the CPU waits for RAM to process a thread which is ready for processing. This reduces the latency hence produces results faster. The CPU can only handle 1-2 thread per core while the GPU can manage up to 1024 threads.


Those people who work in the mining industry which mines the cryptocurrencies prefer the GPU to the CPU DUE to its speed. Generally, the speed of GPUs is 10x to 100x faster when processing the same work as the traditional CPU.


Big Data Analytics

GPUs have brought much business to total realization due to their big data analytics. Many companies like Dell have embraced this technology and have used it to monitor their social media platforms globally. Through it, you will obtain real-time insights many times faster compared to the CPU system. They also offer greater changes that stand out especially when performing large amounts of tasks that are repetitive. The ingestion rates of data are very fast.


3D Processing

The GPUs are naturally designed to carry out 3D processing tasks. This is contrary to the CPU which performs only linear tasks. Whenever you want to increase the number of tasks performed, the GPU will be of great help. This device is also a number-cruncher as it can perform millions of calculations within a short time. It has a high amount of cores compared to the CPU.


The GPU projects can be implemented by setting the database then using the standard SQL to input data. This is done with the help of JDBC, ODBC, Python, R and ML libraries. After that, you can easily scale up by adding another box. The GPU has enough computing power and some GPU databases can store up to 100 TB of raw data.


The other benefits that the GPU offers over the CPU include increased efficiency due to its innovation that is more rapid. This efficiency is twice that of CPUs and the release cycle is also faster. The GPUs are also much smaller compared to the CPU.A GPU with 16 accelerated servers perform the same tasks as 100 CPU cluster. It also exhibits real-time data exploration. This is a great benefit to data scientist and first time machines that are learning algorithms.


People in virtual space can share THE GPU hardware acceleration. This can be shared with around 32 users abroad. The NVIDIA GRID for instance, provides very superior graphics that can be shared on the GPU by many users. The memory of the GPU IS enough to maximize the user density for many application that are graphic in nature. Also given that the latency remote is low.


Generally, setting up a GPU database is much easier a little data will be modelled and no extra expenses incurred. You also don't require skills to set it up. Unlike the traditional CPU, the GPU databases work in conformity to the environment and coexist well with your acquisition tools, BI, analytics, data sources as well as visualization tools.


There is a significant recent shift from the CPU to the GPU due to its numerous benefits that it brings to the various sectors like finance, Ad Tech, Telco, Retail, and Security. You ought to embrace this amazing technology.


About Kevin Gardner Junior   Opinon and Insights

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Joined APSense since, December 27th, 2017, From Salt Lake City, United States.

Created on Jan 16th 2018 12:34. Viewed 208 times.


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