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Individuals often think of supercomputers when they consider big data and algorithms. They contemplate the largest and most powerful computers in the world processing billions of numbers and then creating a valuable output that a company then uses. While this illustration is close to the ideal of the big data-supercomputer relationship, it is not the current situation. Companies that employ massive quantities of data have moved away from massive computers and towards much smaller ones.
Proprietary Machine Learning Algorithms
They have begun to utilize commercially available software and focus their business solely on their proprietary machine learning algorithms and software. This approach is clearly problematic for the data analysis companies. They need to rediscover the utility of the supercomputer and the effectiveness of such a helpful tool for their business. Using a supercomputer can help businesses that crunch numbers and analyze data test out some of their algorithm ideas without having to spend their entire budget on a massively powerful computing device.
Supercomputer Data Processing
A supercomputer is a computer that is fine-tuned to solely process data. The origins of computing were in machines that resemble crude versions of today’s supercomputers. Computers were almost entirely streamlined and focused on crunching numbers and producing outputs. They had military and industrial usages as well as research possibilities.
Processing large amounts of data were their most common usage and the way that they helped revolutionize radar and military procedure after the Second World War. The computer at this time was far from the ubiquitous good that they are today. These machines were not particularly attractive and were not available for public consumption. College students and researchers had to travel to specific sites and sign up for time to process their data. In many ways, they remained a novelty up until the late 1960s and 1970s.
Computer Technology Evolution
Over time, the computer moved out of the massive terminal area and the college campus and into the home. Marketers and entrepreneurs such as Steve Jobs and Bill Gates discovered that they could package computer technology and find ways that individuals could purchase and use it. Computer technology progressed to the point where individuals could carry around computers in their hands.
Data Processing Machines
The older approach to computing moved into the realm of data processing machines known as supercomputers. These supercomputers are massive machines that process data as quickly as technology will allow. Supercomputers partially gain their effectiveness due to their minimalist outside appearance. They have been stripped of all of their accouterments. There are no speakers, prominent visuals, or user-friendly applications. The computer basically has a text readout connected to an apparatus that may be the size of a room.
Supercomputer Power Consumption
Supercomputers could not fit in a home or almost any office for a number of other reasons as well. These machines use a massive amount of electricity and generate an enormous amount of heat. They are far outside of the price range of almost any company except for tech behemoths like IBM and Google. However, all of this streamlining and specialization leads to a computer that can achieve far more than the average computer. Supercomputers can process a vast array of data points and test out predictions and algorithms to a minute detail over decades.
Weather Forecasting Supercomputers
They can make weather predictions for the globe down to a zip code and can track the performance of thousands of businesses. The current fastest computer in the world, built by IBM, has been able to assist in mapping out the human genome. Supercomputers can even exhibit the strength of artificial intelligence that rivals human beings. Several computers have gotten in the news recently for winning human games such as Go and Jeopardy.
Perhaps the most impressive aspect of these supercomputers is that they become considerably more powerful every year. A process called Moore’s Law stipulates that the chips that run all computers become almost twice as powerful every two years. Constant private and public investment allows these chips to quickly be integrated into the newest and most powerful computers being built.
Big Data and Supercomputers
Big data began with the supercomputer. At first, a supercomputer was the only computer powerful enough to process algorithms and display output quickly. The relationship between big data and supercomputers has been strained over time. Over time, the field of data analysis has moved past the usage of supercomputers. Some data analysis firms are formed in basements and on personal computers. Technology has progressed to the point that a basic amount of computation can be performed by an individual computer in an office.
The field has blossomed as well. Originally, big data companies simply developed algorithms and processed numbers that could be sold to interested parties. Now, the field of data analysis has expanded to marketing, advertising, product development, and consulting. As a result, companies that process data are now skeptical of their need for supercomputers. This approach deprives companies of machines that can make their lives immensely easier.
Supercomputer Big Data Analysis
Supercomputers are critical to the vanguard of big data even today. They are the machines that help cultivate algorithms and prove that those algorithms work. The utility of a supercomputer is its ability to process data incredibly quickly. Supercomputers can churn out thousands of calculations in under a second. This approach can be helpful for a big data system. For instance, a company may be attempting to use big data to predict the performance of a steel factory. The algorithm designed may use a neural network to process data and make accurate predictions about how much steel is being produced. An artificial neural network is the most basic structure for artificial intelligence systems and should be used in this project.
Neural Networks in Supercomputers
The neural network must have weights and assignments attributed to each of its nodes. Each node may contain a formula that weighed and analyzed the importance of labor, weather conditions, or electricity prices. An algorithm may be tested to view how it would have predicted earlier outputs given the tools at its disposal in an effort to reproduce those earlier outputs within a margin of error. Such an algorithm may need to be run thousands or millions of times with resulting tweaks in order to help fine-tune its effectiveness before it is used in a public setting.
Supercomputer Hardware Components
Companies often cannot afford supercomputer hardware even if they are leaders in big data production. However, they can contract with the companies that do own these computers. Big data companies can connect with these other facilities and use them to process their newest algorithms. Big data procedures that may take hours or days on a company’s computer may take a few minutes on supercomputer hardware. A supercomputer can be a tool used for proof of concept and to correct the procedures of an algorithm. There is always the possibility that an individual has weighted one factor more heavily than he or she should have. A short-term contract would allow companies to test out their ideas and reduce the amount of time between research and release of products.
Thoughts on Supercomputers
Companies are understandably worried about using supercomputer hardware and software for their algorithms. They want to ensure that their proprietary information is kept secret and cannot easily be stolen. Companies also want to know that they are self-sustaining and that they can survive without having to worry about the equipment of other companies. These concerns are understandable and yet misguided. The massive computing power of supercomputers should be considered an asset rather than a liability. They can be a tool for data companies to make as much money as possible by researching and developing their assets. Such an effort should always be embraced by companies that use and promote algorithms for their businesses.