What is a Supercomputer?

A supercomputer is nothing but a type of computer which is designed with resources, architecture, and components for achieving massive power required for computing. These supercomputers available today contain thousands of processors that help in performing trillions and millions of computations and calculations just in seconds.

Supercomputers are designed to perform massive computing in organizations and enterprises. These computers consist of operational and architectural principles from grid processing and parallel processing. This is nothing but simultaneous execution of processes using thousands of processors that are distributed. Even though supercomputers contain thousands of processors which demand significant floor space, they come with other key components which are like typical computers. They contain components like operating systems, applications, connectors, and even peripheral devices.

Supercomputer- fastest computer

Yes, a supercomputer is the fastest computer that has the power to process substantial amount of data in a short interval of time. When compared to general-purpose computers, computing performance of supercomputers are extremely high. The FLOPS is the measure used to measure the computing measure of supercomputer rather MIPS. The FLOPS is nothing but floating-point operations per second. So, we can state that supercomputers are able to deliver on average hundred quadrillions of FLOPS.

Supercomputers have evolved from the earlier grid system to the cluster system. In a cluster system, actual computing means, a machine using many processors in just single system rather making use of arrays of different computers in the network. When it comes to size, these computers are massive. There are supercomputers which even occupy few feet and there are ones which need hundreds of feet. The cost of these supercomputers is really high. There are supercomputers ranging from 2 lakhs to 100 million dollars.

Supercomputers and their characteristics

Supercomputers can allow over a hundred users at the same time. These computers are capable of handling highly massive calculations which are definitely beyond human capabilities. Hence supercomputers are used in the areas where humans are unable to resolve highly extensive calculations. At the same time, any number of users can access a supercomputer. Supercomputers are most extensive computers available to date.

Supercomputers and their features

  • Supercomputers contain more than one central processing unit. The CPU consists of instructions to interpret instructions so that the computer can execute arithmetic as well as logical operations
  • When it comes to computation speed of the central processing system, you can expect extremely high computation speed from supercomputers
  • Unlike general-purpose computers which operate on pairs of numbers, supercomputers can operate on the pairs of lists of numbers
  • Earlier supercomputers were used mainly for national security, cryptography, and even for nuclear weapon designs. But today they are also used for automotive, aerospace, and even for petroleum industries

Uses and benefits of supercomputers

Supercomputers are never utilized for day to day tasks. This is because they are costly and even because of their superiority. Supercomputers are mainly used in places where there is a need for real-time processing. There are many uses and benefits of supercomputers and some of them are

  • Supercomputers are utilized for research and scientific simulations. For example, they are best suitable for weather forecasting, nuclear energy research, meteorology, chemistry, physics, and even for situations where there is a need for extremely complex and animated graphics
  • Supercomputers are also in use for predicting new illness, diseases, and even for certain types of treatments
  • Supercomputers are used in the military mainly for testing tanks, aircraft, and even for testing weapons
  • Military, they are also used for understanding the effects of wars and effect on the soldiers
  • Supercomputers are best suitable for encrypting data for security purposes
  • They are also the perfect choice when it comes to the testing impact of the nuclear weapon detonation
  • When it comes to the creation of animations in Hollywood, supercomputers are the first choice
  • Supercomputers are also in use behind highly entertaining online gaming. Here they are mainly used for stabilizing the performance of the game mainly when multiple users are making use of the game

Final thoughts

There is an essential feature of computers which should be noted. The computers are nothing but general-purpose machines which can be utilized for all kinds of purposes. It is possible to send emails, play games, and even edit photos using computers. It is possible to perform any number of things using computers. But when it comes to supercomputers, they are slightly different. You won’t need their computing power to check out an adult sex site like Free Fuckbook App. These aren’t machines for personal computing use.

Supercomputers are mainly utilized for mathematically intensive, complex, scientific problems which even include simulation of highly massive nuclear tests and weather forecasting. They are also very useful for simulation of climate changes and tests that find strength of encryption. So, we can make use of general-purpose supercomputers for almost all kinds of purposes.

So, general-purpose supercomputers are one of the varieties in supercomputers that are used for wide range of applications like all kinds of scientific problems. But there are supercomputers which are mainly designed to perform highly specific jobs. For example Deep Blue was the supercomputer that is designed by IBM in the year 1997 just for playing chess. This is an example of specially designed machines which can perform particular job. These are different from general-purpose supercomputers.


What Makes Up a Complete Computer System?

An entire computer system can receive user input, process it, and carry out the required functions, and display the output. It should efficiently store the input or the output and should carry out all these steps in minimal time. A computer can be thought of as a combination of its hardware and software. These two work together to transform data into information.

A computer system is a set of hardware as well as software components that together make the computer function. Major hardware devices include a keyboard, monitor, mouse, and chips, among other optional as well as necessary components. The software includes basic applications and kernel as well as shell scripts, which make the computer understand the inputs and carry out the required functionalities.

These days, computers are being used for all types of applications ranging from complex calculations to leisure games, among others. Any task that can be carried out systematically, it can also be carried out by a computer. In a nutshell, the functional components of a computer can be summarized as below:

  • Input Unit

The input unit of the computer system is responsible for accepting the data. For this, it uses standard input devices, like the ones mentioned above, namely, mouse, keyboard, scanner, bar code reader, and such.

  • Output Unit

This unit is responsible for displaying the data to the user. This data, or information, is the processed data displayed in a human-readable format. The output unit performs these functions. Standard output devices (hardware) include monitor or screen, speaker, printer, and such.

  • Processing Unit
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This unit is responsible for carrying out the given instructions on the provided data. These unit’s functions are performed by the Central Processing Unit (CPU). CPU further has the following components:

  • Arithmetic and Logic Unit (ALU): This unit performs all the arithmetic calculations or logical instructions. Arithmetic calculations include +, – , * , / , among others, while logical instructions include < , > , = , etc.
  • Control Unit (CU): This unit controls the execution of the instructions. It times their implementation according to their priority and makes the ALU and other groups carry them out.
  • Primary Memory: The CPU needs storage space to store the data while the instructions are being carried out. This storage space is called primary memory. It is a collection of registers.

Storage Unit

This is the permanent storage space that stores any kind of data. There are various types of storage devices, such as hard disks, CDs, pen drives, and DVDs, among others.

Computers come in various types, including embedded computers, personal computers such as laptops, desktops, and smartphones, among others, programmable computers, workstations, mainframes, and supercomputers. Each has its unique functionalities and importance. In today’s digital age, there is a rapid adoption in these computer systems. Besides, owing to the proliferation of the internet, one cannot do without a computer anymore. Computers provide speed, reliability, high storage capacity, accuracy, and versatility. While all of these characteristics are very important, computers fundamentally lack decision-making power and have zero IQ. Thus, without a human operating it, a computer cannot do much. However, AI and machine learning has come a long way and many operations are optimized. Of course businesses have rushed to utilize these to slim down there operations. Adult sites like fuckbook and other hookup apps have been at the forefront of utilizing this tech to maximize profits.

What Does a Data Scientist Do?

With so much digitalization in recent years, most organizations is in constant need of data science professionals. The escalation of big data in 2010 led to the growth of data science. It was required to support the need of businesses to draw insights from vast unstructured data sets. The abundance of data allows for a more data-driven approach to train machines rather than a knowledge-based approach.

Data science is described as anything related to data, including modeling, analyzing, and collecting. But the most crucial part is its all sorts of applications like machine learning.

The misconception of a data scientist

The general masses have a popular misconception about data scientists. We think a data scientist is only involved in learning AI (Artificial Intelligence) or machine learning. However, most organizations hire data scientists as analysts. Undeniably, they can solve technical problems, but the companies hire them to solve the problems relating to data.

So, what does a data scientist do?

  • Data collection

One of the primary duties of a data scientist is collecting data. While collecting data, there will also be involvement of business stakeholders. The stakeholders will have domain knowledge about the project. Through them, we can extract data, whereby they offer lots of references and sources. It might be from a third party or web scraping etc. Also, note that the data collected are raw and not clean.

  • Preparation of data

After the collection of data, the team will start preparing data. They will clean the data and put it in the proper format. Cleaning of data is vital as it helps to produce a tremendous analytical report and avoids incorrect conclusion. With the help of software programs, they clean lots of raw data and put it in the right order.

  • Exploratory data analysis

In the exploratory analysis, a data scientist will try to include statistical analysis of the data. Doing statistical analysis helps them understand the data, which is very important while solving machine learning use cases. A data scientist tries to study the behavior of data by involving lots of diagrams or diagram visualization. Because of its thorough analysis, it helps companies their customer behavior and optimized plans according to it.

  • Evaluation and interpreting exploratory data analysis outcome

After identifying the trend and the pattern, a data scientist has to present the result to the stakeholders. The task can be challenging because a data scientist will have to submit a report to marketing professionals. They may have limited knowledge of data science; hence a data scientist must give the result in a simpler term.

  • Model testing and building

After sorting out everything, a data scientist will choose potentials models and algorithms. So, in a model building, the data scientists will select one algorithm and perform high parameter optimization or cross-validation to determine the accuracy.

Apart from the accuracy, they also look upon various factors like the confusion matrix or determining the score of ROC AUC. They have to find out if those accuracies are good or not. Once the accuracy is good, they will move to the next stage.

  • Deployment of model

After the positive outcome on the accuracy, the next step is model deployment. There are various tools for the deployment of the model. One of the tools is Flask. It is a web framework that helps create a REST API and can consume from any front-end application.

  • Optimization of the model

Once there is the deployment of the model, the next step is to optimize the model. Here, the data scientists will set a month or days and see if the accuracy is good or not with actual test data. They will know the outcome of the accuracy after the model is being applied in the production.

If the model is not providing a good outcome, then a data scientist has to start the cycle again. The process continues until it finds the perfect model.

These are what a data scientist does. They will work closely with the stakeholders to understand their requirements. The data scientists design models or develop algorithms to extract data for business needs. It involves a lot of collecting and analyzing data.

What Is A Software Engineer?

Software engineering is one of the most popular branches of computer science. This branch includes the development of web pages, computer software, and applications. In general, everything related to computers and websites is the work of software engineers.

So what is a software engineer? What are the types of software engineers? How to become a software engineer? If you want to master this computer science branch or want to know how to become a software engineer, this article will shed some light on your conquest.

So without further ado, let’s dive deep.

Software engineers are experts in programming languages. They use their extensive programming knowledge and engineering principles to create software. These professionals perform every analysis to develop software tailored to suit any commercial or personal model.

Software engineers study work requirements and develop software in a systematic process. Skilled software engineers are a boon for any industry, which is why companies offer lucrative salaries and payments for their work.

Since technology is driving all industries for several reasons, the need for skillful software engineers has seen a sharp rise. Additionally, the need for technological solutions for commercial and personal purposes has added fuel for more software engineers.

Types of software engineers

Since software engineering covers a broader field, there are different types of software engineers. Some developers will have expertise in maintaining network security, while others will provide solutions for computer information systems.

You can classify software engineers into two primary types:

  • Systems software developers
  • Application software engineers

System Software Developers

System software developers primarily work on back-end development. These developers work with senior systems architects, data science professionals, development teams, and senior management to provide system software.

Here is an overview of their job profile:

  • Take care of both the software and hardware needs
  • Build networks and operating systems for user-facing applications
  • Fulfill the role of systems architects or IT managers
  • Combine different software products onto a single platform
  • Design IT standards and enforce them
  • Maintain IT documentation

Applications Software Developers

Applications software developers work on both front and back-end development. These developers work with project managers, graphic designers, customer service, and marketers to develop application software.

Here is an overview of their job profile:

  • Develop client-focused software products
  • Design interactive software for end-users
  • Conduct requirement analysis
  • Develop applications for different operating systems, including Android, iOS, Linux, and Windows
  • Maintaining and tweaking the software
  • Release updates regularly

How to become a software engineer?

To become a software engineer, one must be well-versed with the following qualities:

  • Computer science fundamentals
  • Coding and programming
  • Architecture and design
  • Data structures and algorithms
  • Debugging software
  • Testing software
  • Information analysis

While a traditional way of becoming a software engineer would be to complete a computer science degree, one can also use coding boot camps to start their journey. These boot camps can last for about 30 weeks depending on their programs and is a fast-forward way of entering the software engineering world.

Coding boot camps features include:

  • More focus on practical skills
  • Designed to focus on specific language
  • Covers technology fundamentals
  • Job preparation
  • Impart skills based on a particular industry and geography

Apart from a dedicated computer science degree and coding boot camps, other degree holders can also join software engineering. Science or math-related degrees, including civil and electronics engineering, can start their career as software developers. People with community college degrees have also found their way into software development.

It would be best to evaluate the type of job you want and choose your degree or program accordingly. Since every industry needs a software engineer, it will not be hard to get a job if you have a sound knowledge of programming skills.

What Is Machine Learning?

The rise of modern technology and its usage are exponential with a considerable margin. There are courses, learners, and people from the IT background trying to constantly develop better technologies and ideas of absorbing machine learning and concepts. Machine learning, in simple terms, is the branch of science that focuses on building applications, studying computer algorithms, improving programming, and using data for introducing new skills.

Without the proper knowledge of machine learning, it is impossible to create or understand the basics of artificial intelligence and new-age world concepts. This article will help in clearing doubts and present a glance at machine learning. The entire process can be intimidating, intriguing, and open doors to new possibilities.

The simplest explanation of how machine learning works

Machine learning is the form of computer science where artificial intelligence teaches computers the behavior of human nature. The intricate details can include thoughts, patterns, experiences, and understanding of people. It works when the AI explores data and involves minimal human interaction using various techniques on supervision to collect data from previous knowledge or find unknown patterns.

The whole process can be grouped into supervised and unsupervised learning categories equally exciting with differentiation. The supervised class will involve the learner collecting data on human nature or any other field and present them to the computer. Human beings usually learn about new things and experiences using the trial and error method. AI or machine learning works similarly through the input presented in the computer.

The developer can specify the information that can include various applications, data, and action-based input to plot graphs and predict the results. The unsupervised machine learning will consist of the algorithm trying to find meaning to inherent data structures and create modeling for training and influential purposes. In simpler terms, machine learning is purely related to mathematics that includes exciting calculations. The machine learning or algorithm depends on all the mathematical calculations that process in the computer.

Advantages of machine learning

Machine learning is an excellent step towards modernization where every action finds its authority at the smack of a button. There are facial detection apps, seamless financial transactions, robotics, and many more tech-savvy projects that are fantastic. Machine learning makes the task of combining AI to daily activities and makes task performance easier.

The algorithms can help in assimilating new data and provide accurate results. They can help accumulate and handle every piece of excellent information for businesses and multi-dollar giant apps like Amazon or Walmart. Machine learning will assist in recognizing trends and patterns for solving problems, analyzing buying patterns in companies, mathematical learning for apps, and engage the brain for more reinforced learning. Machine learning makes every task automatic that can save a lot of time, deliver instant results, draw conclusions on problems and save the day for the service providers.

Machine learning has a significant role in changing the world swiftly into a digitalized world that sees, feels, and understands the necessities of life. It is all about quick action that can double the normal process by spicing the entire life cycle into the palms of artificial intelligence.

Daily life applications of machine learning

Machine learning is the strong buzz in technology that is creating a ring of sensation for its application. Learning and having strong knowledge in machine learning is evident with the present generation to collect, evaluate and distribute data.

  • The simplest example of daily life application of machine learning can be using Google Maps for directions and assistance to find the right destination. Machine learning combines data of people using the services, track their average speed, location, and many other minute details to predict or adjust the route.
  • Machine learning creates buzz in all departments of daily use from medicine, defense, education, businesses, and even social media. Supervised learning is truly a gift to the medical department in detecting heart attacks or high pressure. Face detection, image recognition, speech recognition, or text conversion work incredibly to assist the client in providing quality information at lightning speed.
  • The random product recommendation, commuting through local taxis, or the recent entry of self-driving cars are all at the mercy of machine learning. Machine learning access is a significant step for accessible communication, transfusion of energy, and directs the social activities starting from Instagram or online video streaming like Netflix.

With the rise in technological activities and massive data, machine learning is crucial to solve issues and bring more explicit concepts and understanding. Machine learning is a necessity as it helps to simplify topics and provide answers. The learning process is versatile and famous in the recent media, with young adults accessing the process through various institutions. In the ever-changing world of artificial intelligence, it is essential to dive into possibilities and play with the algorithm to set data inputs and train models to predict and solve problems.

Various Databases Explained

Data is information or facts about any subject, which may relate to any object under consideration. People’s names, date of birth/age, weight, height etc. may be taken as data related to these people. An image, file, pdf may also be considered as data. Structured systems of data which can be stored (among other operations) are called Databases. Databases can be of various types, depending on application and usage. To have different types of databases explained is the purpose of this brief review that follows.

What is Database?

Data are the symbols characters or quantities, with which operations can be performed on a real or virtual computer or computerized system, and which may be transmitted and stored by way of electric signals, and documented on any recording media. Data may also be considered as information or characteristics (which are generally numerical or symbolic) which can be collected, processed, manipulated, transmitted or stored. Technically speaking, data is a set of values of quantitative or qualitative variables about single or multiple objects (or persons). A Data Base is a collection of data that is organized and stored in a form that makes retrieval, transmission, manipulation etc. possible inside the environment of the computerized system (cloud included). Data Bases render management easy. For example, the domestic electricity service provider uses a Data Base to manage and provide monthly billing, customer queries; problems and issues, and also to handle fault Data and restoration of power supply, amongst other matters. A typical Social Media, like Instagram or Facebook, needs to methodically store enormous masses of information related to Members, Member activities, friends of Members, Messages, and Advertisements and so on. An adult dating app has to store this information along with even more for matching. A hookup site like https://meetandfuck.co.uk utilizes massive amounts of various user data to match casual daters in real time. These are only some examples. The examples of uses of Data Bases in our daily life are too numerous to be listed here, and have contributed to a new science of DBMS (Data Base Management System).

Types of Data Bases

Data Bases are, in its simplest form containerized storage for Data. It could also be called a library of organized Data. Technically, Data Bases are computerized structures that store and save, organize and protect and transmit and deliver Data. A typical diagrammatic representation and symbol of a Data Base is a ‘Cylinder’. Following are the main different types of databases explained:

  • Relational Data Bases: These were most important in the 1980s, when the items of Data were organized tabularly, and displayed as arrays of columns and rows. This form of Structured Information allows highly accurate and speedy access to Data.
  • Object Oriented Data Bases: These Data Bases are organized around representations of objects.
  • Distributed Data Bases: Data Bases may, in this case, maybe located and stored in scattered physical locations, multiple computers and different Networks.
  • Data Warehouses: This form utilizes large centralized storage for Data in order to accommodate extremely fast analysis and query.
  • NoSQL Data Bases: These Data Bases took over from Relational Data Bases, as storage and manipulation of both Structured and Unstructured Data, side-by-side, became at once more common, and more complex. This is the most popular Data Base at present.
  • Graph Data Bases: These Data Bases store Data as entities and their relationships to each other.
  • OLTP Data Bases: This stands for Online Transactional Processing, and generally involves Inserting, Updating or Deleting small amounts of Data in a Data Base. OLTP is used to deal with large number of continuously upgraded transactions by a multiplicity of Users.

Data Bases are often confused with Spreadsheets, but they are not the same thing. Both are convenient ways to store information. But Spreadsheets, such as, Microsoft Office Excel, is different from Data Bases, in the manner in which Data is stored and manipulated. Access to the Data, as well as the amount of Data that can be stored is also limited for Spreadsheets, which was originally designed for one User only. It is obvious from the set up of a typical Spreadsheet that it cannot handle more than a maximum number of Users, and allow manipulations that are too complicated. Data Bases on the other hand can store and absorb vast amounts of Data that are almost unlimited. They allow a multiplicity of Users to access the Data stored quickly and securely and manipulate this Data, with incredibly complicated manipulations, logic and language.

Top Universities to Study Computer Science

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Computer science includes brief study of computers and its functions. Computers are the essential parts of everyone’s life in today’s era whether it’s in corporate industries, IT sectors, business, accounting, digital or entertainment industry, news or publishing media, schools, colleges or any private and government offices. Everything is digital nowadays. From saving a small text message to saving large legal or financial documents everybody needs computers. As we all know computers are mainly constructed of two main parts that are its hardware and software systems. In computer science students mostly study about its software system and its functions. Students of computer engineering study about its hardware system but in computer science they gain knowledge about its internal designs, working theories and applications development.

Benefits of Learning Computer Science:

In its study one would learn about computers and its programming languages, study its networking system, digital graphics, and database systems. ‘‘What is program making and computing theory”? It includes all. There is much other information in the field of computer science as it includes a large syllabus to study about. A large number of students around the world these days are enrolling for study of computer science. As in its study one can learn about artificial intelligence and numerical analysis too, so it is getting more knowledgeable and interesting for the learners. Besides all this information it also includes detailed study about vision and graphics, bioinformatics and human computer interaction. So computer science is a subject of computer systems, software engineering, its security and functions with vast theory of computing and database systems.

There are many scopes and fields opened for a computer science graduate.Many reputed multinational companies invite them to work with. Apart from this they can work in government sectors too by applying for the exams in related fields. Students also have scope in research agencies and science and technology departments if they want to do more and detailed study about that. As software designers are in high demand in every field today so many national and international companies hire them at very good packages. Study of graphics and software vision may lead you to reach top positions in technology and software industries. Government also wants software expertise in many reputed work fields like law and order agencies for detailed research about cyber crime and hacking control. Army, navy and air force also take exams to hire software programmers and engineers who should be excellent in their field. They are also in demand in space missions as programming scientists and aerospace researchers. So being a computer science graduate can lead you the way of many possible ways of financially independent as well as get a reputed identity in your field of work.

Universities for Computer Science study:

As students across the world of so many different countries come to enroll and study computer science so there are many universities which provide the best faculty, books and knowledge for the students across the world. Some universities take pre exams for enrolling for graduation in these universities. Universities like Stanford, Oxford have the best computer laboratories, trusted staff, excellent software technologies and syllabus to provide knowledge to learners. Apart from these, the University of Massachusetts and ETH Zurich also have brilliant computer science departments which include numerical algorithms and software intelligence studies. Some of these top universities also have collaboration with many reputed technology and IT companies like Google, IBM, Microsoft etc, which provide direct placements and interview calls for the graduates and undergraduates too. These institutions give advanced knowledge for research and practicals. They also teach video games designing, robotics design and quantum computing to the learners. They also provide courses in computational biology and software verification that give additional knowledge to them. University of Cambridge is also one of the best universities in the world of computer science training. This also gives opportunities to do PhD and MPhil courses to the graduated students in the relative subject that helps students to do research in many other fields and industries they want. These universities help students to be trained in the artificial intelligence, software programs building, their functions, construction, programming languages and their processes which give students a vast and expanded field to explore their careers.

What is AI Supercomputer TX-GAIA at MIT Lincoln Lab?

AI Supercomputer TX-GAIA

Think of the internet as a network that connects people through the web pages or chats. Presently, over 5 billion people will be connected to the internet and 2020 the numbers are expected to reach 25 billion with the global annual traffic expected to exceed the equivalent of 500 billion DVDs. Only powerful super computers are able to support massive rapid computations can cope with the ever-increasing amount of data.

So as to power the AI applications and research across science, engineering and medicine the Massachusetts institute of Technology (MIT) Lincoln laboratory supercomputing center has installed a new GPU-accelerated supercomputer that is powered by 896 NVIDIA Tensor Core V100 GPUs. It is ranked as the most powerful super computer in the world.

The introduction of Artificial Intelligence into the work place has brought diversity. The new super computer has a peak performance of 100 AI peta FLOPs as measured by the computing speed which is required to perform mixed precision floating point operations commonly known as deep neural networks.

The system features a measured performance of around 5 petaFLOPs and is based on the HPE Apollo 2000 system which is specifically designed for the HPC and optimized AI. Deep neural networks continue to grow in size and complexity with time.

The new TX-GAIA computing system at the Lincoln laboratory has been ranked as one of the most powerful artificial intelligence supercomputers in any university. The system which was built by Hewlett Packard Enterprise combines traditional high-performance computing hardware with almost 900 intel processors and hardware that is optimized for AI applications in addition to the use of Nvidia graphics processing applications.

Machine-learning supercomputer

The new TX-GAIA supercomputer is housed within the EcoPOD modular data center and was first revealed to the world in 2011. The system joins other machines in the same location including the TX-E1 which supports collaboration with MIT campus and other institutions. Researchers at the institution are thrilled to have the opportunity to achieve incredible scientific and engineering breakthroughs.

Top 500 ranking

Top 500 ranking is based on LINPACK Benchmark which is basically a measure of a system’s floating-point computing power or how fast a computer solves a dense system of linear equations. The TX-GAIA’s performance is 3.9 quadrillion floating-point operations per second. Or rather petaflops. It has a peak performance of 100 petaflops which makes top any other in any university in the world. A flop is basically a measure of how fast a computer can perform deep neutral network (DNN) operations. DNNs basically refer to a class of algorithms that learn to recognize patterns in huge amounts of data.

Artificial intelligence basically has given rise to various types of miracles in the world which include speech recognition and computer vision. It is this kind of technology that allows Amazon’s Alexa to understand the questions and self-driving cars to recognize objects in their surroundings. As the complexity of the DNNs grow so is the time it takes for them to process massive amounts of datasets. Nvidia GPU accelerators that are installed in TX-GAIA’s are specifically designed for performing these DNN operations quickly.


TX-GIAA is housed in a modular data center called an EcoPOD at the LLSC’s green, hydroelectrically powered site in Holyoke Massachusetts. It joins the ranks of some of the most powerful systems at the LLSC such as the TX-E1 which supports a collaboration with MIT campus and other users.

TX-GAIA will be tapped for training machine learning algorithms which include those that use DNNs. This implies that it will more likely crunch through terabytes of data for instance hundreds of thousands of images or years’ worth of speech. The systems computation power will be able to expedite simulations and data analysis and these capabilities will be able to support projects across R&D areas. This include improving weather forecasting, building autonomous system, accelerating medical analysis, designing synthetic DNA as well as in the development of new materials and devices.

Why supercomputing?

High-performance computing plays a very important role in promoting the scientific discovery and addressing of grand challenges as well as in the promotion of social and economic development. Over the past few decades, several developed countries have invested heavily into a series of key projects and development programs. The development of supercomputing systems has advanced parallel applications in various fields along with related software and technology.

Significance of super computing

A super computer is a high-performance computing which does not necessarily refer to a very large or powerful computer. A super computer comprises thousands of processors working together in parallel and it responds to the ever increasing need to process zillions of data in real time with quality and accuracy. HPC allows people to design and simulate effects of new drugs, provide faster diagnosis, better treatments and control epidemics as well as support in the decision-making process in areas such as water distribution, urban planning and electricity.

A supercomputer is of great benefit in a competitive industry as it helps in the digitization process. It also helps to direct benefits to our health in that super computers are able to detect genetic changes and it also comes in handy during weather forecasting.

The next wave of AI

The adoption of artificial intelligence has exploded in the last few years with virtually every kind of enterprise being on the rush to integrate and deploy AI methodologies in their core business practice. The first wave of artificial intelligence was characterized by small scale proof of concepts and deep learning implementations. In the next wave we will be able to see large scale deployments which are more evolved and concerted effort to apply to AI techniques in production to solve real world problems and drive business decisions.

Artificial Intelligence basically is a supercomputing problem and is expected to double in size within the next few years. AI thrives on massive data sets and there is a great convergence that occurs between AI and simulation. Most of the organizations that are performing simulation are increasingly adding machine and deep learning into their simulation.