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Distinguishing features

Historical development.

Cray-1

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Cray-1

supercomputer , any of a class of extremely powerful computers. The term is commonly applied to the fastest high-performance systems available at any given time. Such computers have been used primarily for scientific and engineering work requiring exceedingly high-speed computations. Common applications for supercomputers include testing mathematical models for complex physical phenomena or designs, such as climate and weather , evolution of the cosmos , nuclear weapons and reactors, new chemical compounds (especially for pharmaceutical purposes), and cryptology . As the cost of supercomputing declined in the 1990s, more businesses began to use supercomputers for market research and other business-related models.

Supercomputers have certain distinguishing features. Unlike conventional computers, they usually have more than one CPU (central processing unit), which contains circuits for interpreting program instructions and executing arithmetic and logic operations in proper sequence. The use of several CPUs to achieve high computational rates is necessitated by the physical limits of circuit technology . Electronic signals cannot travel faster than the speed of light , which thus constitutes a fundamental speed limit for signal transmission and circuit switching. This limit has almost been reached, owing to miniaturization of circuit components, dramatic reduction in the length of wires connecting circuit boards, and innovation in cooling techniques (e.g., in various supercomputer systems, processor and memory circuits are immersed in a cryogenic fluid to achieve the low temperatures at which they operate fastest). Rapid retrieval of stored data and instructions is required to support the extremely high computational speed of CPUs. Therefore, most supercomputers have a very large storage capacity, as well as a very fast input/output capability.

Technician operates the system console on the new UNIVAC 1100/83 computer at the Fleet Analysis Center, Corona Annex, Naval Weapons Station, Seal Beach, CA. June 1, 1981. Univac magnetic tape drivers or readers in background. Universal Automatic Computer

Still another distinguishing characteristic of supercomputers is their use of vector arithmetic—i.e., they are able to operate on pairs of lists of numbers rather than on mere pairs of numbers. For example, a typical supercomputer can multiply a list of hourly wage rates for a group of factory workers by a list of hours worked by members of that group to produce a list of dollars earned by each worker in roughly the same time that it takes a regular computer to calculate the amount earned by just one worker.

Supercomputers were originally used in applications related to national security, including nuclear weapons design and cryptography. Today they are also routinely employed by the aerospace, petroleum, and automotive industries. In addition, supercomputers have found wide application in areas involving engineering or scientific research, as, for example, in studies of the structure of subatomic particles and of the origin and nature of the universe. Supercomputers have become an indispensable tool in weather forecasting: predictions are now based on numerical models. As the cost of supercomputers declined, their use spread to the world of online gaming . In particular, the 5th through 10th fastest Chinese supercomputers in 2007 were owned by a company with online rights in China to the electronic game World of Warcraft , which sometimes had more than a million people playing together in the same gaming world.

Although early supercomputers were built by various companies, one individual, Seymour Cray , really defined the product almost from the start. Cray joined a computer company called Engineering Research Associates (ERA) in 1951. When ERA was taken over by Remington Rand, Inc. (which later merged with other companies to become Unisys Corporation ), Cray left with ERA’s founder, William Norris, to start Control Data Corporation (CDC) in 1957. By that time Remington Rand’s UNIVAC line of computers and IBM had divided up most of the market for business computers, and, rather than challenge their extensive sales and support structures, CDC sought to capture the small but lucrative market for fast scientific computers. The Cray-designed CDC 1604 was one of the first computers to replace vacuum tubes with transistors and was quite popular in scientific laboratories. IBM responded by building its own scientific computer, the IBM 7030—commonly known as Stretch—in 1961. However, IBM, which had been slow to adopt the transistor, found few purchasers for its tube-transistor hybrid, regardless of its speed, and temporarily withdrew from the supercomputer field after a staggering loss, for the time, of $20 million. In 1964 Cray’s CDC 6600 replaced Stretch as the fastest computer on Earth; it could execute three million floating-point operations per second (FLOPS), and the term supercomputer was soon coined to describe it.

Cray left CDC to start Cray Research, Inc., in 1972 and moved on again in 1989 to form Cray Computer Corporation. Each time he moved on, his former company continued producing supercomputers based on his designs.

Cray was deeply involved in every aspect of creating the computers that his companies built. In particular, he was a genius at the dense packaging of the electronic components that make up a computer. By clever design he cut the distances signals had to travel, thereby speeding up the machines. He always strove to create the fastest possible computer for the scientific market, always programmed in the scientific programming language of choice ( FORTRAN ), and always optimized the machines for demanding scientific applications—e.g., differential equations , matrix manipulations, fluid dynamics , seismic analysis , and linear programming .

Among Cray’s pioneering achievements was the Cray-1 , introduced in 1976, which was the first successful implementation of vector processing (meaning, as discussed above, it could operate on pairs of lists of numbers rather than on mere pairs of numbers). Cray was also one of the pioneers of dividing complex computations among multiple processors, a design known as “ multiprocessing .” One of the first machines to use multiprocessing was the Cray X-MP, introduced in 1982, which linked two Cray-1 computers in parallel to triple their individual performance. In 1985 the Cray-2, a four-processor computer, became the first machine to exceed one billion FLOPS.

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While Cray used expensive state-of-the-art custom processors and liquid immersion cooling systems to achieve his speed records, a revolutionary new approach was about to emerge. W. Daniel Hillis , a graduate student at the Massachusetts Institute of Technology , had a remarkable new idea about how to overcome the bottleneck imposed by having the CPU direct the computations between all the processors. Hillis saw that he could eliminate the bottleneck by eliminating the all-controlling CPU in favour of decentralized, or distributed, controls. In 1983 Hillis cofounded the Thinking Machines Corporation to design, build, and market such multiprocessor computers. In 1985 the first of his Connection Machines, the CM-1 (quickly replaced by its more commercial successor, the CM-2), was introduced. The CM-1 utilized an astonishing 65,536 inexpensive one-bit processors, grouped 16 to a chip (for a total of 4,096 chips), to achieve several billion FLOPS for some calculations—roughly comparable to Cray’s fastest supercomputer.

Hillis had originally been inspired by the way that the brain uses a complex network of simple neurons (a neural network ) to achieve high-level computations. In fact, an early goal of these machines involved solving a problem in artificial intelligence , face-pattern recognition. By assigning each pixel of a picture to a separate processor, Hillis spread the computational load, but this introduced the problem of communication between the processors. The network topology that he developed to facilitate processor communication was a 12-dimensional “ hypercube ”—i.e., each chip was directly linked to 12 other chips. These machines quickly became known as massively parallel computers. Besides opening the way for new multiprocessor architectures, Hillis’s machines showed how common, or commodity, processors could be used to achieve supercomputer results.

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Another common artificial intelligence application for multiprocessing was chess . For instance, in 1988 HiTech, built at Carnegie Mellon University , Pittsburgh, Pa., used 64 custom processors (one for each square on the chessboard) to become the first computer to defeat a grandmaster in a match. In February 1996 IBM’s Deep Blue , using 192 custom-enhanced RS/6000 processors, was the first computer to defeat a world champion, Garry Kasparov , in a “slow” game. It was then assigned to predict the weather in Atlanta, Ga., during the 1996 Summer Olympic Games . Its successor (now with 256 custom chess processors) defeated Kasparov in a six-game return match in May 1997.

As always, however, the principal application for supercomputing was military. With the signing of the Comprehensive Test Ban Treaty by the United States in 1996, the need for an alternative certification program for the country’s aging nuclear stockpile led the Department of Energy to fund the Accelerated Strategic Computing Initiative (ASCI). The goal of the project was to achieve by 2004 a computer capable of simulating nuclear tests —a feat requiring a machine capable of executing 100 trillion FLOPS (100 TFLOPS; the fastest extant computer at the time was the Cray T3E, capable of 150 billion FLOPS). ASCI Red, built at Sandia National Laboratories in Albuquerque, N.M., with the Intel Corporation , was the first to achieve 1 TFLOPS. Using 9,072 standard Pentium Pro processors, it reached 1.8 TFLOPS in December 1996 and was fully operational by June 1997.

While the massively multiprocessing approach prevailed in the United States, in Japan the NEC Corporation returned to the older approach of custom designing the computer chip—for its Earth Simulator, which surprised many computer scientists by debuting in first place on the industry’s TOP500 supercomputer speed list in 2002. It did not hold this position for long, however, as in 2004 a prototype of IBM’s Blue Gene/L, with 8,192 processing nodes, reached a speed of about 36 TFLOPS, just exceeding the speed of the Earth Simulator. Following two doublings in the number of its processors, the ASCI Blue Gene/L, installed in 2005 at Sandia National Laboratories in Livermore , Calif., became the first machine to pass the coveted 100 TFLOPS mark, with a speed of about 135 TFLOPS. Other Blue Gene/L machines, with similar architectures, held many of the top spots on successive TOP500 lists. With regular improvements, the ASCI Blue Gene/L reached a speed in excess of 500 TFLOPS in 2007. These IBM supercomputers are also noteworthy for the choice of operating system , Linux , and IBM’s support for the development of open source applications.

The first computer to exceed 1,000 TFLOPS, or 1 petaflop, was built by IBM in 2008. Known as Roadrunner, for New Mexico’s state bird, the machine was first tested at IBM’s facilities in New York , where it achieved the milestone, prior to being disassembled for shipment to the Los Alamos National Laboratory in New Mexico. The test version employed 6,948 dual-core Opteron microchips from Advanced Micro Devices (AMD) and 12,960 of IBM’s Cell Broadband Engines (first developed for use in the Sony Computer Entertainment PlayStation 3 video system). The Cell processor was designed especially for handling the intensive mathematical calculations needed to handle the virtual reality simulation engines in electronic games —a process quite analogous to the calculations needed by scientific researchers running their mathematical models .

See researchers simulate the 3-D motion of the human rhinovirus using IBM Blue Gene Q supercomputer to understand how the virus works

Such progress in computing placed researchers on or past the verge of being able, for the first time, to do computer simulations based on first-principle physics—not merely simplified models. This in turn raised prospects for breakthroughs in such areas as meteorology and global climate analysis, pharmaceutical and medical design, new materials, and aerospace engineering . The greatest impediment for realizing the full potential of supercomputers remains the immense effort required to write programs in such a way that different aspects of a problem can be operated on simultaneously by as many different processors as possible. Even managing this in the case of less than a dozen processors, as are commonly used in modern personal computers , has resisted any simple solution, though IBM’s open source initiative , with support from various academic and corporate partners, made progress in the 1990s and 2000s.

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The vast resources of old working computers are getting bigger and bigger. Companies that consider these old computers as obsolete but still working put these computers to the storage, or worse, junk. Different entities are more and more relying on supercomputers to replace their ordinary and old computers. Supercomputers have become a basic necessity in this generation. It has a wide variety of functions that make our daily work easier. Having a well-built supercomputer with excellent specifications is a must; however, it comes with a cost. The computers can be clustered to create a supercomputer. The computers used in these clustered supercomputers have the exact specifications, such as uniform models with the same number of cores per processor and the same memory capacity. This study focuses on creating a supercomputer using different processors with a different number of cores per processor, processor speed, and different memory capacity. This study can be a valuable system for entities that have old computers in their workplace. They can reuse their old computers and combine them with other computers to create a supercomputer, thus reducing waste and recycling old resources. This study will be using different types of Raspberry Pi (RPi) computers combining old and new models to simulate the scenario of combining multi-core processors in creating supercomputers.

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Supercomputers: The Amazing Race

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CDC 6600 Cordwood modules with approximately xxx transistors per module of the yy modules,

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28 Free Technology PowerPoint Templates for Presentations

If you’re amongst the science and technology teachers, students, or businesses in the field; we have something for you. We deep-dived to find the best free technology PowerPoint templates for your presentation, so today’s collection has 28 amazing designs to choose from.

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A technology-themed template for presentations on consulting, IT, software, and other related subjects. The pixel pattern is grouped by tones which you can change from the master slides.

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This free Powerpoint template is perfect for a presentation about the internet, blockchain, machine learning, cybersecurity, or cloud computing.

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15. Free PowerPoint Template with Techy Contour Lines

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This design has an abstract contour lines background in a dark green color. Ideal for subjects like geography, technology, video games, or even military affairs.

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The isometric design has illustrations on business, marketing, and technology topics that will make every slide stand out.

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For presentations related to scientific or technological topics, with professional hexagonal design.

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Free Rockets Taking Off  PowerPoint Template

Rockets taking off is a great metaphor for growing businesses. It’s also a symbol of progress and technology.

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Smart City offers a futuristic design for subjects such as internet communication, smart city concepts, and tech innovation.

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The perfect template for presentations on cybersecurity, antivirus software, and other related topics.

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Free BlockChain PowerPoint Templates

This template is a 3D rendering design of blockchain technology and you can use it for a variety of purposes.

Presentation Design Tips You Wish You Knew Earlier:

The shorter you keep the text, the better. In fact, some specialists suggest that you shouldn’t use more than 5-6 words per slide . And sometimes, a single word combined with a powerful visual is enough to nail the attention of the people sitting in front of you and make them listen to what you have to say.

22. BitCoin Themed Free PowerPoint Template

Free BitCoin Themed PowerPoint Template

A very versatile template that includes 20 semi-transparent illustrations of different concepts: security, social networks, programming, bitcoin.

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23. Technical Blueprint Free Technology PowerPoint Template

Free Technical Blueprint Technology PowerPoint Template

This template uses a blueprint style and a monospaced font to emulate the technical drawings used in construction and industry.

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24. Blue Connections Free PowerPoint Template

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The design of this free template fits social media, connection, internet, cloud computing, and science-related topics.

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25. Cute Robots Free PowerPoint Template

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Here we have a colorful design with beautifully illustrated robots for presentation on technology, science, and physics.

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26. Green Circuit Free PowerPoint Template

Free Green Circuit PowerPoint Template

This is a free template with futuristic vibes that you can use for your tech presentations both in PowerPoint and Google Slides.

27. Data Particles Free Technology PowerPoint Template

Free Data Particles Technology PowerPoint Template

The design with particle lines gives it a modern and slightly technological look.

28. Science Hexagons Free Technology PowerPoint Template

Free Science Hexagons Technology PowerPoint Template

The background gradients highlight the white text, and the hexagons give it a techie style.

Final Words

That’s it. Today’s collection covered the best free technology PowerPoint templates that you can download and adapt to your presentations related to science, technology, programming, engineering, and physics. Now all you need to do is open your PowerPoint and make the most amazing presentation your viewers have ever seen.

For more freebies, you can check the Best Free Powerpoint Templates of 2022  or see these related articles:

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Meet two of the most powerful supercomputers on the planet

Summit and Sierra are supercomputers at Oak Ridge National Laboratory and Lawrence Livermore National Laboratory. They're helping us model supernovas, pioneer new materials and explore cancer, genetics and the environment — using technologies available to all businesses.

Explore high-performance computing for business

Summit has joined the fight against COVID-19. See what else IBM is doing.

What are Summit and Sierra?

A new kind of supercomputer, designed for data and ai.

Learn about the architecture

Built to tackle the world’s biggest challenges

Discover real-world applications

Supercomputing at IBM: A brief history

See landmarks on the path to Summit and Sierra

Summit by the numbers

Quadrillion calculations per second, petabytes storage capacity, ibm power9 cpus, gigabytes per second between nodes, nvidia tesla gpus, high-performance computing.

HPC provides the computing power to advance AI and to solve big challenges in business, medicine, science and engineering

In March 2014, the U.S. Department of Energy awarded IBM the commission to build two supercomputers.

The mission was to develop a machine 5 to 10 times faster than its predecessor, Titan. Over four years, the DOE worked with a team of IBMers to break through countless technology barriers to build Summit and Sierra — systems with the brains of AI and deep learning, and the brawn of 200 petaflops and 125 petaflops, respectively. One million times more powerful than the fastest laptop, these supercomputers can sift through thousands and thousands of variables and create models and simulations that can help researchers find answers to the world’s most complex problems.

Summit and Sierra represent a major shift from how IBM structured previous systems. IBM developed a new computing architecture that combines high-performance POWER9 CPUs with AI-optimized GPUs from our partner NVIDIA — all linked at extremely high speeds and bandwidth.

In the supercomputers’ new architecture, compute is embedded everywhere data resides, producing incredible speed and creating a system purpose-built for AI. “By building these supercomputers, we are building the world’s leading AI machines,” says Hillery Hunter, IBM Fellow; Director, Accelerated Cognitive Infrastructure.

Another radical shift is that the two machines are built with components available to any enterprise — this technology is part of IBM’s product line, available to accelerate every business.

“ Summit and Sierra will push the boundaries of computing and human understanding. ” — Ginni Rometty, Former Chairman, President, and CEO, IBM

What will we do with 200 petaflops? Here are three sample applications:

Combat cancer

The arrival of these new supercomputers gives researchers a powerful boost in the fight against cancer. For example, machine learning algorithms scaled on Summit will help supply medical researchers with a comprehensive view of the U.S. cancer population at a level of detail typically obtained only for clinical trial patients.

Identify next-generation materials

Deep learning could help scientists identify materials for better batteries, more resilient building materials and more efficient semiconductors. By training algorithms to predict materials’ properties, researchers may answer longstanding questions about materials’ behaviors at atomic scales.

Accelerate understanding of disease

Using a mix of AI techniques, researchers will be able to identify patterns in the function, cooperation, and evolution of human proteins and cellular systems. Greater understanding of how these patterns give rise to clinical phenotypes — observable traits of diseases such as Alzheimer’s, heart disease and addiction — will inform the drug discovery process.

Supercomputing at IBM: A brief history

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Use these google slides themes or download our ppt files for powerpoint or keynote to give a presentation about a computer-related topic, including information technology..

Types of Internet Connection presentation template

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Interactive Classroom Icebreakers

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Computer Science Degree for College presentation template

Computer Science Degree for College

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Cyber Security Kill Chain presentation template

Cyber Security Kill Chain

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AI Tech Types and Tools presentation template

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All About Programming in Java presentation template

All About Programming in Java

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Virtual Slides for Education Day presentation template

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5G Infrastructure Consulting presentation template

5G Infrastructure Consulting

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Silicon Valley Programmer Portfolio presentation template

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Soft Colors UI Design for Agencies presentation template

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Cloud Computing Lesson for College presentation template

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Web Project Proposal presentation template

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Beyond Nvidia: Can Super Micro Computer Be the King of Artificial Intelligence (AI) Hardware?

  • Super Micro Computer builds highly customizable servers.
  • The company has benefited mightily from the AI demand boom.
  • Still, the stock isn't cheap even when considering management's goals.
  • Motley Fool Issues Rare “All In” Buy Alert

NASDAQ: SMCI

Super micro computer.

Super Micro Computer Stock Quote

Supermicro's revenue just tripled in its latest quarter.

I'll be upfront: Nvidia is the artificial intelligence (AI) king. There's not a lot of evidence to dispute this claim if you consider its reach, business growth, and the universal nature of its graphics processing units (GPUs) that power AI.

However, if you move beyond Nvidia, there's still another layer of successful businesses riding the same wave. One of those is Super Micro Computer ( SMCI -0.26% ) . Often referred to as Supermicro, the company builds highly customizable servers that house Nvidia's GPUs to give its clients the computing power they need to run complex models.

So, could Supermicro be the secondary king of AI? And should it be in your stock portfolio?

Strong growth, but not as much as some would like

Before the AI boom, Supermicro's business wasn't all that exciting. Although its servers could be tailored to running AI models, conducting drug research, or engineering calculations, that market wasn't that large. However, once the AI arms race kicked off, Supermicro experienced rapid growth in demand for its servers dedicated to AI computing.

But not everyone is a Supermicro customer. Unlike Nvidia, whose superior products stand apart from the competition, Supermicro has multiple competitors. Hewlett-Packard and Dell are two Supermicro competitors, but their offerings aren't as modular as Supermicro's.

As a result, the market for Supermicro's services is much smaller than that of Nvidia. Still, that doesn't mean it can't be a successful business. Revenue in the third quarter of Supermicro's fiscal 2024 (ended March 31) rose 200% year over year to $3.85 billion.

That's incredible growth, but investors found one metric to nitpick: quarterly growth. Revenue was only up 5% quarter over quarter, which indicates that demand isn't rapidly expanding for Supermicro. Compared to Nvidia's Q1 FY 2025 (ending April 28) revenue growth of 18%, Supermicro isn't seeing similar product interest levels.

For Q4, management expects about $5.3 billion in sales, which would deliver strong quarter-over-quarter growth, but year-over-year growth would decelerate to about 143%. But this brings us to an interesting point: Supermicro's market opportunity.

Management's revenue goal is within reach

Also competing with Supermicro are the cloud computing providers . If a business doesn't want to invest capital in building its own server to access the computing power needed to run AI models, it could rent that power from a cloud computing provider like Amazon Web Services or Microsoft Azure. However, this business also has a partnership aspect, as Supermicro provides some of the hardware for the big cloud computing companies.

As a result, management has been upfront with investors about what it believes its market opportunity is. Supermicro set a goal of $25 billion in revenue, and if it hits its Q4 revenue target, it will have nearly $15 billion in trailing-12-month revenue. Although management has raised its revenue goal once already (increasing it from $20 billion), if Supermicro hits its revenue goal, it will still be a very expensive stock.

SMCI Revenue (TTM) Chart

SMCI Revenue (TTM) data by YCharts

Should Supermicro generate $25 billion in annual revenue combined with its Q3 profit margin of 10.5% , it would generate about $2.63 billion in profits. At Supermicro's current market capitalization of $45 billion, that would price the stock at 17 times forward earnings.

That's not a very expensive price, but if that's the business's ceiling, investors may want to pass. Individual investors aim to find strong companies that can compound for years to come. The current AI boom will likely be peak business for Supermicro, so I think investors should turn elsewhere to find other opportunities.

Thanks to AI, Supermicro will be a successful company and grow tremendously; the problem is that most of that growth is already priced in.

John Mackey, former CEO of Whole Foods Market, an Amazon subsidiary, is a member of The Motley Fool's board of directors. Keithen Drury has positions in Amazon. The Motley Fool has positions in and recommends Amazon, Microsoft, and Nvidia. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy .

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super computers

Super Computers

Apr 07, 2019

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Super Computers. By Phuong Vo. What is a Supercomputer?. Supercomputer is a broad term for one of the fastest computer currently available. Super computers were designed and built to work on extremely large jobs that could not be handled by no other types of computing systems.

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Presentation Transcript

Super Computers By Phuong Vo

What is a Supercomputer? Supercomputer is a broad term for one of the fastest computer currently available. Super computers were designed and built to work on extremely large jobs that could not be handled by no other types of computing systems.

What is a Supercomputer? (cont’d) Supercomputers speed are measured in floating point operations per second (FLOPS) in units of • megaflops (MFOPS) • gigaflops (GFLOPS) • teraflops (TFLOPS)

What are Supercomputers Used For? • scientific simulations • animated graphics • analysis of geological data • nuclear energy research and meteorology • computational fluid dynamics

Early History Supercomputer. • Seymour Clay made the first transistorized computer (CDC 1604) in 1964. • In 1965 Cray developed CDC 6600, the first multithreading computer. Cray then developed the CDC 7600 in 1970. • Cray-1 supercomputers project started in 1972 and finished in 1974 and was twice as fast as the 7600 with a vector speed of 80 MFLOPS.

Early History of Supercomputer. (cont’d) • In 1990 Cray successful build Cray-4 the fastest supercomputer in the world at around 10 gigaflops and was smaller than a human brain. • Cray died on October 5th 1996 at the age of 71 as a result of injuries sustained in an automobile accident. • Currently the fastest computer is IBM BlueGene/L at around 73 teraflops (TFLOPS)

Parallel Processing Two common type used in supercomputers. • Cluster • Multi-Processor

Cluster Computers Cluster computers are two or more computers working parallel to achieve greater performances. Cluster computers breakup work among the computers in the cluster.

Cluster Computers (cont’d) • Each computer in the cluster is a cpu itself with its own processor, memory, and disk. • The computers communicate with each other via an interconnecting bus.

Xbox Cluster www.anandtech.com successfully built a XBOX Linux cluster consisting of 8 XBOXs. XBOX specs: • PIII 733MHz 128K L2 • Seagate 5400RPM 8GB • 64MB Shared PC3200

Xbox Cluster (cont’d) Performance Benchmarks Distributed Compiling Distributed Rendering

Multi-Processor A multi-processor computer has 2 or more cpus. Each processor is capable of running different program simultaneously (true multitasking).

Multi-Processor (cont’d) • The cpus all shared the other parts of the computers: memory, disk system, bus, etc. • Cpu communicate via memory and the system bus. • Cheaper than cluster computers but does not perform as well.

Advantages of Parallel Processing • Costs are much cheaper than building one supercomputer. • Highly scalable.

Disadvantages of parallel processing. • High overload – it is difficult to make many processor work together efficiently. • It is difficult to write programs to utilize multiple processors at once in an efficient manner.

References • www.anandtech.com • http://ei.cs.vt.edu/~history/SUPERCOM.Calle.HTML • http://www.cs.sjsu.edu/~lee/cs147/Super%20Computers%20---Parallel%20Computers.ppt

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Invitation - presentation of Husqvarna Group's report for the second quarter 2024

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STOCKHOLM , June 20, 2024 /PRNewswire/ -- Husqvarna Group will publish its second quarter results on July 18, 2024 , at approximately 07:00 CET . A combined webcast and conference call hosted by CEO Pavel Hajman and CFO Terry Burke will be held at 10:00 CET the same day. The presentation will be held in English and is followed by a Q&A session.

Link to the webcast:

husqvarnagroup.creo.se/8080f1ee-635c-4505-8f81-a5c393bb2868

To join the telephone conference: Sweden : +46 (0)8 505 100 31 United Kingdom : +44 (0) 207 107 06 13 United States : +1 (1) 631 570 56 13

The report and presentation will also be available on the Group's website: www.husqvarnagroup.com/investors .

For additional information, please contact: Investors Johan Andersson , Vice President Investor Relations +46 702 100 451 [email protected]  

Media Henrik Sjöström, Head of External Communications +46 727 15 77 85 [email protected]  

This information was brought to you by Cision http://news.cision.com

https://news.cision.com/husqvarna-group/r/invitation---presentation-of-husqvarna-group-s-report-for-the-second-quarter-2024,c3999287

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Invitation â€" presentation of Husqvarna Group’s report for the second quarter 2024  

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Microsoft Research Blog

Microsoft at cvpr 2024: innovations in computer vision and ai research.

Published June 17, 2024

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Microsoft is proud to sponsor the 41st annual Conference on Computer Vision and Pattern Recognition (CVPR 2024), held from June 17 to June 21. This premier conference covers a broad spectrum of topics in the field, including 3D reconstruction and modeling, action and motion analysis, video and image processing, synthetic data generation, neural networks, and many more. This year, 63 papers from Microsoft have been accepted, with six selected for oral presentations. This post highlights these contributions.

The diversity of these research projects reflects the interdisciplinary approach that Microsoft research teams have taken, from techniques that precisely recreate 3D human figures and perspectives in augmented reality (AR) to combining advanced image segmentation with synthetic data to better replicate real-world scenarios. Other projects demonstrate how researchers are combining machine learning with natural language processing and structured data, developing models that not only visualize but also interact with their environments. Collectively, these projects aim to improve machine perception and enable more accurate and responsive interactions with the world. 

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Microsoft Research Forum

Join us for a continuous exchange of ideas about research in the era of general AI. Watch the first three episodes on demand.

Oral presentations 

Bioclip: a vision foundation model for the tree of life.

Samuel Stevens, Jiaman Wu, Matthew J Thompson, Elizabeth G. Campolongo, Chan Hee Song, David Carlyn,  Li Dong , W. Dahdul, Charles Stewart, Tanya Y. Berger-Wolf, Wei-Lun Chao, Yu Su  

The surge in images captured from diverse sources—from drones to smartphones—offers a rich source of biological data. To harness this potential, we introduce TreeOfLife-10M, the largest and most diverse ML-ready dataset of biology images, and BioCLIP, a foundation model intended for the biological sciences. BioCLIP, utilizing the TreeOfLife-10M’s vast array of organism images and structured knowledge, excels in fine-grained biological classification, outperforming existing models by significant margins and demonstrating strong generalizability. 

EgoGen: An Egocentric Synthetic Data Generator

Gen Li, Kaifeng Zhao, Siwei Zhang, Xiaozhong Lyu, Mihai Dusmanu , Yan Zhang, Marc Pollefeys  

A critical challenge in augmented reality (AR) is simulating realistic anatomical movements to guide cameras for authentic egocentric views. To overcome this, the authors developed EgoGen, a sophisticated synthetic data generator that not only improves training data accuracy for egocentric tasks but also refines the integration of motion and perception. It offers a practical solution for creating realistic egocentric training data, with the goal of serving as a useful tool for egocentric computer vision research. 

Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks

Bin Xiao , Haiping Wu, Weijian Xu, Xiyang Dai , Houdong Hu, Yumao Lu, Michael Zeng , Ce Liu, Lu Yuan  

Florence-2 introduces a unified, prompt-based vision foundation model capable of handling a variety of tasks, from captioning to object detection and segmentation. Designed to interpret text prompts as task instructions, Florence-2 generates text outputs across a spectrum of vision and vision-language tasks. This model’s training utilizes the FLD-5B dataset, which includes 5.4 billion annotations on 126 million images, developed using an iterative strategy of automated image annotation and continual model refinement.

LISA: Reasoning Segmentation via Large Language Model

Xin Lai, Zhuotao Tian, Yukang Chen, Yanwei Li, Yuhui Yuan , Shu Liu, Jiaya Jia

This work introduces reasoning segmentation , a new segmentation task using complex query texts to generate segmentation masks. The authors also established a new benchmark, comprising over a thousand image-instruction-mask data samples, incorporating intricate reasoning and world knowledge for evaluation. Finally, the authors present Large Language Instructed Segmentation Assistant (LISA), a tool that combines the linguistic capabilities of large language models with the ability to produce segmentation masks. LISA effectively handles complex queries and shows robust zero-shot learning abilities, further enhanced by minimal fine-tuning.

MultiPly: Reconstruction of Multiple People from Monocular Video in the Wild

Zeren Jiang, Chen Guo, Manuel Kaufmann, Tianjian Jiang, Julien Valentin (opens in new tab) , Otmar Hilliges, Jie Song  

MultiPly is a new framework for reconstructing multiple people in 3D from single-camera videos in natural settings. This technique employs a layered neural representation for the entire scene, refined through layer-wise differentiable volume rendering. Enhanced by a hybrid instance segmentation that combines self-supervised 3D and promptable 2D techniques, it provides reliable segmentation even with close interactions. The process uses confidence-guided optimization to alternately refine human poses and shapes, achieving high-fidelity, consistent 3D models.

SceneFun3D: Fine-Grained Functionality and Affordance Understanding in 3D Scenes

Alexandros Delitzas, Ayça Takmaz, Federico Tombari, Robert Sumner, Marc Pollefeys , Francis Engelmann  

Traditional 3D scene understanding methods are heavily focused on 3D sematic and instance segmentation, but the true challenge lies in interacting with functional interactive elements like handles, knobs, and buttons to achieve specific tasks. Enter SceneFun3D: a robust dataset featuring over 14,800 precise interaction annotations across 710 high-resolution real-world 3D indoor scenes. This dataset enriches scene comprehension with motion parameters and task-specific natural language descriptions, facilitating advanced research in functionality segmentation, task-driven affordance grounding, and 3D motion estimation.

Discover more about our work and contributions to CVPR 2024, including our full list of publications and sessions , on our conference webpage . 

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HoloAssist: A multimodal dataset for next-gen AI copilots for the physical world

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  1. Super computer ppt

    Super computer ppt. 1. Prepared by: Karan Jani - Thakur Polytechnic Ritik Dhedia - Thakur Polytechnic. 2. A supercomputer is a computer with great speed and memory. This kind of computer can do jobs faster than any other computer of its generation. They are usually thousands of times faster than ordinary personal computers made at that time.

  2. Presentation on computer

    Presentation on computer. Apr 5, 2014 • Download as PPT, PDF •. 114 likes • 381,552 views. AI-enhanced description. Nausad Ahamed. This document provides an overview of computers, including: 1. It classifies computers into microcomputers, minicomputers, mainframe computers, and supercomputers based on their processing ability and speed. 2.

  3. Supercomputer ppt

    1. Supercomputers are the fastest and most powerful computers available and are designed to solve complex mathematical problems quickly. 2. They are used for applications like weather forecasting, computational chemistry, and nuclear simulations. 3.

  4. PPT SUPERCOMPUTERS

    SUPERCOMPUTERS. SUPERCOMPUTERS. PRIYADHARSHINI S. HISTORY First supercomputer built by Seymour Cray in Control Data Corporation (CDC) in 1957 CDC 1604 one of the first computers to replace vacuum tubes with transistors IBM responded with IBM 7030 (or Stretch) in 1961 In 1964, Cray's CDC 6600 replaced Stretch as the fastest computer on earth ...

  5. Supercomputer

    supercomputer, any of a class of extremely powerful computers. The term is commonly applied to the fastest high-performance systems available at any given time. Such computers have been used primarily for scientific and engineering work requiring exceedingly high-speed computations. Common applications for supercomputers include testing ...

  6. What Is Supercomputing?

    The term supercomputer came into use in the early 1960s, when IBM rolled out the IBM 7030 Stretch, and Sperry Rand unveiled the UNIVAC LARC, the first two intentional supercomputers designed to be more powerful than the fastest commercial machines available at the time.Events that influenced the progress of supercomputing began in the late 1950s when the US government began regularly funding ...

  7. (PPT) Super Computer Presentation

    Having a well-built supercomputer with excellent specifications is a must; however, it comes with a cost. The computers can be clustered to create a supercomputer. The computers used in these clustered supercomputers have the exact specifications, such as uniform models with the same number of cores per processor and the same memory capacity.

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    Defining Supercomputers • High performance computers mainly for scientific computation. • Huge amount of computation for Biochemistry, Physics, Astronomy, Meteorology and etc. • Very expensive: developed and managed by national fund. • High level techniques are required to develop and manage them.

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    The first commercially available supercomputers reached speeds of 10 to 100 million FLOPS. • 1teraflop = 1012 flops • IBM's Blue Gene/L - 360 teraflops • IBM's BGW - 115 teraflops • IBM's ASC Purple - 93 teraflops. Today's Supercomputers • Fastest supercomputers include IBM's Blue Gene and ASCI Purple, SCC's Beowulf, and Cray's SV2.

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    Supercomputers are the largest, fastest and most powerful computers built for scientific applications requiring intensive mathematical calculations. They were first developed in the 1960s for the US Department of Defense and have since been used for weather forecasting, seismic analysis and other computationally demanding tasks.

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    4. Supercomputer. Dictionary definition. a very fast, powerful mainframe computer, used in. advanced military and scientific applications. For our sake. Computer SYSTEM designed to operate at super high. speeds which can perform complex calculations. that would require greater than a year for a.

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    Presentation Transcript. Daniel Shin CS 147, Section 1 April 29, 2010 Supercomputers. What is a Supercomputer • A computer that is at the frontline of current processing capacity, particularly speed of calculation • Today's supercomputer tend to become tomorrow's ordinary computer • Designed to perform complex calculations at super high ...

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    Early History of Supercomputer. (cont'd) • In 1990 Cray successful build Cray-4 the fastest supercomputer in the world at around 10 gigaflops and was smaller than a human brain. • Cray died on October 5th 1996 at the age of 71 as a result of injuries sustained in an automobile accident. • Currently the fastest computer is IBM BlueGene/L ...

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  29. Basics of Computer.ppt

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