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-The most complex computers.
-This computers are used by people that need to process large amount of data.

USED TO:
-Solve complex numerical problems.
-Make forecast
-Analyse scientific or engineering problems.

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The IBM Blue Gene/P supercomputer "Intrepid" at Argonne National Laboratory runs 164,000 processor cores using normal data center air conditioning, grouped in 40 racks/cabinets connected by a high-speed 3D torus network.[1][2]

Computing power of the top 1 supercomputer each year, measured in FLOPS

A supercomputer is a computer with a high level of performance as compared to a general-purpose computer. The performance of a supercomputer is commonly measured in floating-point operations per second ( FLOPS) instead of million instructions per second (MIPS). Since 2017, supercomputers have existed which can perform over 10 ¹⁷ FLOPS (a hundred quadrillion FLOPS, 100 petaFLOPS or 100 PFLOPS). For comparison, a desktop computer has performance in the range of hundreds of gigaFLOPS (10 ¹¹) to tens of teraFLOPS (10 ¹³). Since November 2017, all of the world's fastest 500 supercomputers run on Linux-based operating systems. Additional research is being conducted in the United States, the European Union, Taiwan, Japan, and China to build faster, more powerful and technologically superior exascale supercomputers.

Supercomputers play an important role in the field of computational science, and are used for a wide range of computationally intensive tasks in various fields, including quantum mechanics, weather forecasting, climate research, oil and gas exploration, molecular modeling (computing the structures and properties of chemical compounds, biological macromolecules, polymers, and crystals), and physical simulations (such as simulations of the early moments of the universe, airplane and spacecraft aerodynamics, the detonation of nuclear weapons, and nuclear fusion). They have been essential in the field of cryptanalysis.

Supercomputers were introduced in the 1960s, and for several decades the fastest was made by Seymour Cray at Control Data Corporation (CDC), Cray Research and subsequent companies bearing his name or monogram. The first such machines were highly tuned conventional designs that ran more quickly than their more general-purpose contemporaries. Through the decade, increasing amounts of parallelism were added, with one to four processors being typical. In the 1970s, vector processors operating on large arrays of data came to dominate. A notable example is the highly successful Cray-1 of 1976. Vector computers remained the dominant design into the 1990s. From then until today, massively parallel supercomputers with tens of thousands of off-the-shelf processors became the norm.

The US has long been the leader in the supercomputer field, first through Cray's almost uninterrupted dominance of the field, and later through a variety of technology companies. Japan made major strides in the field in the 1980s and 90s, with China becoming increasingly active in the field. As of May 2022, the fastest supercomputer on the TOP500 supercomputer list is Frontier, in the US, with a LINPACK benchmark score of 1.102 ExaFlop/s, followed by Fugaku. The US has five of the top 10; China has two; Japan, Finland, and France have one each. In June 2018, all combined supercomputers on the TOP500 list broke the 1 exaFLOPS mark.

A circuit board from the IBM 7030

The CDC 6600. Behind the system console are two of the "arms" of the plus-sign shaped cabinet with the covers opened. Each arm of the machine had up to four such racks. On the right is the cooling system.

A Cray-1 preserved at the Deutsches Museum

In 1960, UNIVAC built the Livermore Atomic Research Computer (LARC), today considered among the first supercomputers, for the US Navy Research and Development Center. It still used high-speed drum memory, rather than the newly emerging disk drive technology. Also, among the first supercomputers was the IBM 7030 Stretch. The IBM 7030 was built by IBM for the Los Alamos National Laboratory, which in 1955 had requested a computer 100 times faster than any existing computer. The IBM 7030 used transistors, magnetic core memory, pipelined instructions, prefetched data through a memory controller and included pioneering random access disk drives. The IBM 7030 was completed in 1961 and despite not meeting the challenge of a hundredfold increase in performance, it was purchased by the Los Alamos National Laboratory. Customers in England and France also bought the computer, and it became the basis for the IBM 7950 Harvest, a supercomputer built for cryptanalysis.

The third pioneering supercomputer project in the early 1960s was the Atlas at the University of Manchester, built by a team led by Tom Kilburn. He designed the Atlas to have memory space for up to a million words of 48 bits, but because magnetic storage with such a capacity was unaffordable, the actual core memory of the Atlas was only 16,000 words, with a drum providing memory for a further 96,000 words. The Atlas operating system swapped data in the form of pages between the magnetic core and the drum. The Atlas operating system also introduced time-sharing to supercomputing, so that more than one program could be executed on the supercomputer at any one time. Atlas was a joint venture between Ferranti and Manchester University and was designed to operate at processing speeds approaching one microsecond per instruction, about one million instructions per second.

The CDC 6600, designed by Seymour Cray, was finished in 1964 and marked the transition from germanium to silicon transistors. Silicon transistors could run more quickly and the overheating problem was solved by introducing refrigeration to the supercomputer design. Thus, the CDC6600 became the fastest computer in the world. Given that the 6600 outperformed all the other contemporary computers by about 10 times, it was dubbed a supercomputer and defined the supercomputing market, when one hundred computers were sold at $8 million each.

Cray left CDC in 1972 to form his own company, Cray Research. Four years after leaving CDC, Cray delivered the 80 MHz Cray-1 in 1976, which became one of the most successful supercomputers in history. The Cray-2 was released in 1985. It had eight central processing units (CPUs), liquid cooling and the electronics coolant liquid Fluorinert was pumped through the supercomputer architecture. It reached 1.9 gigaFLOPS, making it the first supercomputer to break the gigaflop barrier.

A cabinet of the massively parallel Blue Gene/L, showing the stacked blades, each holding many processors

The only computer to seriously challenge the Cray-1's performance in the 1970s was the ILLIAC IV. This machine was the first realized example of a true massively parallel computer, in which many processors worked together to solve different parts of a single larger problem. In contrast with the vector systems, which were designed to run a single stream of data as quickly as possible, in this concept, the computer instead feeds separate parts of the data to entirely different processors and then recombines the results. The ILLIAC's design was finalized in 1966 with 256 processors and offer speed up to 1 GFLOPS, compared to the 1970s Cray-1's peak of 250 MFLOPS. However, development problems led to only 64 processors being built, and the system could never operate more quickly than about 200 MFLOPS while being much larger and more complex than the Cray. Another problem was that writing software for the system was difficult, and getting peak performance from it was a matter of serious effort.

But the partial success of the ILLIAC IV was widely seen as pointing the way to the future of supercomputing. Cray argued against this, famously quipping that "If you were plowing a field, which would you rather use? Two strong oxen or 1024 chickens?" But by the early 1980s, several teams were working on parallel designs with thousands of processors, notably the Connection Machine (CM) that developed from research at MIT. The CM-1 used as many as 65,536 simplified custom microprocessors connected together in a network to share data. Several updated versions followed; the CM-5 supercomputer is a massively parallel processing computer capable of many billions of arithmetic operations per second.

In 1982, Osaka University's LINKS-1 Computer Graphics System used a massively parallel processing architecture, with 514 microprocessors, including 257 Zilog Z8001 control processors and 257 iAPX 86/20 floating-point processors. It was mainly used for rendering realistic 3D computer graphics. Fujitsu's VPP500 from 1992 is unusual since, to achieve higher speeds, its processors used GaAs, a material normally reserved for microwave applications due to its toxicity. Fujitsu's Numerical Wind Tunnel supercomputer used 166 vector processors to gain the top spot in 1994 with a peak speed of 1.7 gigaFLOPS (GFLOPS) per processor. The Hitachi SR2201 obtained a peak performance of 600 GFLOPS in 1996 by using 2048 processors connected via a fast three-dimensional crossbar network. The Intel Paragon could have 1000 to 4000 Intel i860 processors in various configurations and was ranked the fastest in the world in 1993. The Paragon was a MIMD machine which connected processors via a high speed two-dimensional mesh, allowing processes to execute on separate nodes, communicating via the Message Passing Interface.

Software development remained a problem, but the CM series sparked off considerable research into this issue. Similar designs using custom hardware were made by many companies, including the Evans & Sutherland ES-1, MasPar, nCUBE, Intel iPSC and the Goodyear MPP. But by the mid-1990s, general-purpose CPU performance had improved so much in that a supercomputer could be built using them as the individual processing units, instead of using custom chips. By the turn of the 21st century, designs featuring tens of thousands of commodity CPUs were the norm, with later machines adding graphic units to the mix.

In 1998, David Bader developed the first Linux supercomputer using commodity parts. While at the University of New Mexico, Bader sought to build a supercomputer running Linux using consumer off-the-shelf parts and a high-speed low-latency interconnection network. The prototype utilized an Alta Technologies "AltaCluster" of eight dual, 333 MHz, Intel Pentium II computers running a modified Linux kernel. Bader ported a significant amount of software to provide Linux support for necessary components as well as code from members of the National Computational Science Alliance (NCSA) to ensure interoperability, as none of it had been run on Linux previously. Using the successful prototype design, he led the development of "RoadRunner," the first Linux supercomputer for open use by the national science and engineering community via the National Science Foundation's National Technology Grid. RoadRunner was put into production use in April 1999. At the time of its deployment, it was considered one of the 100 fastest supercomputers in the world. Though Linux-based clusters using consumer-grade parts, such as Beowulf, existed prior to the development of Bader's prototype and RoadRunner, they lacked the scalability, bandwidth, and parallel computing capabilities to be considered "true" supercomputers.

The CPU share of TOP500

Diagram of a three-dimensional torus interconnect used by systems such as Blue Gene, Cray XT3, etc.

Systems with a massive number of processors generally take one of two paths. In the grid computing approach, the processing power of many computers, organized as distributed, diverse administrative domains, is opportunistically used whenever a computer is available. In another approach, many processors are used in proximity to each other, e.g. in a computer cluster. In such a centralized massively parallel system the speed and flexibility of the becomes very important and modern supercomputers have used various approaches ranging from enhanced Infiniband systems to three-dimensional torus interconnects. The use of multi-core processors combined with centralization is an emerging direction, e.g. as in the Cyclops64 system.

As the price, performance and energy efficiency of general-purpose graphics processing units (GPGPUs) have improved, a number of petaFLOPS supercomputers such as Tianhe-I and Nebulae have started to rely on them. However, other systems such as the K computer continue to use conventional processors such as SPARC-based designs and the overall applicability of GPGPUs in general-purpose high-performance computing applications has been the subject of debate, in that while a GPGPU may be tuned to score well on specific benchmarks, its overall applicability to everyday algorithms may be limited unless significant effort is spent to tune the application to it. However, GPUs are gaining ground, and in 2012 the Jaguar supercomputer was transformed into Titan by retrofitting CPUs with GPUs.

High-performance computers have an expected life cycle of about three years before requiring an upgrade. The Gyoukou supercomputer is unique in that it uses both a massively parallel design and liquid immersion cooling.

A number of special-purpose systems have been designed, dedicated to a single problem. This allows the use of specially programmed FPGA chips or even custom ASICs, allowing better price/performance ratios by sacrificing generality. Examples of special-purpose supercomputers include Belle, Deep Blue, and Hydra for playing chess, Gravity Pipe for astrophysics, MDGRAPE-3 for protein structure prediction and molecular dynamics, and Deep Crack for breaking the DES cipher.

The Summit supercomputer was as of November 2018 the fastest supercomputer in the world.[56] With a measured power efficiency of 14.668 GFlops/watt it is also the third most energy efficient in the world.[57]

Throughout the decades, the management of heat density has remained a key issue for most centralized supercomputers. The large amount of heat generated by a system may also have other effects, e.g. reducing the lifetime of other system components. There have been diverse approaches to heat management, from pumping Fluorinert through the system, to a hybrid liquid-air cooling system or air cooling with normal air conditioning temperatures. A typical supercomputer consumes large amounts of electrical power, almost all of which is converted into heat, requiring cooling. For example, Tianhe-1A consumes 4.04 megawatts (MW) of electricity. The cost to power and cool the system can be significant, e.g. 4 MW at $0.10/kWh is $400 an hour or about $3.5 million per year.

An IBM HS20 blade

Heat management is a major issue in complex electronic devices and affects powerful computer systems in various ways. The thermal design power and CPU power dissipation issues in supercomputing surpass those of traditional computer cooling technologies. The supercomputing awards for green computing reflect this issue.

The packing of thousands of processors together inevitably generates significant amounts of heat density that need to be dealt with. The Cray-2 was liquid cooled, and used a Fluorinert "cooling waterfall" which was forced through the modules under pressure. However, the submerged liquid cooling approach was not practical for the multi-cabinet systems based on off-the-shelf processors, and in System X a special cooling system that combined air conditioning with liquid cooling was developed in conjunction with the Liebert company.

In the Blue Gene system, IBM deliberately used low power processors to deal with heat density. The IBM Power 775, released in 2011, has closely packed elements that require water cooling. The IBM Aquasar system uses hot water cooling to achieve energy efficiency, the water being used to heat buildings as well.

The energy efficiency of computer systems is generally measured in terms of " FLOPS per watt". In 2008, Roadrunner by IBM operated at 3.76 MFLOPS/W. In November 2010, the Blue Gene/Q reached 1,684 MFLOPS/W and in June 2011 the top two spots on the Green 500 list were occupied by Blue Gene machines in New York (one achieving 2097 MFLOPS/W) with the DEGIMA cluster in Nagasaki placing third with 1375 MFLOPS/W.

Because copper wires can transfer energy into a supercomputer with much higher power densities than forced air or circulating refrigerants can remove waste heat, the ability of the cooling systems to remove waste heat is a limiting factor. As of 2015, many existing supercomputers have more infrastructure capacity than the actual peak demand of the machine - designers generally conservatively design the power and cooling infrastructure to handle more than the theoretical peak electrical power consumed by the supercomputer. Designs for future supercomputers are power-limited - the thermal design power of the supercomputer as a whole, the amount that the power and cooling infrastructure can handle, is somewhat more than the expected normal power consumption, but less than the theoretical peak power consumption of the electronic hardware.

Since the end of the 20th century, supercomputer operating systems have undergone major transformations, based on the changes in supercomputer architecture. While early operating systems were custom tailored to each supercomputer to gain speed, the trend has been to move away from in-house operating systems to the adaptation of generic software such as Linux.

Since modern massively parallel supercomputers typically separate computations from other services by using multiple types of nodes, they usually run different operating systems on different nodes, e.g. using a small and efficient lightweight kernel such as CNK or CNL on compute nodes, but a larger system such as a Linux-derivative on server and I/O nodes.

While in a traditional multi-user computer system job scheduling is, in effect, a tasking problem for processing and peripheral resources, in a massively parallel system, the job management system needs to manage the allocation of both computational and communication resources, as well as gracefully deal with inevitable hardware failures when tens of thousands of processors are present.

Although most modern supercomputers use Linux-based operating systems, each manufacturer has its own specific Linux-derivative, and no industry standard exists, partly due to the fact that the differences in hardware architectures require changes to optimize the operating system to each hardware design.

Wide-angle view of the ALMA correlator[89]

The parallel architectures of supercomputers often dictate the use of special programming techniques to exploit their speed. Software tools for distributed processing include standard APIs such as MPI and PVM, VTL, and open source software such as Beowulf.

In the most common scenario, environments such as PVM and MPI for loosely connected clusters and OpenMP for tightly coordinated shared memory machines are used. Significant effort is required to optimize an algorithm for the interconnect characteristics of the machine it will be run on; the aim is to prevent any of the CPUs from wasting time waiting on data from other nodes. GPGPUs have hundreds of processor cores and are programmed using programming models such as CUDA or OpenCL.

Moreover, it is quite difficult to debug and test parallel programs. Special techniques need to be used for testing and debugging such applications.

Example architecture of a grid computing system connecting many personal computers over the internet

Opportunistic supercomputing is a form of networked grid computing whereby a "super virtual computer" of many loosely coupled volunteer computing machines performs very large computing tasks. Grid computing has been applied to a number of large-scale embarrassingly parallel problems that require supercomputing performance scales. However, basic grid and cloud computing approaches that rely on volunteer computing cannot handle traditional supercomputing tasks such as fluid dynamic simulations.

The fastest grid computing system is the volunteer computing project Folding@home (F@h). As of April 2020, F@h reported 2.5 exaFLOPS of x86 processing power. Of this, over 100 PFLOPS are contributed by clients running on various GPUs, and the rest from various CPU systems.

The Berkeley Open Infrastructure for Network Computing (BOINC) platform hosts a number of volunteer computing projects. As of February 2017, BOINC recorded a processing power of over 166 petaFLOPS through over 762 thousand active Computers (Hosts) on the network.

As of October 2016, Great Internet Mersenne Prime Search's (GIMPS) distributed Mersenne Prime search achieved about 0.313 PFLOPS through over 1.3 million computers. The PrimeNet server has supported GIMPS's grid computing approach, one of the earliest volunteer computing projects, since 1997.

Quasi-opportunistic supercomputing is a form of distributed computing whereby the "super virtual computer" of many networked geographically disperse computers performs computing tasks that demand huge processing power. Quasi-opportunistic supercomputing aims to provide a higher quality of service than opportunistic grid computing by achieving more control over the assignment of tasks to distributed resources and the use of intelligence about the availability and reliability of individual systems within the supercomputing network. However, quasi-opportunistic distributed execution of demanding parallel computing software in grids should be achieved through the implementation of grid-wise allocation agreements, co-allocation subsystems, communication topology-aware allocation mechanisms, fault tolerant message passing libraries and data pre-conditioning.

Cloud computing with its recent and rapid expansions and development have grabbed the attention of high-performance computing (HPC) users and developers in recent years. Cloud computing attempts to provide HPC-as-a-service exactly like other forms of services available in the cloud such as software as a service, platform as a service, and infrastructure as a service. HPC users may benefit from the cloud in different angles such as scalability, resources being on-demand, fast, and inexpensive. On the other hand, moving HPC applications have a set of challenges too. Good examples of such challenges are virtualization overhead in the cloud, multi-tenancy of resources, and network latency issues. Much research is currently being done to overcome these challenges and make HPC in the cloud a more realistic possibility.

In 2016, Penguin Computing, Parallel Works, R-HPC, Amazon Web Services, Univa, Silicon Graphics International, Rescale, Sabalcore, and Gomput started to offer HPC cloud computing. The Penguin On Demand (POD) cloud is a bare-metal compute model to execute code, but each user is given virtualized login node. POD computing nodes are connected via non-virtualized 10 Gbit/s Ethernet or QDR InfiniBand networks. User connectivity to the POD data center ranges from 50 Mbit/s to 1 Gbit/s. Citing Amazon's EC2 Elastic Compute Cloud, Penguin Computing argues that virtualization of compute nodes is not suitable for HPC. Penguin Computing has also criticized that HPC clouds may have allocated computing nodes to customers that are far apart, causing latency that impairs performance for some HPC applications.

Supercomputers generally aim for the maximum in capability computing rather than capacity computing. Capability computing is typically thought of as using the maximum computing power to solve a single large problem in the shortest amount of time. Often a capability system is able to solve a problem of a size or complexity that no other computer can, e.g. a very complex weather simulation application.

Capacity computing, in contrast, is typically thought of as using efficient cost-effective computing power to solve a few somewhat large problems or many small problems. Architectures that lend themselves to supporting many users for routine everyday tasks may have a lot of capacity but are not typically considered supercomputers, given that they do not solve a single very complex problem.

Top supercomputer speeds: logscale speed over 60 years

In general, the speed of supercomputers is measured and benchmarked in FLOPS (floating-point operations per second), and not in terms of MIPS (million instructions per second), as is the case with general-purpose computers. These measurements are commonly used with an SI prefix such as tera-, combined into the shorthand TFLOPS (10 ¹² FLOPS, pronounced teraflops), or peta-, combined into the shorthand PFLOPS (10 ¹⁵ FLOPS, pronounced petaflops.) Petascale supercomputers can process one quadrillion (10 ¹⁵) (1000 trillion) FLOPS. Exascale is computing performance in the exaFLOPS (EFLOPS) range. An EFLOPS is one quintillion (10 ¹⁸) FLOPS (one million TFLOPS). However, The performance of a supercomputer can be severely impacted by fluctuation brought on by elements like system load, network traffic, and concurrent processes, as mentioned by Brehm and Bruhwiler (2015).

No single number can reflect the overall performance of a computer system, yet the goal of the Linpack benchmark is to approximate how fast the computer solves numerical problems and it is widely used in the industry. The FLOPS measurement is either quoted based on the theoretical floating point performance of a processor (derived from manufacturer's processor specifications and shown as "Rpeak" in the TOP500 lists), which is generally unachievable when running real workloads, or the achievable throughput, derived from the LINPACK benchmarks and shown as "Rmax" in the TOP500 list. The LINPACK benchmark typically performs LU decomposition of a large matrix. The LINPACK performance gives some indication of performance for some real-world problems, but does not necessarily match the processing requirements of many other supercomputer workloads, which for example may require more memory bandwidth, or may require better integer computing performance, or may need a high performance I/O system to achieve high levels of performance.

Top 20 supercomputers in the world (June 2014)

Since 1993, the fastest supercomputers have been ranked on the TOP500 list according to their LINPACK benchmark results. The list does not claim to be unbiased or definitive, but it is a widely cited current definition of the "fastest" supercomputer available at any given time.

This is a recent list of the computers which appeared at the top of the TOP500 list, and the "Peak speed" is given as the "Rmax" rating. In 2018, Lenovo became the world's largest provider for the TOP500 supercomputers with 117 units produced.

This section needs expansion. You can help by adding to it.

The stages of supercomputer application may be summarized in the following table:

The IBM Blue Gene/P computer has been used to simulate a number of artificial neurons equivalent to approximately one percent of a human cerebral cortex, containing 1.6 billion neurons with approximately 9 trillion connections. The same research group also succeeded in using a supercomputer to simulate a number of artificial neurons equivalent to the entirety of a rat's brain.

Modern-day weather forecasting also relies on supercomputers. The National Oceanic and Atmospheric Administration uses supercomputers to crunch hundreds of millions of observations to help make weather forecasts more accurate.

In 2011, the challenges and difficulties in pushing the envelope in supercomputing were underscored by IBM's abandonment of the Blue Waters petascale project.

The Advanced Simulation and Computing Program currently uses supercomputers to maintain and simulate the United States nuclear stockpile.

In early 2020, COVID-19 was front and center in the world. Supercomputers used different simulations to find compounds that could potentially stop the spread. These computers run for tens of hours using multiple paralleled running CPU's to model different processes.

Taiwania 3 is a Taiwanese supercomputer which assisted the scientific community in fighting COVID-19. It was launched in 2020 and has a capacity of about two to three PetaFLOPS.

Distribution of TOP500 supercomputers among different countries, in November 2015

In the 2010s, China, the United States, the European Union, and others competed to be the first to create a 1 exaFLOP (10 ¹⁸ or one quintillion FLOPS) supercomputer. Erik P. DeBenedictis of Sandia National Laboratories has theorized that a zettaFLOPS (10 ²¹ or one sextillion FLOPS) computer is required to accomplish full weather modeling, which could cover a two-week time span accurately. Such systems might be built around 2030.

Many Monte Carlo simulations use the same algorithm to process a randomly generated data set; particularly, integro-differential equations describing physical transport processes, the random paths, collisions, and energy and momentum depositions of neutrons, photons, ions, electrons, etc. The next step for microprocessors may be into the third dimension; and specializing to Monte Carlo, the many layers could be identical, simplifying the design and manufacture process.

The cost of operating high performance supercomputers has risen, mainly due to increasing power consumption. In the mid-1990s a top 10 supercomputer required in the range of 100 kilowatts, in 2010 the top 10 supercomputers required between 1 and 2 megawatts. A 2010 study commissioned by DARPA identified power consumption as the most pervasive challenge in achieving Exascale computing. At the time a megawatt per year in energy consumption cost about 1 million dollars. Supercomputing facilities were constructed to efficiently remove the increasing amount of heat produced by modern multi-core central processing units. Based on the energy consumption of the Green 500 list of supercomputers between 2007 and 2011, a supercomputer with 1 exaFLOPS in 2011 would have required nearly 500 megawatts. Operating systems were developed for existing hardware to conserve energy whenever possible. CPU cores not in use during the execution of a parallelized application were put into low-power states, producing energy savings for some supercomputing applications.

The increasing cost of operating supercomputers has been a driving factor in a trend toward bundling of resources through a distributed supercomputer infrastructure. National supercomputing centers first emerged in the US, followed by Germany and Japan. The European Union launched the Partnership for Advanced Computing in Europe (PRACE) with the aim of creating a persistent pan-European supercomputer infrastructure with services to support scientists across the European Union in porting, scaling and optimizing supercomputing applications. Iceland built the world's first zero-emission supercomputer. Located at the Thor Data Center in Reykjavík, Iceland, this supercomputer relies on completely renewable sources for its power rather than fossil fuels. The colder climate also reduces the need for active cooling, making it one of the greenest facilities in the world of computers.

Funding supercomputer hardware also became increasingly difficult. In the mid-1990s a top 10 supercomputer cost about 10 million euros, while in 2010 the top 10 supercomputers required an investment of between 40 and 50 million euros. In the 2000s national governments put in place different strategies to fund supercomputers. In the UK the national government funded supercomputers entirely and high performance computing was put under the control of a national funding agency. Germany developed a mixed funding model, pooling local state funding and federal funding.

Examples of supercomputers in fiction include HAL 9000, Multivac, The Machine Stops, GLaDOS, The Evitable Conflict, Vulcan's Hammer, Colossus, WOPR, AM, and Deep Thought. The Cray X-MP was mentioned as the supercomputer used to sequence the DNA extracted from preserved parasites in the Jurassic Park series.

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A supercomputer is a computer with a high level of performance as compared to a general-purpose computer. The performance of a supercomputer is commonly measured in floating-point operations per second ( FLOPS) instead of million instructions per second (MIPS). Since 2017, supercomputers have existed which can perform over 10 17 FLOPS (a hundred quadrillion FLOPS, 100 petaFLOPS or 100 PFLOPS).

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- Mainframe computers are large and expensive.
- They have powerful processors and very large memories.
- Hundred of people can work on this computer at the same time.

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A single-frame IBM z15 mainframe. Larger capacity models can have up to four total frames. This model has blue accents, as compared with the LinuxONE III model with orange highlights.

A pair of IBM mainframes. On the left is the IBM z Systems z13. On the right is the IBM LinuxONE Rockhopper.

An IBM System z9 mainframe

A mainframe computer, informally called a mainframe or big iron, is a computer used primarily by large organizations for critical applications like bulk data processing for tasks such as censuses, industry and consumer statistics, enterprise resource planning, and large-scale transaction processing. A mainframe computer is large but not as large as a supercomputer and has more processing power than some other classes of computers, such as minicomputers, servers, workstations, and personal computers. Most large-scale computer-system architectures were established in the 1960s, but they continue to evolve. Mainframe computers are often used as servers.

The term mainframe was derived from the large cabinet, called a main frame, that housed the central processing unit and main memory of early computers. Later, the term mainframe was used to distinguish high-end commercial computers from less powerful machines.

Modern mainframe design is characterized less by raw computational speed and more by:

Redundant internal engineering resulting in high reliability and security

Extensive input-output ("I/O") facilities with the ability to offload to separate engines

Strict backward compatibility with older software

High hardware and computational utilization rates through virtualization to support massive throughput

Hot swapping of hardware, such as processors and memory

The high stability and reliability of mainframes enable these machines to run uninterrupted for very long periods of time, with mean time between failures (MTBF) measured in decades.

Mainframes have high availability, one of the primary reasons for their longevity, since they are typically used in applications where downtime would be costly or catastrophic. The term reliability, availability and serviceability (RAS) is a defining characteristic of mainframe computers. Proper planning and implementation are required to realize these features. In addition, mainframes are more secure than other computer types: the NIST vulnerabilities database, US-CERT, rates traditional mainframes such as IBM Z (previously called z Systems, System z, and zSeries), Unisys Dorado, and Unisys Libra as among the most secure, with vulnerabilities in the low single digits, as compared to thousands for Windows, UNIX, and Linux. Software upgrades usually require setting up the operating system or portions thereof, and are non disruptive only when using virtualizing facilities such as IBM z/OS and Parallel Sysplex, or Unisys XPCL, which support workload sharing so that one system can take over another's application while it is being refreshed.

In the late 1950s, mainframes had only a rudimentary interactive interface (the console) and used sets of punched cards, paper tape, or magnetic tape to transfer data and programs. They operated in batch mode to support back office functions such as payroll and customer billing, most of which were based on repeated tape-based sorting and merging operations followed by line printing to preprinted continuous stationery. When interactive user terminals were introduced, they were used almost exclusively for applications (e.g. airline booking) rather than program development. However, in 1961 the first academic, general-purpose timesharing system that supported software development, CTSS, was released at MIT on an IBM 709, later 7090 and 7094. Typewriter and Teletype devices were common control consoles for system operators through the early 1970s, although ultimately supplanted by keyboard/ display devices.

By the early 1970s, many mainframes acquired interactive user terminals operating as timesharing computers, supporting hundreds of users simultaneously along with batch processing. Users gained access through keyboard/typewriter terminals and later character-mode text terminal CRT displays with integral keyboards, or finally from personal computers equipped with terminal emulation software. By the 1980s, many mainframes supported general purpose graphic display terminals, and terminal emulation, but not graphical user interfaces. This form of end-user computing became obsolete in the 1990s due to the advent of personal computers provided with GUIs. After 2000, modern mainframes partially or entirely phased out classic " green screen" and color display terminal access for end-users in favour of Web-style user interfaces.

The infrastructure requirements were drastically reduced during the mid-1990s, when CMOS mainframe designs replaced the older bipolar technology. IBM claimed that its newer mainframes reduced data center energy costs for power and cooling, and reduced physical space requirements compared to server farms.

Inside an IBM System z9 mainframe

Modern mainframes can run multiple different instances of operating systems at the same time. This technique of virtual machines allows applications to run as if they were on physically distinct computers. In this role, a single mainframe can replace higher-functioning hardware services available to conventional servers. While mainframes pioneered this capability, virtualization is now available on most families of computer systems, though not always to the same degree or level of sophistication.

Mainframes can add or hot swap system capacity without disrupting system function, with specificity and granularity to a level of sophistication not usually available with most server solutions. Modern mainframes, notably the IBM Z servers, offer two levels of virtualization: logical partitions ( LPARs, via the PR/SM facility) and virtual machines (via the z/VM operating system). Many mainframe customers run two machines: one in their primary data center and one in their backup data center-fully active, partially active, or on standby-in case there is a catastrophe affecting the first building. Test, development, training, and production workload for applications and databases can run on a single machine, except for extremely large demands where the capacity of one machine might be limiting. Such a two-mainframe installation can support continuous business service, avoiding both planned and unplanned outages. In practice, many customers use multiple mainframes linked either by Parallel Sysplex and shared DASD (in IBM's case), or with shared, geographically dispersed storage provided by EMC or Hitachi.

Mainframes are designed to handle very high volume input and output (I/O) and emphasize throughput computing. Since the late 1950s, mainframe designs have included subsidiary hardware (called channels or peripheral processors) which manage the I/O devices, leaving the CPU free to deal only with high-speed memory. It is common in mainframe shops to deal with massive databases and files. Gigabyte to terabyte-size record files are not unusual. Compared to a typical PC, mainframes commonly have hundreds to thousands of times as much data storage online, and can access it reasonably quickly. Other server families also offload I/O processing and emphasize throughput computing.

Mainframe return on investment (ROI), like any other computing platform, is dependent on its ability to scale, support mixed workloads, reduce labor costs, deliver uninterrupted service for critical business applications, and several other risk-adjusted cost factors.

Mainframes also have execution integrity characteristics for fault tolerant computing. For example, z900, z990, System z9, and System z10 servers effectively execute result-oriented instructions twice, compare results, arbitrate between any differences (through instruction retry and failure isolation), then shift workloads "in flight" to functioning processors, including spares, without any impact to operating systems, applications, or users. This hardware-level feature, also found in HP's NonStop systems, is known as lock-stepping, because both processors take their "steps" (i.e. instructions) together. Not all applications absolutely need the assured integrity that these systems provide, but many do, such as financial transaction processing.

IBM, with the IBM Z series, continues to be a major manufacturer in the mainframe market. In 2000, Hitachi co-developed the zSeries z900 with IBM to share expenses, and the latest Hitachi AP10000 models are made by IBM. Unisys manufactures ClearPath Libra mainframes, based on earlier Burroughs MCP products and ClearPath Dorado mainframes based on Sperry Univac OS 1100 product lines. Hewlett Packard Enterprise sells its unique NonStop systems, which it acquired with Tandem Computers and which some analysts classify as mainframes. Groupe Bull's GCOS, Stratus OpenVOS, Fujitsu (formerly Siemens) BS2000, and Fujitsu- ICL VME mainframes are still available in Europe, and Fujitsu (formerly Amdahl) GS21 mainframes globally. NEC with ACOS and Hitachi with AP10000- VOS3 still maintain mainframe businesses in the Japanese market.

The amount of vendor investment in mainframe development varies with market share. Fujitsu and Hitachi both continue to use custom S/390-compatible processors, as well as other CPUs (including POWER and Xeon) for lower-end systems. Bull uses a mixture of Itanium and Xeon processors. NEC uses Xeon processors for its low-end ACOS-2 line, but develops the custom NOAH-6 processor for its high-end ACOS-4 series. IBM also develops custom processors in-house, such as the Telum. Unisys produces code compatible mainframe systems that range from laptops to cabinet-sized mainframes that use homegrown CPUs as well as Xeon processors. Furthermore, there exists a market for software applications to manage the performance of mainframe implementations. In addition to IBM, significant market competitors include BMC, Precisely, Compuware, and CA Technologies. Starting in the 2010s, cloud computing is now a less expensive, more scalable alternative.

Operator's console for an IBM 701

Several manufacturers and their successors produced mainframe computers from the 1950s until the early 21st century, with gradually decreasing numbers and a gradual transition to simulation on Intel chips rather than proprietary hardware. The US group of manufacturers was first known as " IBM and the Seven Dwarfs": usually Burroughs, UNIVAC, NCR, Control Data, Honeywell, General Electric and RCA, although some lists varied. Later, with the departure of General Electric and RCA, it was referred to as IBM and the BUNCH. IBM's dominance grew out of their 700/7000 series and, later, the development of the 360 series mainframes. The latter architecture has continued to evolve into their current zSeries mainframes which, along with the then Burroughs and Sperry (now Unisys) MCP-based and OS1100 mainframes, are among the few mainframe architectures still extant that can trace their roots to this early period. While IBM's zSeries can still run 24-bit System/360 code, the 64-bit IBM Z CMOS servers have nothing physically in common with the older systems. Notable manufacturers outside the US were Siemens and Telefunken in Germany, ICL in the United Kingdom, Olivetti in Italy, and Fujitsu, Hitachi, Oki, and NEC in Japan. The Soviet Union and Warsaw Pact countries manufactured close copies of IBM mainframes during the Cold War; the BESM series and Strela are examples of independently designed Soviet computers. Elwro in Poland was another Eastern Bloc manufacturer, producing the ODRA, R-32 and R-34 mainframes.

Shrinking demand and tough competition started a shakeout in the market in the early 1970s-RCA sold out to UNIVAC and GE sold its business to Honeywell; between 1986 and 1990 Honeywell was bought out by Bull; UNIVAC became a division of Sperry, which later merged with Burroughs to form Unisys Corporation in 1986.

In 1984 estimated sales of desktop computers ($11.6 billion) exceeded mainframe computers ($11.4 billion) for the first time. IBM received the vast majority of mainframe revenue. During the 1980s, minicomputer-based systems grew more sophisticated and were able to displace the lower end of the mainframes. These computers, sometimes called departmental computers, were typified by the Digital Equipment Corporation VAX series.

In 1991, AT&T Corporation briefly owned NCR. During the same period, companies found that servers based on microcomputer designs could be deployed at a fraction of the acquisition price and offer local users much greater control over their own systems given the IT policies and practices at that time. Terminals used for interacting with mainframe systems were gradually replaced by personal computers. Consequently, demand plummeted and new mainframe installations were restricted mainly to financial services and government. In the early 1990s, there was a rough consensus among industry analysts that the mainframe was a dying market as mainframe platforms were increasingly replaced by personal computer networks. InfoWorld's Stewart Alsop infamously predicted that the last mainframe would be unplugged in 1996; in 1993, he cited Cheryl Currid, a computer industry analyst as saying that the last mainframe "will stop working on December 31, 1999", a reference to the anticipated Year 2000 problem (Y2K).

That trend started to turn around in the late 1990s as corporations found new uses for their existing mainframes and as the price of data networking collapsed in most parts of the world, encouraging trends toward more centralized computing. The growth of e-business also dramatically increased the number of back-end transactions processed by mainframe software as well as the size and throughput of databases. Batch processing, such as billing, became even more important (and larger) with the growth of e-business, and mainframes are particularly adept at large-scale batch computing. Another factor currently increasing mainframe use is the development of the Linux operating system, which arrived on IBM mainframe systems in 1999. Linux allows users to take advantage of open source software combined with mainframe hardware RAS. Rapid expansion and development in emerging markets, particularly People's Republic of China, is also spurring major mainframe investments to solve exceptionally difficult computing problems, e.g. providing unified, extremely high volume online transaction processing databases for 1 billion consumers across multiple industries (banking, insurance, credit reporting, government services, etc.) In late 2000, IBM introduced 64-bit z/Architecture, acquired numerous software companies such as Cognos and introduced those software products to the mainframe. IBM's quarterly and annual reports in the 2000s usually reported increasing mainframe revenues and capacity shipments. However, IBM's mainframe hardware business has not been immune to the recent overall downturn in the server hardware market or to model cycle effects. For example, in the 4th quarter of 2009, IBM's System z hardware revenues decreased by 27% year over year. But MIPS (millions of instructions per second) shipments increased 4% per year over the past two years. Alsop had himself photographed in 2000, symbolically eating his own words ("death of the mainframe").

In 2012, NASA powered down its last mainframe, an IBM System z9. However, IBM's successor to the z9, the z10, led a New York Times reporter to state four years earlier that "mainframe technology-hardware, software and services-remains a large and lucrative business for I.B.M., and mainframes are still the back-office engines behind the world's financial markets and much of global commerce". As of 2010, while mainframe technology represented less than 3% of IBM's revenues, it "continue[d] to play an outsized role in Big Blue's results".

IBM has continued to launch new generations of mainframes: the IBM z13 in 2015, the z14 in 2017, the z15 in 2019 and the z16 in 2022, the latter featuring among other things an "integrated on-chip AI accelerator" and the new Telum microprocessor.

A supercomputer is a computer at the leading edge of data processing capability, with respect to calculation speed. Supercomputers are used for scientific and engineering problems ( high-performance computing) which crunch numbers and data, while mainframes focus on transaction processing. The differences are:

Mainframes are built to be reliable for transaction processing (measured by TPC-metrics; not used or helpful for most supercomputing applications) as it is commonly understood in the business world: the commercial exchange of goods, services, or money.[citation needed ] A typical transaction, as defined by the Transaction Processing Performance Council, updates a database system for inventory control (goods), airline reservations (services), or banking (money) by adding a record. A transaction may refer to a set of operations including disk read/writes, operating system calls, or some form of data transfer from one subsystem to another which is not measured by the processing speed of the CPU. Transaction processing is not exclusive to mainframes but is also used by microprocessor-based servers and online networks.

Supercomputer performance is measured in floating point operations per second (FLOPS) or in traversed edges per second or TEPS, metrics that are not very meaningful for mainframe applications, while mainframes are sometimes measured in millions of instructions per second (MIPS), although the definition depends on the instruction mix measured. Examples of integer operations measured by MIPS include adding numbers together, checking values or moving data around in memory (while moving information to and from storage, so-called I/O is most helpful for mainframes; and within memory, only helping indirectly). Floating point operations are mostly addition, subtraction, and multiplication (of binary floating point in supercomputers; measured by FLOPS) with enough digits of precision to model continuous phenomena such as weather prediction and nuclear simulations (only recently standardized decimal floating point, not used in supercomputers, are appropriate for monetary values such as those useful for mainframe applications). In terms of computational speed, supercomputers are more powerful.

Mainframes and supercomputers cannot always be clearly distinguished; up until the early 1990s, many supercomputers were based on a mainframe architecture with supercomputing extensions. An example of such a system is the HITAC S-3800, which was instruction-set compatible with IBM System/370 mainframes, and could run the Hitachi VOS3 operating system (a fork of IBM MVS). The S-3800 therefore can be seen as being both simultaneously a supercomputer and also an IBM-compatible mainframe.

In 2007, an amalgamation of the different technologies and architectures for supercomputers and mainframes has led to a so-called gameframe.

Media related to Mainframe computers at Wikimedia Commons

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A mainframe computer, informally called a mainframe or big iron, is a computer used primarily by large organizations for critical applications like bulk data processing for tasks such as censuses, industry and consumer statistics, enterprise resource planning, and large-scale transaction processing.

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- Minicomputers are smaller and cheaper than mainframe computers.
- Have less processing power.
- Can process large amounts of data.

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A minicomputer, or colloquially mini, is a class of smaller general purpose computers that developed in the mid-1960s and sold for much less than mainframe and mid-size computers from IBM and its direct competitors. In a 1970 survey, The New York Times suggested a consensus definition of a minicomputer as a machine costing less than US$25,000 (equivalent to $174,000 in 2021), with an input-output device such as a teleprinter and at least four thousand words of memory, that is capable of running programs in a higher level language, such as Fortran or BASIC.

The class formed a distinct group with its own software architectures and operating systems. Minis were designed for control, instrumentation, human interaction, and communication switching as distinct from calculation and record keeping. Many were sold indirectly to original equipment manufacturers (OEMs) for final end use application. During the two decade lifetime of the minicomputer class (1965-1985), almost 100 companies formed and only a half dozen remained.

When single-chip CPU microprocessors appeared, beginning with the Intel 4004 in 1971, the term "minicomputer" came to mean a machine that lies in the middle range of the computing spectrum, in between the smallest mainframe computers and the microcomputers. The term "minicomputer" is seldom used today; the contemporary term for this class of system is " midrange computer", such as the higher-end SPARC from Oracle, Power ISA from IBM, and Itanium-based systems from Hewlett-Packard.

The term "minicomputer" developed in the 1960s to describe the smaller computers that became possible with the use of transistors and core memory technologies, minimal instructions sets and less expensive peripherals such as the ubiquitous Teletype Model 33 ASR. They usually took up one or a few 19-inch rack cabinets, compared with the large mainframes that could fill a room.

In terms of relative computing power compared to contemporary mainframes, small systems that were similar to minicomputers had been available from the 1950s. In particular, there was an entire class of drum machines, like the UNIVAC 1101 and LGP-30, that share some features of the minicomputer class. Similar models using magnetic delay-line memory followed in the early 1960s. These machines however, were essentially designed as small mainframes, using a custom chassis and often supporting only peripherals from the same company. In contrast, the machines that became known as minicomputers were often designed to fit into a standard chassis and deliberately designed to use common devices like the ASR 33.

Another common difference was that most earlier small machines were not "general purpose", in that they were designed for a specific role like process control or accounting. On these machines, programming was generally carried out in their custom machine language, or even hard-coded into a plugboard, although some used a form of BASIC. DEC wrote, regarding their PDP-5, that it was "the world's first commercially produced minicomputer". It meets most definitions of "mini" in terms of power and size, but was designed and built to be used as an instrumentation system in labs, not as a general-purpose computer. Many similar examples of small special-purpose machines exist from the early 1960s, including the UK Ferranti Argus and Soviet UM-1NKh.

The CDC 160, circa 1960, is sometimes pointed to as an early example of a minicomputer, as it was small, transistorized and (relatively) inexpensive. However, its basic price of $100,000 (equivalent to $915,973 in 2021) and custom desk-like chassis places it within the "small system" or "midrange computer" category as opposed to the more modern use of the term minicomputer. Nevertheless, it retains a strong contender for the term "first minicomputer".

Most computing histories point to the 1964 introduction of Digital Equipment Corporation's (DEC) 12-bit PDP-8 as the first minicomputer. Some of this is no doubt due to DEC's widespread use of the term starting in the mid-1960s. Smaller systems, including those from DEC like the PDP-5 and LINC, had existed prior to this point, but it was the PDP-8 combination of small size, general purpose orientation and low price that puts it firmly within the modern definition. Its introductory price of $18,500 (equivalent to $159,077 in 2021) places it in an entirely different market segment than earlier examples like the CDC 160.

In contemporary terms, the PDP-8 was a runaway success, ultimately selling 50,000 examples. Follow-on versions using small scale integrated circuits further lowered the cost and size of the system. Its success led to widespread imitation, and the creation of an entire industry of minicomputer companies along Massachusetts Route 128, including Data General, Wang Laboratories and Prime Computer. Other popular minis from the era were the HP 2100, Honeywell 316 and TI-990.

Early minis had a variety of word sizes, with DEC's 12 and 18-bit systems being typical examples. The introduction and standardization of the 7-bit ASCII character set led to the move to 16-bit systems, with the late-1969 Data General Nova being a notable entry in this space. By the early 1970s, most minis were 16-bit, including DEC's PDP-11. For a time, "minicomputer" was almost synonymous with "16-bit", as the larger mainframe machines almost always used 32-bit or larger word sizes.

As integrated circuit design improved, especially with the introduction of the 7400-series integrated circuits, minicomputers became smaller, easier to manufacture, and as a result, less expensive. They were used in manufacturing process control, telephone switching and to control laboratory equipment. In the 1970s, they were the hardware that was used to launch the computer-aided design (CAD) industry and other similar industries where a small dedicated system was needed.

The boom in worldwide seismic exploration for oil and gas in the early 1970s saw the widespread use of minicomputers in dedicated processing centres close to the data collection crews. Raytheon Data Systems RDS 704 and later RDS 500 were predominantly the systems of choice for nearly all the geophysical exploration as well as oil companies.

At the launch of the MITS Altair 8800 in 1975, Radio Electronics magazine referred to the system as a "minicomputer", although the term microcomputer soon became usual for personal computers based on single-chip microprocessors. At the time, microcomputers were 8-bit single-user, relatively simple machines running simple program-launcher operating systems like CP/M or MS-DOS, while minis were much more powerful systems that ran full multi-user, multitasking operating systems, such as VMS and Unix.

Around the same time, minis began to move upward in size. Although several 24 and 32-bit minis had entered the market earlier, it was DEC's 1977 VAX, which they referred to as a superminicomputer, or supermini, that caused the mini market to move en-masse to 32-bit architectures. This provided ample headroom even as single-chip 16-bit microprocessors like the TMS 9900 and Zilog Z8000 appeared in the later 1970s. Most mini vendors introduced their own single-chip processors based on their own architecture and used these mostly in low-cost offerings while concentrating on their 32-bit systems. Examples include the Intersil 6100 single-chip PDP-8, DEC T-11 PDP-11, microNOVA and Fairchild 9440 Nova, and TMS9900 TI-990.

By the early 1980s, the 16-bit market had all but disappeared as newer 16-bit microprocessors began to improve in performance. Those customers who required more performance than these offered had generally already moved to 32-bit systems by this time. But it was not long before this market also began to come under threat; the Motorola 68000 offered a significant percentage of the performance of a typical mini in a desktop platform. True 32-bit processors like the National Semiconductor NS32016, Motorola 68020 and Intel 80386 soon followed. By the mid-1980s, high-end microcomputers offered compute performance equal to low-end and mid-range minis, and the new RISC approach promised performance levels well beyond the fastest minis, and even high-end mainframes.

All that really separated micros from the mini market was storage and memory capacity. Both of these began to be addressed through the later 1980s; 1 MB of RAM became typical by around 1987, desktop hard drives rapidly pushed past the 100 MB range by 1990, and the introduction of inexpensive and easily deployable local area network (LAN) systems provide solutions for those looking for multi-user systems. The introduction of the workstation machines opened new markets for graphics-based systems that the terminal-oriented minis could not even address. Minis retained a force for those using existing software products or those who required high-performance multitasking, but the introduction of newer operating systems based on Unix began to become highly practical replacements for these roles as well.

Mini vendors began to rapidly disappear through this period. Data General responded to the changing market by focussing entirely on the high-performance file server market, embracing a role within large LANs that appeared resilient. This did not last; Novell NetWare rapidly pushed such solutions into niche roles, and later versions of Microsoft Windows did the same to Novell. DEC decided to move into the large-computer space instead, introducing the VAX 9000 mainframe in 1989, but it was a flop in the market and disappeared after almost no sales. The company then attempted to enter the workstation and server markets with the DEC Alpha, but was too late to save the company and they eventually sold their remains to Compaq in 1998. By the end of the decade all of the classic vendors were gone; Data General, Prime, Computervision, Honeywell, and Wang, failed, merged, or were bought out.

Today, only a few proprietary minicomputer architectures survive. The IBM System/38 operating system, which introduced many advanced concepts, lives on with IBM's AS/400. Great efforts were made by IBM to enable programs originally written for the IBM System/34 and System/36 to be moved to the AS/400. After being rebranded multiple times, the AS/400 platform was replaced by IBM Power Systems running IBM i. In contrast, competing proprietary computing architectures from the early 1980s, such as DEC's VAX, Wang VS, and Hewlett-Packard's HP 3000 have long been discontinued without a compatible upgrade path. OpenVMS runs on HP Alpha and Intel IA-64 ( Itanium) CPU architectures.

Tandem Computers, which specialized in reliable large-scale computing, was acquired by Compaq, and a few years afterward the combined entity merged with Hewlett-Packard. The NSK-based NonStop product line was re-ported from MIPS processors to Itanium-based processors branded as ' HP Integrity NonStop Servers'. As in the earlier migration from stack machines to MIPS microprocessors, all customer software was carried forward without source changes. Integrity NonStop continues to be HP's answer for the extreme scaling needs of its very largest customers. The NSK operating system, now termed NonStop OS, continues as the base software environment for the NonStop Servers, and has been extended to include support for Java and integration with popular development tools like Visual Studio and Eclipse.

A variety of companies emerged that built turnkey systems around minicomputers with specialized software and, in many cases, custom peripherals that addressed specialized problems such as computer-aided design, computer-aided manufacturing, process control, manufacturing resource planning, and so on. Many if not most minicomputers were sold through these original equipment manufacturers and value-added resellers.

Several pioneering computer companies first built minicomputers, such as DEC, Data General, and Hewlett-Packard (HP) (who now refers to its HP3000 minicomputers as "servers" rather than "minicomputers"). And although today's PCs and servers are clearly microcomputers physically, architecturally their CPUs and operating systems have developed largely by integrating features from minicomputers.[citation needed ]

In the software context, the relatively simple OSs for early microcomputers were usually inspired by minicomputer OSs (such as CP/M's similarity to Digital's single user OS/8 and RT-11 and multi-user RSTS time-sharing system). Also, the multiuser OSs of today are often either inspired by, or directly descended from, minicomputer OSs.[citation needed ] UNIX was originally a minicomputer OS, while Windows NT kernel-the foundation for all current versions of Microsoft Windows-borrowed design ideas liberally from VMS. Many of the first generation of PC programmers were educated on minicomputer systems.

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A minicomputer, or colloquially mini, is a class of smaller general purpose computers that developed in the mid-1960s and sold for much less than mainframe and mid-size computers from IBM and its direct competitors.

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-Also called: deskop computers or personal computers.

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- Laptop computers are smaller but costiler than desktop computers.
- They are also called notebooks.
- They can be operated with a battery and as they are portable.
- They are mostly used while traveling.

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USED TO:
- Organize information
- Play games
- Read e-books

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- Is a microprocessor that is fitted into a device control its operation.
-Embedded computers are installed in smart appliances and automobiles to perform special operations.

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A question we get asked a lot is: "what is an embedded computer?". An embedded computer functions as part of a larger device or system, rather than being used as a standalone computer. Typically, an embedded computer performs a highly specific function.