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Several updated versions followed; the CM-5 supercomputer is a massively parallel processing computer capable of many billions of arithmetic operations per second. It was mainly used for rendering realistic 3D computer graphics.

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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. But by the mids, 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. Systems with a massive number of processors generally take one of two paths. In the grid computing approach, the processing power of many computers, organised as distributed, diverse administrative domains, is opportunistically used whenever a computer is available. In such a centralized massively parallel system the speed and flexibility of the interconnect becomes very important and modern supercomputers have used various approaches ranging from enhanced Infiniband systems to three-dimensional torus interconnects.

High-performance computers have an expected life cycle of about three years before requiring an upgrade. A number of "special-purpose" systems have been designed, dedicated to a single problem. Throughout the decades, the management of heat density has remained a key issue for most centralized supercomputers. For example, Tianhe-1A consumes 4. Heat management is a major issue in complex electronic devices and affects powerful computer systems in various ways. 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. In , IBM's Roadrunner operated at 3. Because copper wires can transfer energy into a supercomputer with much higher power densities than forced air or circulating refrigerants can remove waste heat , [73] the ability of the cooling systems to remove waste heat is a limiting factor.

Since the end of the 20th century, supercomputer operating systems have undergone major transformations, based on the changes in supercomputer architecture. 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. 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 a Linux -based operating system, 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.

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The parallel architectures of supercomputers often dictate the use of special programming techniques to exploit their speed. 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. Moreover, it is quite difficult to debug and test parallel programs. Special techniques need to be used for testing and debugging such applications. 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 distributed computing project Folding home F h.

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.

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However, quasi-opportunistic distributed execution of demanding parallel computing software in grids should be achieved through 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 HPC users and developers in recent years.

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.

The Penguin On Demand POD cloud is a bare-metal compute model to execute code, but each user is given virtualized login node. Penguin Computing has also criticized that HPC clouds may 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.

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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. 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. 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 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 TOP list, [] and the "Peak speed" is given as the "Rmax" rating. Source: TOP In , Lenovo became the worlds largest provider for the top supercomputers.

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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 , 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.

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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 s a top 10 supercomputer required in the range of kilowatt, in the top 10 supercomputers required between 1 and 2 megawatt. 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 list of supercomputers between and , a supercomputer with 1 exaflops in would have required nearly megawatt.

Operating systems were developed for existing hardware to conserve energy whenever possible. The increasing cost of operating supercomputers has been a driving factor in a trend towards bundling of resources through a distributed supercomputer infrastructure. National supercomputing centres 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.

Located at the Thor Data Center in Reykjavik , 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.


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Funding supercomputer hardware also became increasingly difficult. In the mid s a top 10 supercomputer cost about 10 Million Euros, while in the top 10 supercomputers required an investment of between 40 and 50 million Euros. 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. Many science-fiction writers have depicted supercomputers in their works, both before and after the historical construction of such computers.

Much of such fiction deals with the relations of humans with the computers they build and with the possibility of conflict eventually developing between them. Some scenarios of this nature appear on the AI-takeover page. From Wikipedia, the free encyclopedia. This is the latest accepted revision , reviewed on 20 September For narrower definitions of HPC, see high-throughput computing and many-task computing. For other uses, see supercomputer disambiguation. Extremely powerful computer for its era. Main article: History of supercomputing. Main articles: Supercomputer architecture and Parallel computer hardware.

See also: Computer cooling and Green Main article: Supercomputer operating systems. Main article: Message passing in computer clusters. See also: Parallel computing and Parallel programming model.

kamishiro-hajime.info/voice/golocalisation-portable/application-android-pour-jeedom.php Main article: Grid computing. Main article: Quasi-opportunistic supercomputing.

Main article: TOP Further information: History of supercomputing. Add to Watchlist Unwatch. Watch list is full.