
n.
A mainframe computer that is among the largest, fastest, or most powerful of those available at a given time.
On this page
American Heritage Dictionary:
su·per·com·put·er |

|
Featured Videos:
|
Britannica Concise Encyclopedia:
supercomputer |
For more information on supercomputer, visit Britannica.com.
McGraw-Hill Science & Technology Encyclopedia:
Supercomputer |
A computer which, among existing general-purpose computers at any given time, is superlative, often in several senses: highest computation rate, largest memory, or highest cost. Predominantly, the term refers to the fastest “number crunchers,” that is, machines designed to perform numerical calculations at the highest speed that the latest electronic device technology and the state of the art of computer architecture allow.
The demand for the ability to execute arithmetic operations at the highest possible rate originated in computer applications areas collectively referred to as scientific computing. Large-scale numerical simulations of physical processes are often needed in fields such as physics, structural mechanics, meteorology, and aerodynamics. A common technique is to compute an approximate numerical solution to a set of partial differential equations which mathematically describe the physical process of interest but are too complex to be solved by formal mathematical methods. This solution is obtained by first superimposing a grid on a region of space, with a set of numerical values attached to each grid point. Large-scale scientific computations of this type often require hundreds of thousands of grid points with 10 or more values attached to each point, with 10 to 500 arithmetic operations necessary to compute each updated value, and hundreds of thousands of time steps over which the computation must be repeated before a steady-state solution is reached. See also Computational fluid dynamics; Numerical analysis; Simulation.
Two lines of technological advancement have significantly contributed to what roughly amounts to a doubling of the fastest computers' speeds every year since the early 1950s—the steady improvement in electronic device technology and the accumulation of improvements in the architectural designs of digital computers.
Computers incorporate very large-scale integrated (VLSI) circuits with tens of millions of transistors per chip for both logic and memory components. A variety of types of integrated circuitry is used in contemporary supercomputers. Several use high-speed complementary metallic oxide semiconductor (CMOS) technology. Throughout most of the history of digital computing, supercomputers generally used the highest-performance switching circuitry available at the time—which was usually the most exotic and expensive. However, many supercomputers now use the conventional, inexpensive device technology of commodity microprocessors and rely on massive parallelism for their speed. See also Computer storage technology; Concurrent processing; Integrated circuits; Logic circuits; Semiconductor memories.
Increases in computing speed which are purely due to the architectural structure of a computer can largely be attributed to the introduction of some form of parallelism into the machine's design: two or more operations which were performed one after the other in previous computers can now be performed simultaneously. See also Computer systems architecture.
Pipelining is a technique which allows several operations to be in progress in the central processing unit at once. The first form of pipelining used was instruction pipelining. Since each instruction must have the same basic sequence of steps performed, namely instruction fetch, instruction decode, operand fetch, and execution, it is feasible to construct an instruction pipeline, where each of these steps happens at a separate stage of the pipeline. The efficiency of the instruction pipeline depends on the likelihood that the program being executed allows a steady stream of instructions to be fetched from contiguous locations in memory.
The central processing unit nearly always has a much faster cycle time than the memory. This implies that the central processing unit is capable of processing data items faster than a memory unit can provide them. Interleaved memory is an organization of memory units which at least partially relieves this problem.
Parallelism within arithmetic and logical circuitry has been introduced in several ways. Adders, multipliers, and dividers now operate in bit-parallel mode, while the earliest machines performed bit-serial arithmetic. Independently operating parallel functional units within the central processing unit can each perform an arithmetic operation such as add, multiply, or shift. Array processing is a form of parallelism in which the instruction execution portion of a central processing unit is replicated several times and connected to its own memory device as well as to a common instruction interpretation and control unit. In this way, a single instruction can be executed at the same time on each of several execution units, each on a different set of operands. This kind of architecture is often referred to as single-instruction stream, multiple-data stream (SIMD).
Vector processing is the term applied to a form of pipelined arithmetic units which are specialized for performing arithmetic operations on vectors, which are uniform, linear arrays of data values. It can be thought of as a type of SIMD processing, since a single instruction invokes the execution of the same operation on every element of the array. See also Computer programming; Programming languages.
A central processing unit can contain multiple sets of the instruction execution hardware for either scalar or vector instructions. The task of scheduling instructions which can correctly execute in parallel with one another is generally the responsibility of the compiler or special scheduling hardware in the central processing unit. Instruction-level parallelism is almost never visible to the application programmer.
Multiprocessing is a form of parallelism that has complete central processing units operating in parallel, each fetching and executing instructions independently from the others. This type of computer organization is called multiple-instruction stream, multiple-data stream (MIMD). See also Multiprocessing.
TechEncyclopedia:
supercomputer |
The fastest computer available. Supercomputers are typically used for simulations in petroleum exploration and production, structural analysis, computational fluid dynamics, physics and chemistry, electronic design, nuclear energy research and meteorology. Years ago, it took a supercomputer to perform real-time animated graphics, but that changed as high-performance desktop computers became capable of doing the job. See supercomputer sites and grid computing.
Download Computer Desktop Encyclopedia to your PC, iPhone or Android.
Barron's Accounting Dictionary:
supercomputer |
| Superabsorption Costing, Sunk Cost, Sum-Of-The-Years'-Digits (SYD) Method | |
| Supervariable Costing, Supplementary Statement, Suppliers' Management |
Columbia Encyclopedia:
supercomputer |
Gale Encyclopedia of Espionage & Intelligence:
Supercomputers |
A supercomputer is a powerful computer that possesses the capacity to store and process far more information than is possible using a conventional personal computer.
An illustrative comparison can be made between the hard drive capacity of a personal computer and a super-computer. Hard drive capacity is measured in terms of gigabytes. A gigabyte is one billion bytes. A byte is a unit of data that is eight binary digits (i.e., 0's and 1's) long; this is enough data to represent a number, letter, or a typographic symbol. Premium personal computers have a hard drive that is capable of storing on the order of 30 gigabytes of information. In contrast, a supercomputer has a capacity of 200 to 300 gigabytes or more.
Another useful comparison between supercomputers and personal computers is in the number of processors in each machine. A processor is the circuitry responsible for handling the instructions that drive a computer. Personal computers have a single processor. The largest supercomputers have thousands of processors.
This enormous computation power makes supercomputers capable of handling large amounts of data and processing information extremely quickly. For example, in April 2002, a Japanese supercomputer that contains 5,104 processors established a calculation speed record of 35,600 gigaflops (a gigaflop is one billion mathematical calculations per second). This exceeded the old record that was held by the ASCI White-Pacific supercomputer located at the Lawrence Livermore National Laboratory in Berkeley, California. The Livermore supercomputer, which is equipped with over 7,000 processors, achieves 7,226 gigaflops.
These speeds are a far cry from the first successful supercomputer, the Sage System CDC 6600, which was designed by Seymour Cray (founder of the Cray Corporation) in 1964. His computer had a speed of 9 megaflops, thousands of times slower than the present day versions. Still, at that time, the CDC 6600 was an impressive advance in computer technology.
Beginning around 1995, another approach to designing supercomputers appeared. In grid computing, thousands of individual computers are networked together, even via the Internet. The combined computational power can exceed that of the all-in-one supercomputer at far less cost. In the grid approach, a problem can be broken down into components, and the components can be parceled out to the various computers. As the component problems are solved, the solutions are pieced back together mathematically to generate the overall solution.
The phenomenally fast calculation speeds of the present day supercomputers essentially corresponds to "real time," meaning an event can be monitored or analyzed as it occurs. For example, a detailed weather map, which would take a personal computer several days to compile, can be complied on a supercomputer in just a few minutes.
Supercomputers like the Japanese version are built to model events such as climate change, global warming, and earthquake patterns. Increasingly, however, supercomputers are being used for security purposes such as the analysis of electronic transmissions (i.e., email, faxes, telephone calls) for codes. For example, a network of supercomputers and satellites that is called Echelon is used to monitor electronic communications in the United States, Canada, United Kingdom, Australia, and New Zealand. The stated purpose of Echelon is to combat terrorism and organized crime activities.
The next generation of supercomputers is under development. Three particularly promising technologies are being explored. The first of these is optical computing. Light is used instead of using electrons to carry information. Light moves much faster than an electron can, therefore the speed of transmission is greater.
The second technology is known as DNA computing. Here, recombining DNA in different sequences does calculations. The sequence(s) that are favored and persist represent the optimal solution. Solutions to problems can be deduced even before the problem has actually appeared.
The third technology is called quantum computing. Properties of atoms or nuclei, designated as quantum bits, or qubits, would be the computer's processor and memory. A quantum computer would be capable of doing a computation by working on many aspects of the problem at the same time, on many different numbers at once, then using these partial results to arrive at a single answer. For example, deciphering the correct code from a 400-digit number would take a supercomputer millions of years. However, a quantum computer that is about the size of a teacup could do the job in about a year.
Further Reading
Books
Stork, David G. (ed) and Arthur C. Clarke. HAL's Legacy: 2001's Computer Dream and Reality. Boston: MIT Press, 1998.
Electronic
Cray Corporation. "What Is a Supercomputer?" Supercomputing. 2002. <http://www.cray.com/supercomputing>(15 December 2002).
The History of Computing Foundation. "Introduction to Supercomputers." Supercomputers. October 13, 2002. <http://www.thocp.net/hardware/supercomputers.htm>(15 December 2002).
Random House Word Menu:
categories related to 'supercomputer' |

Wikipedia on Answers.com:
Supercomputer |
A supercomputer is a computer at the frontline of current processing capacity, particularly speed of calculation. Supercomputers were introduced in the 1960s and were designed primarily by Seymour Cray at Control Data Corporation (CDC), and later at Cray Research. While the supercomputers of the 1970s used only a few processors, in the 1990s, machines with thousands of processors began to appear and by the end of the 20th century, massively parallel supercomputers with tens of thousands of "off-the-shelf" processors were the norm.[2][3]
Systems with a massive number of processors generally take one of two paths: in one approach, e.g. in grid computing the processing power of a large number of computers in distributed, diverse administrative domains, is opportunistically used whenever a computer is available.[4]. In another approach, a large number of processors are used in close proximity to each other, e.g. in a computer cluster. The use of multi-core processors combined with centralization is an emerging direction.[5][6] Currently, Japan's K computer (a cluster) is the fastest in the world.[7]
Supercomputers are used for highly calculation-intensive tasks such as problems including quantum physics, 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 simulation of airplanes in wind tunnels, simulation of the detonation of nuclear weapons, and research into nuclear fusion).
|
Contents
|
The history of supercomputing goes back to the 1960s when a series of computers at Control Data Corporation (CDC) were designed by Seymour Cray to use innovative designs and parallelism to achieve superior computational peak performance.[8] The CDC 6600, released in 1964, is generally considered the first supercomputer.[9][10]
Cray left CDC in 1972 to form his own company.[11] Four years after leaving CDC, Cray delivered the 80 MHz Cray 1 in 1976, and it become one of the most successful supercomputers in history.[12][13] The Cray-2 released in 1985 was an 8 processor liquid cooled computer and Fluorinert was pumped through it as it operated. It performed at 1.9 gigaflops and was the world's fastest until 1990.[14]
While the supercomputers of the 1980s used only a few processors, in the 1990s, machines with thousands of processors began to appear both in the United States and in Japan, setting new computational performance records. 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 per processor.[15][16] The Hitachi SR2201 obtained a peak performance of 600 gigaflops in 1996 by using 2048 processors connected via a fast three dimensional crossbar network.[17][18][19] 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.[20]
Approaches to supercomputer architecture have taken dramatic turns since the earliest systems were introduced in the 1960s. Early supercomputer architectures pioneered by Seymour Cray relied on compact innovative designs and local parallelism to achieve superior computational peak performance.[8] However, in time the demand for increased computational power ushered in the age of massively parallel systems.
While the supercomputers of the 1970s used only a few processors, in the 1990s, machines with thousands of processors began to appear and by the end of the 20th century, massively parallel supercomputers with tens of thousands of "off-the-shelf" processors were the norm. Supercomputers of the 21st century can use over 100,000 processors (some being graphic units) connected by fast connections.[2][3]
Throughout the decades, the management of heat density has remained a key issue for most centralized supercomputers.[21][22][23] The large amount of heat generated by a system may also have other effects, e.g. reducing the lifetime of other system components.[24] 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.[14][25]
Systems with a massive number of processors generally take one of two paths: in one approach, e.g. in grid computing the processing power of a large number of computers in distributed, diverse administrative domains, is opportunistically used whenever a computer is available.[4]. In another approach, a large number of processors are used in close proximity to each other, e.g. in a computer cluster. 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.[26][27] The use of multi-core processors combined with centralization is an emerging direction, e.g. as in the Cyclops64 system.[28][6]
As the price/performance of general purpose graphic processors (GPGPUs) has improved, a number of petaflop supercomputers such as Tianhe-I and Nebulae have started to rely on them.[29] 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 maybe 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 towards it.[30] However, GPUs are gaining ground and in 2012 the Jaguar supercomputer was transformed into Titan by replacing CPUs with GPUs.[31][32][33]
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 VLSI chips, allowing higher price/performance ratios by sacrificing generality. Examples of special-purpose supercomputers include Belle,[34] Deep Blue,[35] and Hydra,[36] for playing chess, Gravity Pipe for astrophysics [37], MDGRAPE-3 for protein structure computation molecular dynamics[38] and Deep Crack,[39] for breaking the DES cipher.
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 of electricity.[40] The cost to power and cool the system can be significant, e.g. 4MW at $0.10/KWh is $400 an hour or about $3.5 million per year.
Heat management is a major issue in complex electronic devices, and affects powerful computer systems in various ways.[41] 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.[42] [43][44]
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.[14] 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.[25]
In the Blue Gene system IBM deliberately used low power processors to deal with heat density.[45] On the other hand, the IBM Power 775, released in 2011, has closely packed elements that require water cooling.[46] The IBM Aquasar system, on the other hand uses hot water cooling to achieve energy efficiency, the water being used to heat buildings as well.[47][48]
The energy efficiency of computer systems is generally measured in terms of "FLOPS per Watt". In 2008 IBM's Roadrunner operated at 376 MFLOPS/Watt.[49][50] In November 2010, the Blue Gene/Q reached 1684 MFLOPS/Watt.[51][52] In June 2011 the top 2 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.[53]
| Parts of this article (those related to section) are outdated. Please update this article to reflect recent events or newly available information. Please see the talk page for more information. (July 2011) |
Supercomputers today most often use variants of the Linux operating system as shown by the graph to the right.[54]
Until the early-to-mid-1980s, supercomputers usually sacrificed instruction set compatibility and code portability for performance (processing and memory access speed). For the most part, supercomputers to this time (unlike high-end mainframes) had vastly different operating systems. The Cray-1 alone had at least six different proprietary OSs largely unknown to the general computing community. In a similar manner, different and incompatible vectorizing and parallelizing compilers for Fortran existed. This trend would have continued with the ETA-10 were it not for the initial instruction set compatibility between the Cray-1 and the Cray X-MP, and the adoption of computer systems such as Cray's Unicos, or Linux.
The parallel architectures of supercomputers often dictate the use of special programming techniques to exploit their speed.
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.
Software tools for distributed processing include standard APIs such as MPI and PVM, VTL, and open source-based software solutions such as Beowulf.
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 can not handle traditional supercomputing tasks such as fluid dynamic simulations.
The fastest grid computing system, Folding@home, which is based on BOINC, reported 8.8 petaflops of processing power as of May 2011[update]. Of this, 7.1 petaflops are contributed by clients running on various GPUs, 1.8 petaflops come from PlayStation 3 systems, and the rest from various computer systems.[55]
The BOINC platform hosts a number of distributed computing projects. As of May 2011[update], BOINC recorded a processing power of over 5.5 petaflops through over 480,000 active computers on the network[56] The most active project (measured by computational power), MilkyWay@home, reports processing power of over 700 teraflops through over 33,000 active computers.[57]
As of May 2011[update], GIMPS's distributed Mersenne Prime search currently achieves about 60 teraflops through over 25,000 registered computers.[58] The Internet PrimeNet Server supports GIMPS's grid computing approach, one of the earliest and most successful grid computing projects, since 1997.
Quasi-opportunistic Supercomputing is a form of distributed computing whereby the “super virtual computer” of a large number of networked geographically disperse computers performs huge processing power demanding computing tasks.[59] 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 implementation of grid-wise allocation agreements, co-allocation subsystems, communication topology-aware allocation mechanisms, fault tolerant message passing libraries and data pre-conditioning.[59]
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.[60]
Capacity computing in contrast is typically thought of as using efficient cost-effective computing power to solve a small number of somewhat large problems or a large number of small problems, e.g. many user access requests to a database or a web site.[60] 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.[60]
In general, the speed of supercomputers is measured and benchmarked in "FLOPS" (FLoating Point Operations Per Second), and not in terms of MIPS, i.e. as "instructions per second", as is the case with general purpose computers.[61] These measuremens are commonly used with an SI prefix such as tera-, combined into the shorthand "TFLOPS" (1012 FLOPS, pronounced teraflops), or peta-, combined into the shorthand "PFLOPS" (1015 FLOPS, pronounced petaflops.) "Petascale" supercomputers can process one quadrillion (1015) (1000 trillion) FLOPS. Exascale is computing performance in the exaflops range. An exaflop is one quintillion (1018) FLOPS (one million teraflops).
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.[62]. 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.[62]
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,[63] and the "Peak speed" is given as the "Rmax" rating. For more historical data see History of supercomputing.
| Year | Supercomputer | Peak speed (Rmax) |
Location |
|---|---|---|---|
| 2008 | IBM Roadrunner | 1.026 PFLOPS | DoE-Los Alamos National Laboratory, New Mexico, USA |
| 1.105 PFLOPS | |||
| 2009 | Cray Jaguar | 1.759 PFLOPS | DoE-Oak Ridge National Laboratory, Tennessee, USA |
| 2010 | Tianhe-IA | 2.566 PFLOPS | National Supercomputing Center, Tianjin, China |
| 2011 | Fujitsu K computer | 10.51 PFLOPS | RIKEN, Kobe, Japan |
The K computer is the worlds fastest supercomputer at 10.51 petaflops. It consists of 88,000 SPARC64 VIIIfx CPUs, and spans 864 server racks. In November 2011, the power consumption was reported to be 12659.89 kW[64] The operating costs for the system are about $10M per year.[65]
The stages of supercomputer application may be summarized in the following table:
| Decade | Uses and computer involved |
|---|---|
| 1970s | Weather forecasting, aerodynamic research (Cray-1).[66] |
| 1980s | Probabilistic analysis,[67] radiation shielding modeling[68] (CDC Cyber). |
| 1990s | Brute force code breaking (EFF DES cracker),[69]
3D nuclear test simulations as a substitute for legal conduct Nuclear Proliferation Treaty (ASCI Q).[70] |
| 2010s | Molecular Dynamics Simulation (Tianhe-1A)[71] |
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.[72]
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.[73]
In 2011, the challenges and difficulties in pushing the envelope in supercomputing were underscored by IBM's abandonment of the Blue Waters petascale project.[74]
IBM is developing the Cyclops64 architecture, intended to create a "supercomputer on a chip". IBM is also constructing a 20 PFLOPs supercomputer at Lawrence Livermore National Laboratory, named Sequoia, based on the Blue Gene architecture which is scheduled to go online in 2012.
Given the current speed of progress, supercomputers are projected to reach 1 exaflops (1018) (one quintillion FLOPS) in 2019.[75] Using the Intel MIC multi-core processor architecture, which is Intel's response to GPU systems, SGI plans to achieve a 500 times increase in performance by 2018 to achieve an exaflop.[76] Samples of MIC chips with 32 cores which combine vector processing units with standard CPU have become available.[76]
On October 11, 2011, the Oak Ridge National Laboratory announced they were building a 20 petaflop supercomputer, named Titan, which will become operational in 2012, the hybrid Titan system will combine AMD Opteron processors with Nvidia GeForce 600 "Kepler" graphic processing unit (GPU) technologies.[31]
Erik P. DeBenedictis of Sandia National Laboratories theorizes that a zettaflops (1021) (one sextillion FLOPS) computer is required to accomplish full weather modeling, which could cover a two week time span accurately.[77] Such systems might be built around 2030.[78]
The Indian government has committed about $940 million to develop the world's fastest supercomputer by 2017. The Planning Commission of India has agreed to provide the funds to ISRO and to the Indian Institute of Science (IISc), Bangalore to develop a supercomputer with a performance of 132.8 exaflops, about 1,000 times faster than the 2012 fastest computers.[79]
| Wikimedia Commons has media related to: Supercomputers |
|
||||||||||||||||||||||||||||||||||||||
|
|||||||||||||||||||||||||||||||||||||
This entry is from Wikipedia, the leading user-contributed encyclopedia. It may not have been reviewed by professional editors (see full disclaimer)
Translations:
Supercomputer |
Dansk (Danish)
n. - supercomputer
Nederlands (Dutch)
supercomputer
Français (French)
n. - supercalculateur
Deutsch (German)
n. - Supercomputer
Ελληνική (Greek)
n. - (Η/Υ) υπερυπολογιστής
Italiano (Italian)
supercomputer
Português (Portuguese)
n. - supercomputador (m)
Русский (Russian)
суперкомпьютер, сверхмощная, сверхбыстро- действующая ЭВМ
Español (Spanish)
n. - superordenador
Svenska (Swedish)
n. - superdator
中文(简体)(Chinese (Simplified))
超型计算机
中文(繁體)(Chinese (Traditional))
n. - 超型電腦
한국어 (Korean)
n. - 슈퍼 컴퓨터, 초고속 전자 계산기
日本語 (Japanese)
n. - スーパーコンピューター
العربيه (Arabic)
(الاسم) الحاسوب العملاق
If you are unable to view some languages clearly, click here.
To select your translation preferences click here.
| BlueGene (technology) | |
| mini-supercomputer (technology) | |
| virtual supercomputer (technology) |
| What rhymes with supercomputer? Read answer... | |
| Where is a supercomputer located? Read answer... | |
| What is the accuracy of supercomputer? Read answer... |
| What is pioneer in supercomputers? | |
| What are the types of supercomputer? | |
| When was the supercomputer created? |
Copyrights:
![]() |
![]() | American Heritage Dictionary. The American Heritage® Dictionary of the English Language, Fourth Edition Copyright © 2007, 2000 by Houghton Mifflin Company. Updated in 2009. Published by Houghton Mifflin Company. All rights reserved. Read more |
![]() | Britannica Concise Encyclopedia. Britannica Concise Encyclopedia. © 1994-2012 Encyclopædia Britannica, Inc. All rights reserved. Read more | |
![]() |
![]() | McGraw-Hill Science & Technology Encyclopedia. McGraw-Hill Encyclopedia of Science and Technology. Copyright © 2005 by The McGraw-Hill Companies, Inc. All rights reserved. Read more |
![]() |
![]() | TechEncyclopedia. THIS DEFINITION IS FOR PERSONAL USE ONLY. All other reproduction is strictly prohibited without permission from the publisher. © 1981-2012 The Computer Language Company Inc. All rights reserved. Read more |
![]() | Barron's Accounting Dictionary. Dictionary of Accounting Terms. Copyright © 2010 by Barron's Educational Series, Inc. All rights reserved. Read more | |
![]() |
![]() | Columbia Encyclopedia. The Columbia Electronic Encyclopedia, Sixth Edition Copyright © 2012, Columbia University Press. Licensed from Columbia University Press. All rights reserved. www.cc.columbia.edu/cu/cup/. Read more |
![]() |
![]() | Gale Encyclopedia of Espionage & Intelligence. Encyclopedia of Espionage, Intelligence, and Security. Copyright © 2004 by The Gale Group, Inc. All rights reserved. Read more |
![]() |
![]() | Random House Word Menu. © 2010 Write Brothers Inc. Word Menu is a registered trademark of the Estate of Stephen Glazier. Write Brothers Inc. All rights reserved. Read more |
![]() |
![]() | Wikipedia on Answers.com. This article is licensed under the Creative Commons Attribution/Share-Alike License. It uses material from the Wikipedia article Supercomputer. Read more |
![]() | Translations. Copyright © 2007, WizCom Technologies Ltd. All rights reserved. Read more |
Mentioned in