Supercomputer - wikipedia Supercomputer `` High - performance computing '' redirects here. For narrower definitions of HPC, see high - throughput computing and many - task computing. For other uses, see supercomputer ( disambiguation ). 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 3 - D torus network. A supercomputer is a computer with a high level of performance compared to a general - purpose computer. Performance of a supercomputer is measured in floating - point operations per second ( FLOPS ) instead of million instructions per second ( MIPS ). As of 2017, there are supercomputers which can perform up to nearly a hundred quadrillion FLOPS. As of November 2017, all of the world 's fastest 500 supercomputers run Linux - based operating systems. Additional research is being conducted in China, the United States, the European Union, Taiwan and Japan to build even faster, more powerful and more 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 ). Throughout their history, they have been essential in the field of cryptanalysis. Supercomputers were introduced in the 1960s, and for several decades the fastest were 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 faster than their more general - purpose contemporaries. Through the 1960s, they began to add increasing amounts of parallelism with one to four processors being typical. From the 1970s, the vector computing concept with specialized math units 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 a 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, but since then China has become increasingly active in the field. As of June 2018, the fastest supercomputer on the TOP500 supercomputer list is the Summit, in the United States, with a LINPACK benchmark score of 122.3 PFLOPS, exceeding the previous record holder, Sunway TaihuLight, by around 29 PFLOPS. Sunway TaihuLight 's is notable for its use of indigenous chips and is the first Chinese computer to enter the TOP500 list without using hardware from the United States. As of June 2018, China had more computers ( 206 ) on the TOP500 list than the United States ( 124 ) ; however, US built computers held eight of the top 20 positions ; the U.S. has six of the top 10 and China has two. Contents 1 History 1.1 Massively parallel designs 2 Special purpose supercomputers 3 Energy usage and heat management 4 Software and system management 4.1 Operating systems 4.2 Software tools and message passing 5 Distributed supercomputing 5.1 Opportunistic approaches 5.2 Quasi-opportunistic approaches 6 HPC in the Cloud 7 Performance measurement 7.1 Capability versus capacity 7.2 Performance metrics 7.3 The TOP500 list 8 Largest Supercomputer Vendors according to the total Rmax ( GFLOPS ) operated 9 Applications 10 Development and trends 11 In fiction 12 See also 13 Notes and references 14 External links History Main article : History of supercomputing 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 Sperry Rand built the Livermore Atomic Research Computer ( LARC ), today considered among the first supercomputers, for the US Navy Research and Development Centre. 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 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 programe could be executed on the supercomputer at any one time. Atlas was a joint venture between Ferranti and the 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 faster 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 performed at 1.9 gigaFLOPS and was the world 's second fastest after M - 13 supercomputer in Moscow. Massively parallel designs Main articles : Supercomputer architecture and Parallel computer hardware A Blue Gene / L cabinet 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 faster 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 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. The CPU share of TOP500 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 another approach, a large number of 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 interconnect 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 graphic processors ( 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 towards 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. Special purpose supercomputers 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 computation molecular dynamics and Deep Crack, for breaking the DES cipher. Energy Usage and heat management See also : Computer cooling and Green 500 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, IBM 's Roadrunner operated at 3.76 MFLOPS / W. In November 2010, the Blue Gene / Q reached 1,684 MFLOPS / W. 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. 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. Software and system management Operating systems Main article : Supercomputer operating systems 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 the Linux 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. Software tools and message passing Main article : Message passing in computer clusters See also : Parallel computing and Parallel programming model Wide - angle view of the ALMA correlator 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 - based software solutions 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. Distributed supercomputing Opportunistic approaches Main article : Grid computing 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 can not handle traditional supercomputing tasks such as fluid dynamic simulations. The fastest grid computing system is the distributed computing project Folding@home ( F@h ). F@h reported 101 PFLOPS of x86 processing power As of October 2016. 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 distributed 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 Internet PrimeNet Server supports GIMPS 's grid computing approach, one of the earliest and most successful grid computing projects, since 1997. Quasi-opportunistic approaches Main article : Quasi-opportunistic supercomputing 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 implementation of grid-wise allocation agreements, co-allocation subsystems, communication topology - aware allocation mechanisms, fault tolerant message passing libraries and data pre-conditioning. HPC in the Cloud Cloud Computing with its recent and rapid expansions and development have grabbed the attention of HPC users and developers in recent years. Cloud Computing attempts to provide HPC - as - a-Service exactly like other forms of services currently 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. Performance measurement Capability versus capacity 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. Performance metrics See also : LINPACK benchmarks 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 FLOPS, pronounced teraflops ), or peta -, combined into the shorthand `` PFLOPS '' ( 10 FLOPS, pronounced petaflops. ) `` Petascale '' supercomputers can process one quadrillion ( 10 ) ( 1000 trillion ) FLOPS. Exascale is computing performance in the exaFLOPS ( EFLOPS ) range. An EFLOPS is one quintillion ( 10 ) FLOPS ( one million TFLOPS ). 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. The TOP500 list Main article : TOP500 Further information : History of supercomputing Distribution of TOP500 supercomputers among different countries, as of November 2015 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. Top 20 Supercomputers in the World, as of June 2014 Year Supercomputer Peak speed ( Rmax ) Location 2018 IBM Summit 122.3 PFLOPS Oak Ridge, U.S. 2016 Sunway TaihuLight 93.01 PFLOPS Wuxi, China 2013 NUDT Tianhe - 2 33.86 PFLOPS Guangzhou, China 2012 Cray Titan 17.59 PFLOPS Oak Ridge, U.S. 2012 IBM Sequoia 17.17 PFLOPS Livermore, U.S. 2011 Fujitsu K computer 10.51 PFLOPS Kobe, Japan Tianhe - I A 2.566 PFLOPS Tianjin, China 2009 Cray Jaguar 1.759 PFLOPS Oak Ridge, U.S. 2008 IBM Roadrunner 1.026 PFLOPS Los Alamos, U.S. 1.105 PFLOPS Largest supercomputer vendors according to the total Rmax ( GFLOPS ) operated See also : Grid computing § Fastest virtual supercomputers Source : TOP500 In 2018 Lenovo became the worlds largest provider ( 117 ) for the top500 supercomputers. Country / Vendor System count System share ( % ) Rmax ( GFLOPS ) Rpeak ( GFLOPS ) Processor cores IBM 27 5.4 56,428,002 67,161,639 4,611,236 Cray Inc. 57 11.4 160,476,360 229,400,160 5,981,864 HP 143 28.6 124,430,645 181,738,373 4,996,780 NUDT 0.8 39,271,790 64,020,685 3,534,336 SGI 23 4.6 14,741,773 17,963,102 813,376 Fujitsu 11 37,624,378 51,859,986 1,753,368 Bull 18 3.6 24,362,683 31,212,663 978,924 Dell 15 3.0 24,528,727 42,623,632 1,247,118 Atipa Technologies 0.6 3,044,976 4,163,712 214,584 NEC / HP 0.2 2,785,000 5,735,685 76,032 T - Platforms 0.6 4,428,620 6,355,903 170,824 RSC Group 0.2 658,112 829,338 19,936 Dawning 0.4 1,451,600 3,217,772 151,360 Hitachi / Fujitsu 0.2 1,018,000 1,502,236 222,072 Supermicro 0.2 602,983 677,376 20,160 NRCPCET 0.2 795,900 1,070,160 137,200 ClusterVision 0.4 784,735 881,254 42,368 Intel 0.2 758,873 933,481 51,392 Amazon 0.4 724,269 947,610 43,520 Oracle 0.4 708,300 804,835 68,672 MEGWARE 0.6 610,521 710,592 54,800 NEC 0.6 578,987 709,520 21,296 Adtech 0.2 532,600 1,098,000 38,400 Hitachi 0.4 496,900 622,598 20,544 IPE, Nvidia, Tyan 0.2 496,500 1,012,650 29,440 Itautec 0.4 411,800 920,830 27,776 Netweb Technologies 0.2 388,442 520,358 30,056 Xenon Systems 0.2 335,300 472,498 6,875 AMD, ASUS, FIAS, GSI 0.2 316,700 593,600 10,976 Clustervision / Supermicro 0.2 299,300 588,749 44,928 Niagara Computers, Supermicro 0.2 289,500 348,660 5,310 Inspur 0.2 196,234 262,560 8,412 HP / WIPRO 0.2 188,700 394,760 12,532 PEZY Computing / Exascaler Inc. 0.2 178,107 395,264 262,784 Acer Group 0.2 177,100 231,859 26,244 Applications The stages of supercomputer application may be summarized in the following table : Decade Uses and computer involved 1970s Weather forecasting, aerodynamic research ( Cray - 1 ). 1980s Probabilistic analysis, radiation shielding modeling ( CDC Cyber ). 1990s Brute force code breaking ( EFF DES cracker ). 2000s 3D nuclear test simulations as a substitute for legal conduct Nuclear Non-Proliferation Treaty ( ASCI Q ). 2010s Molecular Dynamics Simulation ( Tianhe - 1A ) 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. Development and Trends Diagram of a three - dimensional torus interconnect used by systems such as Blue Gene, Cray XT3, etc. In the 2010s, China, the United States, the European Union, and others competed to be the first to create a 1 exaFLOP ( 10 or one quintillion FLOPS ) supercomputer. Erik P. DeBenedictis of Sandia National Laboratories has theorized that a zettaFLOPS ( 10 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 kilowatt, in 2010 the top 10 supercomputers required between 1 and 2 megawatt. 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 dollar. 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 megawatt. Operating systems were developed for existing hardware to conserve energy whenever possible. CPU cores not in use during the execution of a parallelised 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 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. Iceland built the world 's first zero - emission supercomputer. 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. 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. In fiction Main article : AI takeover 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. Examples of supercomputers in fiction include HAL - 9000, Multivac, The Machine Stops, GLaDOS, The Evitable Conflict and Vulcan 's Hammer. See also Wikimedia Commons has media related to Supercomputers. ACM / IEEE Supercomputing Conference Jungle computing Nvidia Tesla Personal Supercomputer Parallel computing Supercomputing in China Supercomputing in Europe Supercomputing in India Supercomputing in Japan Testing high - performance computing applications Ultra Network Technologies Notes and references Jump up ^ `` IBM Blue gene announcement ''. 03.ibm.com. 26 June 2007. Retrieved 9 June 2012. Jump up ^ `` Argonne National Laboratory, Intrepid ''. Retrieved 24 May 2017. ^ Jump up to : `` The List : June 2018 ''. Top 500. Retrieved 25 June 2018. Jump up ^ `` Operating system Family / Linux ''. TOP500.org. Retrieved 30 November 2017. Jump up ^ https://spectrum.ieee.org/tech-talk/computing/hardware/global-race-toward-exascale-will-drive-supercomputing-ai-to-masses Jump up ^ Lemke, Tim ( 8 May 2013 ). `` NSA Breaks Ground on Massive Computing Center ''. Retrieved 11 December 2013. ^ Jump up to : Hoffman, Allan R. ; et al. ( 1990 ). Supercomputers : directions in technology and applications. National Academies. pp. 35 -- 47. ISBN 0 - 309 - 04088 - 4. ^ Jump up to : Hill, Mark Donald ; Jouppi, Norman Paul ; Sohi, Gurindar ( 1999 ). Readings in computer architecture. pp. 40 -- 49. ISBN 1 - 55860 - 539 - 8. Jump up ^ https://www.top500.org/news/china-tops-supercomputer-rankings-with-new-93-petaflop-machine/ Jump up ^ Clark, Don, China computer claims top speed, Wall Street Journal, 21 June 2016, p. B4 Jump up ^ Markoff, John, China crowds top computer list, New York Times, 21 June 2016, page B1 Jump up ^ Eric G. Swedin & David L. Ferro ( 2007 ). Computers : The Life Story of a Technology. JHU Press. p. 57. ISBN 9780801887741. CS1 maint : Uses authors parameter ( link ) Jump up ^ Eric G. Swedin & David L. Ferro ( 2007 ). Computers : The Life Story of a Technology. JHU Press. p. 56. ISBN 9780801887741. CS1 maint : Uses authors parameter ( link ) Jump up ^ Eric G. Swedin & David L. Ferro ( 2007 ). Computers : The Life Story of a Technology. JHU Press. p. 58. ISBN 9780801887741. CS1 maint : Uses authors parameter ( link ) Jump up ^ The Atlas, University of Manchester, archived from the original on 28 July 2012, retrieved 21 September 2010 Jump up ^ The Supermen, Charles Murray, Wiley & Sons, 1997. Jump up ^ Paul E. Ceruzzi ( 2003 ). A History of Modern Computing. MIT Press. p. 161. ISBN 978 - 0 - 262 - 53203 - 7. ^ Jump up to : Hannan, Caryn ( 2008 ). Wisconsin Biographical Dictionary. State History Publications. pp. 83 -- 84. ISBN 1 - 878592 - 63 - 7. Jump up ^ John Impagliazzo ; John A.N. Lee ( 2004 ). History of computing in education. p. 172. ISBN 1 - 4020 - 8135 - 9. Jump up ^ Andrew R.L. Cayton ; Richard Sisson ; Chris Zacher ( 2006 ). The American Midwest : An Interpretive Encyclopedia. Indiana University Press. p. 1489. ISBN 0 - 253 - 00349 - 0. Jump up ^ Readings in computer architecture by Mark Donald Hill, Norman Paul Jouppi, Gurindar Sohi 1999 ISBN 978 - 1 - 55860 - 539 - 8 page 41 - 48 Jump up ^ Milestones in computer science and information technology by Edwin D. Reilly 2003 ISBN 1 - 57356 - 521 - 0 page 65 Jump up ^ http://www.icfcst.kiev.ua/MUSEUM/Kartsev.html Jump up ^ Seymour Cray quotes Jump up ^ Steve Nelson ( 3 October 2014 ). `` ComputerGK.com : Supercomputers ''. Jump up ^ http://museum.ipsj.or.jp/en/computer/other/0013.html Jump up ^ `` TOP500 Annual Report 1994 ''. Netlib.org. 1 October 1996. Retrieved 9 June 2012. Jump up ^ N. Hirose & M. Fukuda ( 1997 ). Numerical Wind Tunnel ( NWT ) and CFD Research at National Aerospace Laboratory. Proceedings of HPC - Asia ' 97. IEEE Computer SocietyPages. doi : 10.1109 / HPC. 1997.592130. Jump up ^ H. Fujii, Y. Yasuda, H. Akashi, Y. Inagami, M. Koga, O. Ishihara, M. Syazwan, H. Wada, T. Sumimoto, Architecture and performance of the Hitachi SR2201 massively parallel processor system, Proceedings of 11th International Parallel Processing Symposium, April 1997, pages 233 -- 241. Jump up ^ Y. Iwasaki, The CP - PACS project, Nuclear Physics B : Proceedings Supplements, Volume 60, Issues 1 -- 2, January 1998, pages 246 -- 254. Jump up ^ A.J. van der Steen, Overview of recent supercomputers, Publication of the NCF, Stichting Nationale Computer Faciliteiten, the Netherlands, January 1997. Jump up ^ Scalable input / output : achieving system balance by Daniel A. Reed 2003 ISBN 978 - 0 - 262 - 68142 - 1 page 182 Jump up ^ Prodan, Radu ; Fahringer, Thomas ( 2007 ). Grid computing : experiment management, tool integration, and scientific workflows. pp. 1 -- 4. ISBN 3 - 540 - 69261 - 4. Jump up ^ Knight, Will : `` IBM creates world 's most powerful computer '', NewScientist.com news service, June 2007 Jump up ^ N.R. Agida ; et al. ( 2005 ). `` Blue Gene / L Torus Interconnection Network IBM Journal of Research and Development '' ( PDF ). Torus Interconnection Network. p. 265. Archived from the original ( PDF ) on 15 August 2011. Jump up ^ Performance Modelling and Optimization of Memory Access on Cellular Computer Architecture Cyclops64 K Barner, GR Gao, Z Hu, Lecture Notes in Computer Science, 2005, Volume 3779, Network and Parallel Computing, pages 132 -- 143 Jump up ^ Analysis and performance results of computing betweenness centrality on IBM Cyclops64 by Guangming Tan, Vugranam C. Sreedhar and Guang R. Gao The Journal of Supercomputing Volume 56, Number 1, 1 -- 24 September 2011 Jump up ^ Mittal et al., `` A Survey of Methods for Analyzing and Improving GPU Energy Efficiency '', ACM Computing Surveys, 2014. Jump up ^ Prickett, Timothy ( 31 May 2010 ). `` Top 500 supers -- The Dawning of the GPUs ''. Theregister.co.uk. Jump up ^ `` A Survey of CPU - GPU Heterogeneous Computing Techniques '', ACM Computing Surveys, 2015 Jump up ^ Hans Hacker ; Carsten Trinitis ; Josef Weidendorfer ; Matthias Brehm ( 2010 ). `` Considering GPGPU for HPC Centers : Is It Worth the Effort ? ''. In Rainer Keller ; David Kramer ; Jan - Philipp Weiss. Facing the Multicore - Challenge : Aspects of New Paradigms and Technologies in Parallel Computing. Springer Science & Business Media. pp. 118 -- 121. ISBN 3 - 642 - 16232 - 0. Jump up ^ Damon Poeter ( 11 October 2011 ). `` Cray 's Titan Supercomputer for ORNL Could Be World 's Fastest ''. Pcmag.com. Jump up ^ Feldman, Michael ( 11 October 2011 ). `` GPUs Will Morph ORNL 's Jaguar Into 20 - Petaflop Titan ''. Hpcwire.com. Jump up ^ Timothy Prickett Morgan ( 11 October 2011 ). `` Oak Ridge changes Jaguar 's spots from CPUs to GPUs ''. Theregister.co.uk. Jump up ^ `` The NETL SuperComputer ''. page 2. Jump up ^ Condon, J.H. and K. Thompson, `` Belle Chess Hardware '', In Advances in Computer Chess 3 ( ed. M.R.B. Clarke ), Pergamon Press, 1982. Jump up ^ Hsu, Feng - hsiung ( 2002 ). `` Behind Deep Blue : Building the Computer that Defeated the World Chess Champion ''. Princeton University Press. ISBN 0 - 691 - 09065 - 3. Jump up ^ C. Donninger, U. Lorenz. The Chess Monster Hydra. Proc. of 14th International Conference on Field - Programmable Logic and Applications ( FPL ), 2004, Antwerp -- Belgium, LNCS 3203, pp. 927 -- 932 Jump up ^ J Makino and M. Taiji, Scientific Simulations with Special Purpose Computers : The GRAPE Systems, Wiley. 1998. Jump up ^ RIKEN press release, Completion of a one - petaFLOPS computer system for simulation of molecular dynamics Jump up ^ Electronic Frontier Foundation ( 1998 ). Cracking DES -- Secrets of Encryption Research, Wiretap Politics & Chip Design. Oreilly & Associates Inc. ISBN 1 - 56592 - 520 - 3. Archived from the original on 12 November 2004. Jump up ^ Xue - June Yang, Xiang - Ke Liao, et al in Journal of Computer Science and Technology. `` The TianHe - 1A Supercomputer : Its Hardware and Software ''. pp. 344 -- 351. CS1 maint : Multiple names : authors list ( link ) Jump up ^ The Supermen : Story of Seymour Cray and the Technical Wizards Behind the Supercomputer by Charles J. Murray 1997, ISBN 0 - 471 - 04885 - 2, pages 133 -- 135 Jump up ^ Parallel Computational Fluid Dyynamics ; Recent Advances and Future Directions edited by Rupak Biswas 2010 ISBN 1 - 60595 - 022 - X page 401 Jump up ^ Supercomputing Research Advances by Yongge Huáng 2008, ISBN 1 - 60456 - 186 - 6, pages 313 -- 314 ^ Jump up to : Parallel computing for real - time signal processing and control by M.O. Tokhi, Mohammad Alamgir Hossain 2003, ISBN 978 - 1 - 85233 - 599 - 1, pages 201 -- 202 ^ Jump up to : Computational science -- ICCS 2005 : 5th international conference edited by Vaidy S. Sunderam 2005, ISBN 3 - 540 - 26043 - 9, pages 60 -- 67 Jump up ^ `` NVIDIA Tesla GPUs Power World 's Fastest Supercomputer '' ( Press release ). Nvidia. 29 October 2010. Jump up ^ Balandin, Alexander A. ( October 2009 ). `` Better Computing Through CPU Cooling ''. Spectrum.ieee.org. Jump up ^ `` The Green 500 ''. Green500.org. Jump up ^ `` Green 500 list ranks supercomputers ''. iTnews Australia. Archived from the original on 22 October 2008. Jump up ^ Wu - chun Feng ( 2003 ). `` Making a Case for Efficient Supercomputing ACM Queue Magazine, Volume 1 Issue 7, 10 January 2003 doi 10.1145 / 957717.957772 '' ( PDF ). Archived from the original ( PDF ) on 30 March 2012. Jump up ^ `` IBM uncloaks 20 petaflops BlueGene / Q super ''. The Register. 22 November 2010. Retrieved 25 November 2010. Jump up ^ Prickett, Timothy ( 15 July 2011 ). `` The Register : IBM ' Blue Waters ' super node washes ashore in August ''. Theregister.co.uk. Retrieved 9 June 2012. Jump up ^ `` HPC Wire 2 July 2010 ''. Hpcwire.com. 2 July 2010. Archived from the original on 13 August 2012. Retrieved 9 June 2012. Jump up ^ Martin LaMonica ( 10 May 2010 ). `` CNet 10 May 2010 ''. News.cnet.com. Retrieved 9 June 2012. Jump up ^ `` Government unveils world 's fastest computer ''. CNN. Archived from the original on 10 June 2008. performing 376 million calculations for every watt of electricity used. Jump up ^ `` IBM Roadrunner Takes the Gold in the Petaflop Race ''. Archived from the original on 17 December 2008. Jump up ^ `` Top500 Supercomputing List Reveals Computing Trends ''. IBM... BlueGene / Q system... setting a record in power efficiency with a value of 1,680 MFLOPS / W, more than twice that of the next best system. Jump up ^ `` IBM Research A Clear Winner in Green 500 ''. Jump up ^ `` Green 500 list ''. Green500.org. Archived from the original on 3 July 2011. Retrieved 9 June 2012. Jump up ^ Saed G. Younis. `` Asymptotically Zero Energy Computing Using Split - Level Charge Recovery Logic ''. 1994. page 14. Jump up ^ `` Hot Topic -- the Problem of Cooling Supercomputers '' Archived 18 January 2015 at the Wayback Machine... Jump up ^ Anand Lal Shimpi. `` Inside the Titan Supercomputer : 299K AMD x86 Cores and 18.6 K NVIDIA GPUs ''. 2012. Jump up ^ Curtis Storlie ; Joe Sexton ; Scott Pakin ; Michael Lang ; Brian Reich ; William Rust. `` Modeling and Predicting Power Consumption of High - Performance Computing Jobs ''. 2014. ^ Jump up to : Encyclopedia of Parallel Computing by David Padua 2011 ISBN 0 - 387 - 09765 - 1 pages 426 -- 429 Jump up ^ Knowing machines : essays on technical change by Donald MacKenzie 1998 ISBN 0 - 262 - 63188 - 1 page 149 - 151 Jump up ^ Euro - Par 2004 Parallel Processing : 10th International Euro - Par Conference 2004, by Marco Danelutto, Marco Vanneschi and Domenico Laforenza, ISBN 3 - 540 - 22924 - 8, page 835 Jump up ^ Euro - Par 2006 Parallel Processing : 12th International Euro - Par Conference, 2006, by Wolfgang E. Nagel, Wolfgang V. Walter and Wolfgang Lehner ISBN 3 - 540 - 37783 - 2 page Jump up ^ An Evaluation of the Oak Ridge National Laboratory Cray XT3 by Sadaf R. Alam etal International Journal of High Performance Computing Applications February 2008 vol. 22 no. 1 52 -- 80 Jump up ^ Open Job Management Architecture for the Blue Gene / L Supercomputer by Yariv Aridor et al. in Job scheduling strategies for parallel processing by Dror G. Feitelson 2005 ISBN 978 - 3 - 540 - 31024 - 2 pages 95 -- 101 Jump up ^ `` Top500 OS chart ''. Top500.org. Archived from the original on 5 March 2012. Retrieved 31 October 2010. Jump up ^ `` Wide - angle view of the ALMA correlator ''. ESO Press Release. Retrieved 13 February 2013. Jump up ^ `` Folding@home : OS Statistics ''. Stanford University. Retrieved 30 October 2016. Jump up ^ `` BOINCstats : BOINC Combined ''. BOINC. Archived from the original on 19 September 2010. Retrieved 30 October 2016 Note this link will give current statistics, not those on the date last accessed. Jump up ^ `` Internet PrimeNet Server Distributed Computing Technology for the Great Internet Mersenne Prime Search ''. GIMPS. Retrieved 6 June 2011. ^ Jump up to : Kravtsov, Valentin ; Carmeli, David ; Dubitzky, Werner ; Orda, Ariel ; Schuster, Assaf ; Yoshpa, Benny. `` Quasi-opportunistic supercomputing in grids, hot topic paper ( 2007 ) ''. IEEE International Symposium on High Performance Distributed Computing. IEEE. Retrieved 4 August 2011. Jump up ^ Jamalian, S. ; Rajaei, H. ( 1 March 2015 ). `` ASETS : A SDN Empowered Task Scheduling System for HPCaaS on the Cloud ''. 2015 IEEE International Conference on Cloud Engineering : 329 -- 334. doi : 10.1109 / IC2E. 2015.56. ISBN 978 - 1 - 4799 - 8218 - 9. Jump up ^ Jamalian, S. ; Rajaei, H. ( 1 June 2015 ). `` Data - Intensive HPC Tasks Scheduling with SDN to Enable HPC - as - a-Service ''. 2015 IEEE 8th International Conference on Cloud Computing : 596 -- 603. doi : 10.1109 / CLOUD. 2015.85. ISBN 978 - 1 - 4673 - 7287 - 9. Jump up ^ Gupta, A. ; Milojicic, D. ( 1 October 2011 ). `` Evaluation of HPC Applications on Cloud ''. 2011 Sixth Open Cirrus Summit : 22 -- 26. doi : 10.1109 / OCS. 2011.10. ISBN 978 - 0 - 7695 - 4650 - 6. Jump up ^ Kim, H. ; el - Khamra, Y. ; Jha, S. ; Parashar, M. ( 1 December 2009 ). `` An Autonomic Approach to Integrated HPC Grid and Cloud Usage ''. 2009 Fifth IEEE International Conference on e-Science : 366 -- 373. doi : 10.1109 / e-Science. 2009.58. ISBN 978 - 1 - 4244 - 5340 - 5. ^ Jump up to : The Potential Impact of High - End Capability Computing on Four Illustrative Fields of Science and Engineering by Committee on the Potential Impact of High - End Computing on Illustrative Fields of Science and Engineering and National Research Council ( 28 October 2008 ) ISBN 0 - 309 - 12485 - 9 page 9 Jump up ^ Xingfu Wu ( 1999 ). Performance Evaluation, Prediction and Visualization of Parallel Systems. Springer Science & Business Media. pp. 114 -- 117. ISBN 0 - 7923 - 8462 - 8. ^ Jump up to : Dongarra, Jack J. ; Luszczek, Piotr ; Petitet, Antoine ( 2003 ), `` The LINPACK Benchmark : past, present and future '' ( PDF ), Concurrency and Computation : Practice and Experience, John Wiley & Sons, Ltd. : 803 -- 820 Jump up ^ `` Understanding measures of supercomputer performance and storage system capacity ''. Indiana University. Retrieved December 3, 2017. Jump up ^ `` Frequently Asked Questions ''. TOP500.org. Retrieved December 3, 2017. Jump up ^ Intel brochure -- 11 / 91. `` Directory page for Top500 lists. Result for each list since June 1993 ''. Top500.org. Retrieved 31 October 2010. Jump up ^ `` Lenovo Attains Status as Largest Global Provider of TOP500 Supercomputers ''. Business Wire. 25 June 2018. Jump up ^ `` The Cray - 1 Computer System '' ( PDF ). Cray Research, Inc. Retrieved 25 May 2011. Jump up ^ Joshi, Rajani R. ( 9 June 1998 ). `` A new heuristic algorithm for probabilistic optimization ''. Department of Mathematics and School of Biomedical Engineering, Indian Institute of Technology Powai, Bombay, India. doi : 10.1016 / S0305 - 0548 ( 96 ) 00056 - 1. ( Subscription required ( help ) ). Jump up ^ `` Abstract for SAMSY -- Shielding Analysis Modular System ''. OECD Nuclear Energy Agency, Issy - les - Moulineaux, France. Retrieved 25 May 2011. Jump up ^ `` EFF DES Cracker Source Code ''. Cosic.esat.kuleuven.be. Retrieved 8 July 2011. Jump up ^ `` Disarmament Diplomacy : -- DOE Supercomputing & Test Simulation Programme ''. Acronym.org.uk. 22 August 2000. Retrieved 8 July 2011. Jump up ^ `` China 's Investment in GPU Supercomputing Begins to Pay Off Big Time ! ''. Blogs.nvidia.com. Retrieved 8 July 2011. Jump up ^ Kaku, Michio. Physics of the Future ( New York : Doubleday, 2011 ), 65. Jump up ^ `` Faster Supercomputers Aiding Weather Forecasts ''. News.nationalgeographic.com. 28 October 2010. Retrieved 8 July 2011. Jump up ^ Washington Post 8 August 2011 Jump up ^ `` Supercomputers ''. U.S. Department of Energy. Retrieved 7 March 2017. Jump up ^ `` EU $1.2 supercomputer project to several 10 - 100 PetaFLOP computers by 2020 and exaFLOP by 2022 NextBigFuture.com ''. NextBigFuture.com. 2018 - 02 - 04. Retrieved 2018 - 05 - 21. Jump up ^ Erik, DeBenedictis. `` The Path To Extreme Computing '' ( PDF ). Sandia National Laboratories. Retrieved 1 December 2017. Jump up ^ Cohen, Reuven ( November 28, 2013 ). `` Global Bitcoin Computing Power Now 256 Times Faster Than Top 500 Supercomputers, Combined ! ''. Forbes. Retrieved 1 December 2017. Jump up ^ DeBenedictis, Erik P. ( 2005 ). `` Reversible logic for supercomputing ''. Proceedings of the 2nd conference on Computing frontiers. pp. 391 -- 402. ISBN 1 - 59593 - 019 - 1. Jump up ^ `` IDF : Intel says Moore 's Law holds until 2029 ''. Heise Online. 4 April 2008. Archived from the original on 8 December 2013. Jump up ^ Solem, J.C. ( 1985 ). `` MECA : A multiprocessor concept specialized to Monte Carlo ''. Proceedings of the Joint Los Alamos National Laboratory -- Commissariat à l'Energie Atomique Meeting Held at Cadarache Castle, Provence, France 22 -- 26 April 1985 ; Monte - Carlo Methods and Applications in Neutronics, Photonics and Statistical Physics, Alcouffe, R. ; Dautray, R. ; Forster, A. ; Forster, G. ; Mercier, B. ; eds. ( Springer Verlag, Berlin ). 240 : 184 -- 195. doi : 10.1007 / BFb0049047 # page - 1. Jump up ^ Yiannis Cotronis, Anthony Danalis, Dimitris Nikolopoulos & Jack Dongarra ( 2011 ). Recent Advances in the Message Passing Interface : 18th European MPI Users ' Group Meeting, EuroMPI 2011, Santorini, Greece, September 18 - 21, 2011. Proceedings. Springer Science & Business Media. p. 1. ISBN 9783642244483. CS1 maint : Uses authors parameter ( link ) Jump up ^ Springer Science & Business Media ( 2012 ). Energy - Efficient High Performance Computing : Measurement and Tuning. Springer Science & Business Media. p. 1. ISBN 9781447144922. CS1 maint : Uses authors parameter ( link ) Jump up ^ Springer Science & Business Media ( 2012 ). Energy - Efficient High Performance Computing : Measurement and Tuning. Springer Science & Business Media. p. 2. ISBN 9781447144922. CS1 maint : Uses authors parameter ( link ) Jump up ^ Springer Science & Business Media ( 2012 ). Energy - Efficient High Performance Computing : Measurement and Tuning. Springer Science & Business Media. p. 3. ISBN 9781447144922. CS1 maint : Uses authors parameter ( link ) Jump up ^ Yiannis Cotronis, Anthony Danalis, Dimitris Nikolopoulos & Jack Dongarra ( 2011 ). Recent Advances in the Message Passing Interface : 18th European MPI Users ' Group Meeting, EuroMPI 2011, Santorini, Greece, September 18 - 21, 2011. Proceedings. Springer Science & Business Media. p. 1. ISBN 9783642244483. CS1 maint : Uses authors parameter ( link ) Jump up ^ `` Green Supercomputer Crunches Big Data in Iceland ''. intelfreepress.com. 21 May 2015. Archived from the original on 20 May 2015. Retrieved 18 May 2015. Jump up ^ Yiannis Cotronis, Anthony Danalis, Dimitris Nikolopoulos & Jack Dongarra ( 2011 ). Recent Advances in the Message Passing Interface : 18th European MPI Users ' Group Meeting, EuroMPI 2011, Santorini, Greece, September 18 - 21, 2011. Proceedings. Springer Science & Business Media. p. 1. ISBN 9783642244483. CS1 maint : Uses authors parameter ( link ) Jump up ^ Yiannis Cotronis, Anthony Danalis, Dimitris Nikolopoulos & Jack Dongarra ( 2011 ). Recent Advances in the Message Passing Interface : 18th European MPI Users ' Group Meeting, EuroMPI 2011, Santorini, Greece, September 18 - 21, 2011. Proceedings. Springer Science & Business Media. p. 2. ISBN 9783642244483. CS1 maint : Uses authors parameter ( link ) External links A Tunable, Software - based DRAM Error Detection and Correction Library for HPC Detection and Correction of Silent Data Corruption for Large - Scale High - Performance Computing `` NetworkComputer -- Industry 's Fastest Enterprise Job Scheduler ''. Parallel computing General Distributed computing Parallel computing Massively parallel Cloud computing High - performance computing Multiprocessing Manycore processor GPGPU Computer network Systolic array Levels Bit Instruction Thread Task Data Memory Loop Pipeline Multithreading Temporal Simultaneous ( SMT ) Speculative ( SpMT ) Preemptive Cooperative Clustered Multi-Thread ( CMT ) Hardware scout Theory PRAM model Analysis of parallel algorithms Amdahl 's law Gustafson 's law Cost efficiency Karp -- Flatt metric Slowdown Speedup Elements Process Thread Fiber Instruction window Array data structure Coordination Multiprocessing Memory coherency Cache coherency Cache invalidation Barrier Synchronization Application checkpointing Programming Stream processing Dataflow programming Models Implicit parallelism Explicit parallelism Concurrency Non-blocking algorithm Hardware Flynn 's taxonomy SISD SIMD SIMT MISD MIMD Dataflow architecture Pipelined processor Superscalar processor Vector processor Multiprocessor symmetric asymmetric Memory shared distributed distributed shared UMA NUMA COMA Massively parallel computer Computer cluster Grid computer Hardware acceleration APIs Ateji PX Boost. Thread Chapel Charm++ Cilk Coarray Fortran CUDA Dryad C++ AMP Global Arrays MPI OpenMP OpenCL OpenHMPP OpenACC TPL PLINQ PVM POSIX Threads RaftLib UPC TBB ZPL Problems Deadlock Livelock Deterministic algorithm Embarrassingly parallel Parallel slowdown Race condition Software lockout Scalability Starvation Category : parallel computing Media related to Parallel computing at Wikimedia Commons Computer sizes Classes of computers Microcomputer, personal computer Stationary Workstation Desktop Home Personal supercomputer Portable Tabletop Smart TV Interactive kiosk Game arcade cabinet System board Home console Microconsole SFF Nettop Plug Smart speaker Mobile Laptop Desktop replacement 2 - in - 1 Subnotebook Netbook Smartbook Ultrabook Ultra-mobile PC Tablet Ultra-mobile PC 2 - in - 1 Phablet Tabletop Handheld Handheld PC Palmtop PC Pocket computer PDA Electronic organizer Mobile phone Feature phone Smartphone Phablet PMP E-reader Handheld game console Portable / Mobile data terminal Calculator Scientific Programmable Graphing Wearable Digital wristwatch Calculator watch Smartwatch Smartglasses Smart ring Midrange Server Minicomputer Supermini Large Super Mainframe Minisuper Others Microcontroller Nanocomputer Single - board computer Smartdust Wireless sensor network Major fields of computer science Note : This template roughly follows the 2012 ACM Computing Classification System. Hardware Printed circuit board Peripheral Integrated circuit Very - large - scale integration Systems on Chip ( SoCs ) Energy consumption ( Green computing ) Electronic design automation Hardware acceleration Computer systems organization Computer architecture Embedded system Real - time computing Dependability Networks Network architecture Network protocol Network components Network scheduler Network performance evaluation Network service Software organization Interpreter Middleware Virtual machine Operating system Software quality Software notations and tools Programming paradigm Programming language Compiler Domain - specific language Modeling language Software framework Integrated development environment Software configuration management Software library Software repository Software development Software development process Requirements analysis Software design Software construction Software deployment Software maintenance Programming team Open - source model Theory of computation Model of computation Formal language Automata theory Computational complexity theory Logic Semantics Algorithms Algorithm design Analysis of algorithms Algorithmic efficiency Randomized algorithm Computational geometry Mathematics of computing Discrete mathematics Probability Statistics Mathematical software Information theory Mathematical analysis Numerical analysis Information systems Database management system Information storage systems Enterprise information system Social information systems Geographic information system Decision support system Process control system Multimedia information system Data mining Digital library Computing platform Digital marketing World Wide Web Information retrieval Security Cryptography Formal methods Security services Intrusion detection system Hardware security Network security Information security Application security Human -- computer interaction Interaction design Social computing Ubiquitous computing Visualization Accessibility Concurrency Concurrent computing Parallel computing Distributed computing Multithreading Multiprocessing Artificial intelligence Natural language processing Knowledge representation and reasoning Computer vision Automated planning and scheduling Search methodology Control method Philosophy of artificial intelligence Distributed artificial intelligence Machine learning Supervised learning Unsupervised learning Reinforcement learning Multi-task learning Cross-validation Graphics Animation Rendering Image manipulation Graphics processing unit Mixed reality Virtual reality Image compression Solid modeling Applied computing E-commerce Enterprise software Computational mathematics Computational physics Computational chemistry Computational biology Computational social science Computational engineering Computational healthcare Digital art Electronic publishing Cyberwarfare Electronic voting Video games Word processing Operations research Educational technology Document management Book Portal WikiProject Commons Information technology portal Computer science portal NDL : 01090879 Retrieved from `` https://en.wikipedia.org/w/index.php?title=Supercomputer&oldid=866324461 '' Categories : Supercomputers American inventions Cluster computing Concurrent computing Distributed computing architecture Parallel computing Hidden categories : CS1 maint : Uses authors parameter CS1 maint : Multiple names : authors list Webarchive template wayback links Pages containing links to subscription - only content All articles with dead external links Articles with dead external links from June 2012 Use dmy dates from January 2015 Articles containing potentially dated statements from 2015 All articles containing potentially dated statements Articles containing potentially dated statements from October 2016 Articles containing potentially dated statements from February 2017 All articles with unsourced statements Articles with unsourced statements from October 2016 Wikipedia articles with NDL identifiers Talk Contents About Wikipedia Alemannisch Azərbaycanca বাংলা Беларуская Български Català Čeština Dansk Deutsch Eesti Ελληνικά Español Euskara فارسی Français Galego 한국어 Հայերեն हिन्दी Hrvatski Bahasa Indonesia Íslenska Italiano עברית ქართული Қазақша Kiswahili Кыргызча ລາວ Latviešu Lietuvių Lumbaart Malagasy മലയാളം मराठी მარგალური Bahasa Melayu Mirandés Монгол မြန်မာဘာသာ Nederlands नेपाली 日本 語 Norsk Occitan ਪੰਜਾਬੀ Polski Português Română Русский Scots Shqip Sicilianu සිංහල Simple English Slovenčina Slovenščina Српски / srpski Srpskohrvatski / српскохрватски Suomi Svenska தமிழ் తెలుగు ไทย Türkçe Українська اردو Tiếng Việt 粵語 中文 Lingua Franca Nova 63 more Edit links This page was last edited on 29 October 2018, at 17 : 44 ( UTC ). Text is available under the Creative Commons Attribution - ShareAlike License ; additional terms may apply. By using this site, you agree to the Terms of Use and Privacy Policy. Wikipedia ® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization. About Wikipedia

Question: how many cores does the fastest super computer have
Answer: 4,611,236