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The IBM Blue Gene/P supercomputer "Intrepid" at Argonne National Laboratory runs 164,000 processor cores using normal data center air conditioning, grouped in 40 racks/cabinets connected by a high-speed 3-D torus network. IBM Blue Gene P supercomputer.jpg
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 as compared to a general-purpose computer. The performance of a supercomputer is commonly measured in floating-point operations per second (FLOPS) instead of million instructions per second (MIPS). Since 2017, there are supercomputers which can perform over a hundred quadrillion FLOPS (petaFLOPS). [3] Since November 2017, all of the world's fastest 500 supercomputers run Linux-based operating systems. [4] Additional research is being conducted in China, the United States, the European Union, Taiwan and Japan to build even faster, more powerful and technologically superior exascale supercomputers. [5]

A computer is a machine that can be instructed to carry out sequences of arithmetic or logical operations automatically via computer programming. Modern computers have the ability to follow generalized sets of operations, called programs. These programs enable computers to perform an extremely wide range of tasks. A "complete" computer including the hardware, the operating system, and peripheral equipment required and used for "full" operation can be referred to as a computer system. This term may as well be used for a group of computers that are connected and work together, in particular a computer network or computer cluster.

In computing, floating point operations per second is a measure of computer performance, useful in fields of scientific computations that require floating-point calculations. For such cases it is a more accurate measure than measuring instructions per second.

TOP500 Ranking of the 500 most powerful supercomputers

The TOP500 project ranks and details the 500 most powerful non-distributed computer systems in the world. The project was started in 1993 and publishes an updated list of the supercomputers twice a year. The first of these updates always coincides with the International Supercomputing Conference in June, and the second is presented at the ACM/IEEE Supercomputing Conference in November. The project aims to provide a reliable basis for tracking and detecting trends in high-performance computing and bases rankings on HPL, a portable implementation of the high-performance LINPACK benchmark written in Fortran for distributed-memory computers.


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. [6]

Computational science is a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems. It is an area of science which spans many disciplines, but at its core it involves the development of models and simulations to understand natural systems.

Quantum mechanics branch of physics that acts as an abstract framework formulating all the laws of nature

Quantum mechanics,, including quantum field theory, is a fundamental theory in physics which describes nature at the smallest – including atomic and subatomic – scales.

Weather forecasting application of science, technology to predict the conditions of the atmosphere for a given location and time

Weather forecasting is the application of science and technology to predict the conditions of the atmosphere for a given location and time. People have attempted to predict the weather informally for millennia and formally since the 19th century. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere at a given place and using meteorology to project how the atmosphere will change.

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, vector processors operating on large arrays of data came to dominate. A notable example is the highly successful Cray-1 of 1976. Vector computers remained the dominant design into the 1990s. From then until today, massively parallel supercomputers with tens of thousands of off-the-shelf processors became the norm. [7] [8]

Seymour Cray Applied mathematician, computer scientist, and electrical engineer

Seymour Roger Cray was an American electrical engineer and supercomputer architect who designed a series of computers that were the fastest in the world for decades, and founded Cray Research which built many of these machines. Called "the father of supercomputing", Cray has been credited with creating the supercomputer industry. Joel S. Birnbaum, then chief technology officer of Hewlett-Packard, said of him: "It seems impossible to exaggerate the effect he had on the industry; many of the things that high performance computers now do routinely were at the farthest edge of credibility when Seymour envisioned them." Larry Smarr, then director of the National Center for Supercomputing Applications at the University of Illinois said that Cray is "the Thomas Edison of the supercomputing industry."

Control Data Corporation defunct supercomputer firm

Control Data Corporation (CDC) was a mainframe and supercomputer firm. CDC was one of the nine major United States computer companies through most of the 1960s; the others were IBM, Burroughs Corporation, DEC, NCR, General Electric, Honeywell, RCA, and UNIVAC. CDC was well-known and highly regarded throughout the industry at the time. For most of the 1960s, Seymour Cray worked at CDC and developed a series of machines that were the fastest computers in the world by far, until Cray left the company to found Cray Research (CRI) in the 1970s. After several years of losses in the early 1980s, in 1988 CDC started to leave the computer manufacturing business and sell the related parts of the company, a process that was completed in 1992 with the creation of Control Data Systems, Inc. The remaining businesses of CDC currently operate as Ceridian.

Parallel computing programming paradigm in which many calculations or the execution of processes are carried out simultaneously

Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but it's gaining broader interest due to the physical constraints preventing frequency scaling. As power consumption by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.

The US has long been the leader in the supercomputer field, first through Cray's almost uninterrupted dominance of the field, and later through a variety of technology companies. Japan made major strides in the field in the 1980s and 90s, but since then China has become increasingly active in the field. As of November 2018, the fastest supercomputer on the TOP500 supercomputer list is the Summit, in the United States, with a LINPACK benchmark score of 143.5 PFLOPS, followed by, Sierra, by around 48.860 PFLOPS. [9] The US has five of the top 10 and China has two. [9] In June 2018, all supercomputers on the list combined broke the 1 exaFLOPS mark. [10]

Summit (supercomputer) Supercomputer developed by IBM

Summit or OLCF-4 is a supercomputer developed by IBM for use at Oak Ridge National Laboratory, which as of November 2018 was the fastest supercomputer in the world, capable of 200 petaFLOPS. Its current LINPACK benchmark is clocked at 148.6 petaFLOPS. As of November 2018, the supercomputer is also the 3rd most energy efficient in the world with a measured power efficiency of 14.668 gigaFLOPS/watt. Summit is the first supercomputer to reach exaop speed, achieving 1.88 exaops during a genomic analysis and is expected to reach 3.3 exaops using mixed precision calculations.

Sierra (supercomputer) supercomputer

Sierra or ATS-2 is a supercomputer built for the Lawrence Livermore National Laboratory for use by the National Nuclear Security Administration as the second Advanced Technology System. It is primarily used for predictive applications in stockpile stewardship, helping to assure the safety, reliability and effectiveness of the United States' nuclear weapons.

Exa is a decimal unit prefix in the metric system denoting 1018 or 1000000000000000000. It was added as an SI prefix to the International System of Units (SI) in 1975, and has the unit symbol E.


A circuit board from the IBM 7030 IBM 7030 Stretch circuit board.jpg
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. CDC 6600.jc.jpg
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 Cray-1-deutsches-museum.jpg
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. [11] 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. [12]

Drum memory Magnetic data storage device

Drum memory was a magnetic data storage device invented by Gustav Tauschek in 1932 in Austria. Drums were widely used in the 1950s and into the 1960s as computer memory.

IBM 7030 Stretch first IBM supercomputer using dedicated transistors

The IBM 7030, also known as Stretch, was IBM's first transistorized supercomputer. It was the fastest computer in the world from 1961 until the first CDC 6600 became operational in 1964.

Los Alamos National Laboratory research laboratory for the design of nuclear weapons

Los Alamos National Laboratory is a United States Department of Energy national laboratory initially organized during World War II for the design of nuclear weapons as part of the Manhattan Project. It is located a short distance northwest of Santa Fe, New Mexico in the southwestern US.

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. [13] 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. [14]

Atlas (computer) Supercomputer of the 1960s

The Atlas Computer was one of the world's first supercomputers, in use from 1962 until 1971. It was considered to be the most powerful computer in the world at that time. It is notable for being the first machine with virtual memory using paging techniques; this approach quickly spread, and is now ubiquitous.

Victoria University of Manchester British university (1851-2004)

The former Victoria University of Manchester, now the University of Manchester, was founded in 1851 as Owens College. In 1880, the college joined the federal Victoria University, gaining an independent university charter in 1904 as the Victoria University of Manchester after the collapse of the federal university.

Tom Kilburn British electrical engineer

Tom Kilburn was an English mathematician and computer scientist. Over the course of a productive 30-year career, he was involved in the development of five computers of great historical significance. With Freddie Williams he worked on the Williams–Kilburn tube and the world's first electronic stored-program computer, the Manchester Baby, while working at the University of Manchester. His work propelled Manchester and Britain into the forefront of the emerging field of computer science.

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. [15] 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. [16] [17] [18] [19]

Cray left CDC in 1972 to form his own company, Cray Research. [17] Four years after leaving CDC, Cray delivered the 80 MHz Cray-1 in 1976, which became one of the most successful supercomputers in history. [20] [21] 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. [22]

Massively parallel designs

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

The only computer to seriously challenge the Cray-1's performance in the 1970s was the ILLIAC IV. This machine was the first realized example of a true massively parallel computer, in which many processors worked together to solve different parts of a single larger problem. In contrast with the vector systems, which were designed to run a single stream of data as quickly as possible, in this concept, the computer instead feeds separate parts of the data to entirely different processors and then recombines the results. The ILLIAC's design was finalized in 1966 with 256 processors and offer speed up to 1 GFLOPS, compared to the 1970s Cray-1's peak of 250 MFLOPS. However, development problems led to only 64 processors being built, and the system could never operate 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?" [23] 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. [24]

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. [25] 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. [26] [27] The Hitachi SR2201 obtained a peak performance of 600 GFLOPS in 1996 by using 2048 processors connected via a fast three-dimensional crossbar network. [28] [29] [30] 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. [31]

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. [7] [8]

The CPU share of TOP500 Processor families in TOP500 supercomputers.svg
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. [32] 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. [33] [34] The use of multi-core processors combined with centralization is an emerging direction, e.g. as in the Cyclops64 system. [35] [36]

As the price, performance and energy efficiency of general purpose graphic processors (GPGPUs) have improved, [37] a number of petaFLOPS supercomputers such as Tianhe-I and Nebulae have started to rely on them. [38] 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. [39] [40] However, GPUs are gaining ground and in 2012 the Jaguar supercomputer was transformed into Titan by retrofitting CPUs with GPUs. [41] [42] [43]

High-performance computers have an expected life cycle of about three years before requiring an upgrade. [44]

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, [45] Deep Blue, [46] and Hydra, [47] for playing chess, Gravity Pipe for astrophysics, [48] MDGRAPE-3 for protein structure computation molecular dynamics [49] and Deep Crack, [50] for breaking the DES cipher.

Energy usage and heat management

The Summit supercomputer is as of November 2018 the fastest supercomputer in the world. With a measured power efficiency of 14.668 GFlops/watt it is also the 3rd most energy efficient in the world. Summit (supercomputer).jpg
The Summit supercomputer is as of November 2018 the fastest supercomputer in the world. With a measured power efficiency of 14.668 GFlops/watt it is also the 3rd most energy efficient in the world.

Throughout the decades, the management of heat density has remained a key issue for most centralized supercomputers. [53] [54] [55] The large amount of heat generated by a system may also have other effects, e.g. reducing the lifetime of other system components. [56] 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. [57] [58] 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. [59] 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 IBM HS20 blade server.jpg
An IBM HS20 blade

Heat management is a major issue in complex electronic devices and affects powerful computer systems in various ways. [60] 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. [61] [62] [63]

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. [57] 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. [58]

In the Blue Gene system, IBM deliberately used low power processors to deal with heat density. [64] The IBM Power 775, released in 2011, has closely packed elements that require water cooling. [65] The IBM Aquasar system uses hot water cooling to achieve energy efficiency, the water being used to heat buildings as well. [66] [67]

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. [68] [69] In November 2010, the Blue Gene/Q reached 1,684 MFLOPS/W. [70] [71] 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. [72]

Because copper wires can transfer energy into a supercomputer with much higher power densities than forced air or circulating refrigerants can remove waste heat, [73] the ability of the cooling systems to remove waste heat is a limiting factor. [74] [75] 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. [76]

Software and system management

Operating systems

Since the end of the 20th century, supercomputer operating systems have undergone major transformations, based on the changes in supercomputer architecture. [77] 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. [78]

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. [79] [80] [81]

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. [82]

Although most modern supercomputers use a Linux-based operating system, each manufacturer has its own specific Linux-derivative, and no industry standard exists, partly due to the fact that the differences in hardware architectures require changes to optimize the operating system to each hardware design. [77] [83]

Software tools and message passing

Wide-angle view of the ALMA correlator Wide-angle view of the ALMA correlator.jpg
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

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

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

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. [85]

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. [86]

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. [87] The Internet PrimeNet Server supports GIMPS's grid computing approach, one of the earliest and most successful[ citation needed ] grid computing projects, since 1997.

Quasi-opportunistic approaches

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. [88] 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. [88]

HPC clouds

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. [89] [90] [91] [92]

In 2016 Penguin Computing, R-HPC, Amazon Web Services, Univa, Silicon Graphics International, Sabalcore, and Gomput started to offer HPC cloud computing. The Penguin On Demand (POD) cloud is a bare-metal compute model to execute code, but each user is given virtualized login node. POD computing nodes are connected via nonvirtualized 10 Gbit/s Ethernet or QDR InfiniBand networks. User connectivity to the POD data center ranges from 50 Mbit/s to 1 Gbit/s. [93] Citing Amazon's EC2 Elastic Compute Cloud, Penguin Computing argues that virtualization of compute nodes is not suitable for HPC. Penguin Computing has also criticized that HPC clouds may allocated computing nodes to customers that are far apart, causing latency that impairs performance for some HPC applications. [94]

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. [95]

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. [95] 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. [95]

Performance metrics

Top supercomputer speeds: logscale speed over 60 years Supercomputing-rmax-graph2.svg
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. [96] These measurements 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 (EFLOPS) range. An EFLOPS is one quintillion (1018) 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. [97] 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. [98] The LINPACK benchmark typically performs LU decomposition of a large matrix. [99] 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. [97]

The TOP500 list

Top 20 Supercomputers in the World in June 2014 Top20supercomputers.png
Top 20 Supercomputers in the World in June 2014
Distribution of TOP500 supercomputers among different countries, in November 2015 Supercomputer Share Top500 November2015.png
Distribution of TOP500 supercomputers among different countries, in 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, [100] and the "Peak speed" is given as the "Rmax" rating.

YearSupercomputer Peak speed
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
2010 Tianhe-IA2.566 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

Source: TOP500

In 2018, Lenovo became the worlds largest provider (117) for the top500 supercomputers. [101]

Country/VendorSystem countSystem share (%)Rmax (GFLOPS)Rpeak (GFLOPS)Processor cores
Flag of the United States.svg Cray Inc. 5711.4160,476,360229,400,1605,981,864
Flag of the United States.svg HP 14328.6124,430,645181,738,3734,996,780
Flag of the United States.svg IBM 275.456,428,00267,161,6394,611,236
Flag of the People's Republic of China.svg NUDT 40.839,271,79064,020,6853,534,336
Flag of Japan.svg Fujitsu 112.237,624,37851,859,9861,753,368
Flag of the United States.svg Dell 153.024,528,72742,623,6321,247,118
Flag of France.svg Bull 183.624,362,68331,212,663978,924
Flag of the United States.svg SGI 234.614,741,77317,963,102813,376
Flag of Russia.svg T-Platforms 30.64,428,6206,355,903170,824
Flag of the United States.svg Atipa Technologies 30.63,044,9764,163,712214,584
Flag of Japan.svg Flag of the United States.svg NEC/HP 10.22,785,0005,735,68576,032
Flag of the People's Republic of China.svg Dawning 20.41,451,6003,217,772151,360
Flag of Japan.svg Hitachi/Fujitsu 10.21,018,0001,502,236222,072
Flag of the People's Republic of China.svg NRCPCET 10.2795,9001,070,160137,200
Flag of the Netherlands.svg ClusterVision 20.4784,735881,25442,368
Flag of the United States.svg Intel 10.2758,873933,48151,392
Flag of the United States.svg Amazon 20.4724,269947,61043,520
Flag of the United States.svg Oracle 20.4708,300804,83568,672
Flag of Russia.svg RSC Group 10.2658,112829,33819,936
Flag of Germany.svg MEGWARE 30.6610,521710,59254,800
Flag of the United States.svg Supermicro 10.2602,983677,37620,160
Flag of Japan.svg NEC 30.6578,987709,52021,296
Flag of the United States.svg Adtech10.2532,6001,098,00038,400
Flag of Japan.svg Hitachi 20.4496,900622,59820,544
Flag of the People's Republic of China.svg Flag of the United States.svg Flag of the Republic of China.svg IPE, Nvidia, Tyan 10.2496,5001,012,65029,440
Flag of Brazil.svg Itautec 20.4411,800920,83027,776
Flag of India.svg Netweb Technologies10.2388,442520,35830,056
Flag of Australia (converted).svg Xenon Systems 10.2335,300472,4986,875
Flag of the United States.svg Flag of the Republic of China.svg Flag of Germany.svg AMD, ASUS, FIAS, GSI 10.2316,700593,60010,976
Flag of the Netherlands.svg Flag of the United States.svg Clustervision/Supermicro 10.2299,300588,74944,928
Flag of Canada (Pantone).svg Flag of the United States.svg Niagara Computers, Supermicro 10.2289,500348,6605,310
Flag of the People's Republic of China.svg Inspur 10.2196,234262,5608,412
Flag of the United States.svg Flag of India.svg HP/WIPRO 10.2188,700394,76012,532
Flag of Japan.svg Flag of Canada (Pantone).svg PEZY Computing/Exascaler Inc. 10.2178,107395,264262,784
Flag of the Republic of China.svg Acer Group 10.2177,100231,85926,244


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

DecadeUses and computer involved
1970sWeather forecasting, aerodynamic research (Cray-1). [102]
1980sProbabilistic analysis, [103] radiation shielding modeling [104] (CDC Cyber).
1990sBrute force code breaking (EFF DES cracker). [105]
2000s3D nuclear test simulations as a substitute for legal conduct Nuclear Non-Proliferation Treaty (ASCI Q). [106]
2010sMolecular Dynamics Simulation (Tianhe-1A) [107]

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. [108]

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. [109]

In 2011, the challenges and difficulties in pushing the envelope in supercomputing were underscored by IBM's abandonment of the Blue Waters petascale project. [110]

The Advanced Simulation and Computing Program currently uses supercomputers to maintain and simulate the United States nuclear stockpile. [111]

Diagram of a three-dimensional torus interconnect used by systems such as Blue Gene, Cray XT3, etc. 2x2x2torus.svg
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 (1018 or one quintillion FLOPS) supercomputer. [112] Erik P. DeBenedictis of Sandia National Laboratories has theorized that a zettaFLOPS (1021 or one sextillion FLOPS) computer is required to accomplish full weather modeling, which could cover a two-week time span accurately. [113] [114] [115] Such systems might be built around 2030. [116]

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. [117]

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. [118] A 2010 study commissioned by DARPA identified power consumption as the most pervasive challenge in achieving Exascale computing. [119] 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. [120] 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. [121]

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. [118] 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. [122]

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. [118] 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. [118]

In fiction

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, Vulcan's Hammer, Colossus and Deep Thought.

See also

Notes and references

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Jaguar or OLCF-2 was a petascale supercomputer built by Cray at Oak Ridge National Laboratory (ORNL) in Oak Ridge, Tennessee. The massively parallel Jaguar had a peak performance of just over 1,750 teraFLOPS. It had 224,256 x86-based AMD Opteron processor cores, and operated with a version of Linux called the Cray Linux Environment. Jaguar was a Cray XT5 system, a development from the Cray XT4 supercomputer.

Tianhe-I, Tianhe-1, or TH-1 is a supercomputer capable of an Rmax of 2.5 petaFLOPS. Located at the National Supercomputing Center of Tianjin, China, it was the fastest computer in the world from October 2010 to June 2011 and is one of the few petascale supercomputers in the world.

India's Supercomputer Programme was started in late 1980s, precisely during the 3rd quarter of 1987, in New Delhi for Software, in Bangalore for Hardware, and in Pune for Firmware, while Sam Pitroda, Advisor to C-DOT, and C-DOT's Indigenous Architecture and Design Team constituted by its Senior Member Technical Staff / Senior Programme Managers including Mohan Subramaniyam alias Mohan Rose Ali, Periasamy Muthiah, and Leslie D'Souza had all worked hard at the Centre for Development of Telematics (C-DOT), after successfully completing their 3 years mission on designing the Nation's first ever indigenous C-DOT Digital Switching System - DSS, to create C-DOT's Indigenous Super-computing Machine called CHIPPS - C-DOT High-Performance Parallel Processing System, because the contracted Cray X-MP Supercomputers were denied for export to India which was under the Statesmanship and Stewardship of Mr. Rajiv Gandhi, the then Prime Minister of India, due to an arms embargo imposed by US on India during Ronald Reagan's Presidential Administration, for it was a dual-use technology and it could be used for developing indigenous Strategic Defense Systems by India.

Supercomputing in Japan

Japan operates a number of centers for supercomputing which hold world records in speed, with the K computer becoming the world's fastest in June 2011.

History of supercomputing aspect of history

The history of supercomputing goes back to the early 1920s in the United States with the IBM tabulators at Columbia University and a series of computers at Control Data Corporation (CDC), designed by Seymour Cray to use innovative designs and parallelism to achieve superior computational peak performance. The CDC 6600, released in 1964, is generally considered the first supercomputer. However, some earlier computers were considered supercomputers for their day, such as the 1954 IBM NORC, the 1960 UNIVAC LARC, and the IBM 7030 Stretch and the Atlas, both in 1962.

Supercomputing in Europe

Several centers for supercomputing exist across Europe, and distributed access to them is coordinated by European initiatives to facilitate high-performance computing. One such initiative, the HPC Europa project, fits within the Distributed European Infrastructure for Supercomputing Applications (DEISA), which was formed in 2002 as a consortium of eleven supercomputing centers from seven European countries. Operating within the CORDIS framework, HPC Europa aims to provide access to supercomputers across Europe.

The LINPACK Benchmarks are a measure of a system's floating point computing power. Introduced by Jack Dongarra, they measure how fast a computer solves a dense n by n system of linear equations Ax = b, which is a common task in engineering.

Supercomputer architecture

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. However, in time the demand for increased computational power ushered in the age of massively parallel systems.

Titan (supercomputer) American supercomputer

Titan or OLCF-3 was a supercomputer built by Cray at Oak Ridge National Laboratory for use in a variety of science projects. Titan was an upgrade of Jaguar, a previous supercomputer at Oak Ridge, that uses graphics processing units (GPUs) in addition to conventional central processing units (CPUs). Titan was the first such hybrid to perform over 10 petaFLOPS. The upgrade began in October 2011, commenced stability testing in October 2012 and it became available to researchers in early 2013. The initial cost of the upgrade was US$60 million, funded primarily by the United States Department of Energy.

Supercomputing in Pakistan

The high performance supercomputing program started in mid-to-late 1980s in Pakistan. Supercomputing is a recent area of Computer science in which Pakistan has made progress, driven in part by the growth of the information technology age in the country. Developing on the ingenious supercomputer program started in 1980s when the deployment of the Cray supercomputers was initially denied.

Cray XK7 supercomputer

XK7 is a supercomputing platform, produced by Cray, launched on October 29, 2012. XK7 is the second platform from Cray to use a combination of central processing units ("CPUs") and graphical processing units ("GPUs") for computing; the hybrid architecture requires a different approach to programming to that of CPU-only supercomputers. Laboratories that host XK7 machines host workshops to train researchers in the new programming languages needed for XK7 machines. The platform is used in Titan, the world's second fastest supercomputer in the November 2013 list as ranked by the TOP500 organization. Other customers include the Swiss National Supercomputing Centre which has a 272 node machine and Blue Waters has a machine that has Cray XE6 and XK7 nodes that performs at approximately 1 petaFLOPS (1015 floating-point operations per second).

The Cray XC30 is a massively parallel multiprocessor supercomputer manufactured by Cray. It consists of Intel Xeon processors, with optional Nvidia Tesla or Xeon Phi accelerators, connected together by Cray's proprietary "Aries" interconnect, stored in air-cooled or liquid-cooled cabinets. Each liquid-cooled cabinet can contain up to 48 blades, each with eight CPU sockets, and uses 90 kW of power. The XC series supercomputers are available with the Cray DataWarp applications I/O accelerator technology.

Cray XC40 Supercomputer manufactured by Cray

The Cray XC40 is a massively parallel multiprocessor supercomputer manufactured by Cray. It consists of Intel Haswell Xeon processors, with optional Nvidia Tesla or Intel Xeon Phi accelerators, connected together by Cray's proprietary "Aries" interconnect, stored in air-cooled or liquid-cooled cabinets. The XC series supercomputers are available with the Cray DataWarp applications I/O accelerator technology.