A mainframe computer that is among the largest, fastest, or most powerful of those available at a given time.
supercomputer
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su·per·com·put·er (sū'pər-kəm-pyū'tər)  |
| Sci-Tech Encyclopedia: Supercomputer |
A computer which, among existing general-purpose computers at any given time, is superlative, often in several senses: highest computation rate, largest memory, or highest cost. Predominantly, the term refers to the fastest “number crunchers,” that is, machines designed to perform numerical calculations at the highest speed that the latest electronic device technology and the state of the art of computer architecture allow.
The demand for the ability to execute arithmetic operations at the highest possible rate originated in computer applications areas collectively referred to as scientific computing. Large-scale numerical simulations of physical processes are often needed in fields such as physics, structural mechanics, meteorology, and aerodynamics. A common technique is to compute an approximate numerical solution to a set of partial differential equations which mathematically describe the physical process of interest but are too complex to be solved by formal mathematical methods. This solution is obtained by first superimposing a grid on a region of space, with a set of numerical values attached to each grid point. Large-scale scientific computations of this type often require hundreds of thousands of grid points with 10 or more values attached to each point, with 10 to 500 arithmetic operations necessary to compute each updated value, and hundreds of thousands of time steps over which the computation must be repeated before a steady-state solution is reached. See also Computational fluid dynamics;
Two lines of technological advancement have significantly contributed to what roughly amounts to a doubling of the fastest computers' speeds every year since the early 1950s—the steady improvement in electronic device technology and the accumulation of improvements in the architectural designs of digital computers.
Computers incorporate very large-scale integrated (VLSI) circuits with tens of millions of transistors per chip for both logic and memory components. A variety of types of integrated circuitry is used in contemporary supercomputers. Several use high-speed complementary metallic oxide semiconductor (CMOS) technology. Throughout most of the history of digital computing, supercomputers generally used the highest-performance switching circuitry available at the time—which was usually the most exotic and expensive. However, many supercomputers now use the conventional, inexpensive device technology of commodity microprocessors and rely on massive parallelism for their speed. See also Computer storage technology; Concurrent processing; Integrated circuits; Logic circuits; Semiconductor memories.
Increases in computing speed which are purely due to the architectural structure of a computer can largely be attributed to the introduction of some form of parallelism into the machine's design: two or more operations which were performed one after the other in previous computers can now be performed simultaneously. See also Computer systems architecture.
Pipelining is a technique which allows several operations to be in progress in the central processing unit at once. The first form of pipelining used was instruction pipelining. Since each instruction must have the same basic sequence of steps performed, namely instruction fetch, instruction decode, operand fetch, and execution, it is feasible to construct an instruction pipeline, where each of these steps happens at a separate stage of the pipeline. The efficiency of the instruction pipeline depends on the likelihood that the program being executed allows a steady stream of instructions to be fetched from contiguous locations in memory.
The central processing unit nearly always has a much faster cycle time than the memory. This implies that the central processing unit is capable of processing data items faster than a memory unit can provide them. Interleaved memory is an organization of memory units which at least partially relieves this problem.
Parallelism within arithmetic and logical circuitry has been introduced in several ways. Adders, multipliers, and dividers now operate in bit-parallel mode, while the earliest machines performed bit-serial arithmetic. Independently operating parallel functional units within the central processing unit can each perform an arithmetic operation such as add, multiply, or shift. Array processing is a form of parallelism in which the instruction execution portion of a central processing unit is replicated several times and connected to its own memory device as well as to a common instruction interpretation and control unit. In this way, a single instruction can be executed at the same time on each of several execution units, each on a different set of operands. This kind of architecture is often referred to as single-instruction stream, multiple-data stream (SIMD).
Vector processing is the term applied to a form of pipelined arithmetic units which are specialized for performing arithmetic operations on vectors, which are uniform, linear arrays of data values. It can be thought of as a type of SIMD processing, since a single instruction invokes the execution of the same operation on every element of the array. See also
A central processing unit can contain multiple sets of the instruction execution hardware for either scalar or vector instructions. The task of scheduling instructions which can correctly execute in parallel with one another is generally the responsibility of the compiler or special scheduling hardware in the central processing unit. Instruction-level parallelism is almost never visible to the application programmer.
Multiprocessing is a form of parallelism that has complete central processing units operating in parallel, each fetching and executing instructions independently from the others. This type of computer organization is called multiple-instruction stream, multiple-data stream (MIMD). See also Multiprocessing.
| Computer Desktop Encyclopedia: supercomputer |
The fastest computer available. Supercomputers are typically used for simulations in petroleum exploration and production, structural analysis, computational fluid dynamics, physics and chemistry, electronic design, nuclear energy research and meteorology. Years ago, it took a supercomputer to perform real-time animated graphics, but that changed as high-performance desktop computers became capable of doing the job. See supercomputer sites and grid computing.
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| Accounting Dictionary: Supercomputer |
Data processing machine designed to be significantly larger and/or faster than the typical mainframe computer. It can process both scalar and vector quantities. It handles many thousands of operations simultaneously. Supercomputers can perform billions of additions per second. An example is the Cyberplus parallel processor.
| Britannica Concise Encyclopedia: supercomputer |
For more information on supercomputer, visit Britannica.com.
| Columbia Encyclopedia: supercomputer |
supercomputer, a state-of-the-art, extremely powerful computer capable of manipulating massive amounts of data in a relatively short time. Supercomputers are very expensive and are employed for specialized scientific and engineering applications that must handle very large databases or do a great amount of computation, among them meteorology, animated graphics, fluid dynamic calculations, nuclear energy research and weapon simulation, and petroleum exploration. There are two approaches to the design of supercomputers. One, called massively parallel processing (MPP), is to chain together thousands of commercially available microprocessors utilizing parallel processing techniques. A variant of this, called a Beowulf cluster, or cluster computing, employs large numbers of personal computers interconnected by a local area network and running programs written for parallel processing. The other approach, called vector processing, is to develop specialized hardware to solve complex calculations. This technique was employed in the Earth Simulator, a Japanese supercomputer introduced in 2002 that utilizes 640 nodes composed of 5104 specialized processors to execute 35.6 trillion mathematical operations per second; it is used to analyze earthquake and weather patterns and climate change, including global warming. Currently the fastest supercomputer is the Blue Gene/L, completed at Lawrence Livermore National Laboratory in 2005 and upgraded in 2007; it utilizes 212,992 processors to execute potentially as many 596 trillion mathematical operations per second. The computer is used to do nuclear weapons safety and reliability analyses. A prototype of Blue Gene/L demonstrated in 2003 was air-cooled, as opposed to many high-performance machines that use water and refrigeration, and used no more power than the average home. In 2003 scientists at Virginia Tech assembled a relatively low-cost supercomputer using 1,100 dual-processor Apple Macintoshes; it was ranked at the time as the third fastest machine in the world.
| Intelligence Encyclopedia: Supercomputers |
A supercomputer is a powerful computer that possesses the capacity to store and process far more information than is possible using a conventional personal computer.
An illustrative comparison can be made between the hard drive capacity of a personal computer and a super-computer. Hard drive capacity is measured in terms of gigabytes. A gigabyte is one billion bytes. A byte is a unit of data that is eight binary digits (i.e., 0's and 1's) long; this is enough data to represent a number, letter, or a typographic symbol. Premium personal computers have a hard drive that is capable of storing on the order of 30 gigabytes of information. In contrast, a supercomputer has a capacity of 200 to 300 gigabytes or more.
Another useful comparison between supercomputers and personal computers is in the number of processors in each machine. A processor is the circuitry responsible for handling the instructions that drive a computer. Personal computers have a single processor. The largest supercomputers have thousands of processors.
This enormous computation power makes supercomputers capable of handling large amounts of data and processing information extremely quickly. For example, in April 2002, a Japanese supercomputer that contains 5,104 processors established a calculation speed record of 35,600 gigaflops (a gigaflop is one billion mathematical calculations per second). This exceeded the old record that was held by the ASCI White-Pacific supercomputer located at the Lawrence Livermore National Laboratory in Berkeley, California. The Livermore supercomputer, which is equipped with over 7,000 processors, achieves 7,226 gigaflops.
These speeds are a far cry from the first successful supercomputer, the Sage System CDC 6600, which was designed by Seymour Cray (founder of the Cray Corporation) in 1964. His computer had a speed of 9 megaflops, thousands of times slower than the present day versions. Still, at that time, the CDC 6600 was an impressive advance in computer technology.
Beginning around 1995, another approach to designing supercomputers appeared. In grid computing, thousands of individual computers are networked together, even via the Internet. The combined computational power can exceed that of the all-in-one supercomputer at far less cost. In the grid approach, a problem can be broken down into components, and the components can be parceled out to the various computers. As the component problems are solved, the solutions are pieced back together mathematically to generate the overall solution.
The phenomenally fast calculation speeds of the present day supercomputers essentially corresponds to "real time," meaning an event can be monitored or analyzed as it occurs. For example, a detailed weather map, which would take a personal computer several days to compile, can be complied on a supercomputer in just a few minutes.
Supercomputers like the Japanese version are built to model events such as climate change, global warming, and earthquake patterns. Increasingly, however, supercomputers are being used for security purposes such as the analysis of electronic transmissions (i.e., email, faxes, telephone calls) for codes. For example, a network of supercomputers and satellites that is called Echelon is used to monitor electronic communications in the United States, Canada, United Kingdom, Australia, and New Zealand. The stated purpose of Echelon is to combat terrorism and organized crime activities.
The next generation of supercomputers is under development. Three particularly promising technologies are being explored. The first of these is optical computing. Light is used instead of using electrons to carry information. Light moves much faster than an electron can, therefore the speed of transmission is greater.
The second technology is known as DNA computing. Here, recombining DNA in different sequences does calculations. The sequence(s) that are favored and persist represent the optimal solution. Solutions to problems can be deduced even before the problem has actually appeared.
The third technology is called quantum computing. Properties of atoms or nuclei, designated as quantum bits, or qubits, would be the computer's processor and memory. A quantum computer would be capable of doing a computation by working on many aspects of the problem at the same time, on many different numbers at once, then using these partial results to arrive at a single answer. For example, deciphering the correct code from a 400-digit number would take a supercomputer millions of years. However, a quantum computer that is about the size of a teacup could do the job in about a year.
Further Reading
Books
Stork, David G. (ed) and Arthur C. Clarke. HAL's Legacy: 2001's Computer Dream and Reality. Boston: MIT Press, 1998.
Electronic
Cray Corporation. "What Is a Supercomputer?" Supercomputing. 2002. <http://www.cray.com/supercomputing>(15 December 2002).
The History of Computing Foundation. "Introduction to Supercomputers." Supercomputers. October 13, 2002. <http://www.thocp.net/hardware/supercomputers.htm>(15 December 2002).
| Wikipedia: Supercomputer |
For other uses, see Supercomputer (disambiguation).
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A supercomputer is a computer that is at the frontline of current processing capacity, particularly speed of calculation. Supercomputers introduced in the 1960s were designed primarily by Seymour Cray at Control Data Corporation (CDC), and led the market into the 1970s until Cray left to form his own company, Cray Research.
He then took over the supercomputer market with his new designs, holding the top spot in supercomputing for five years (1985–1990). In the 1980s a large number of smaller competitors entered the market, in parallel to the creation of the minicomputer market a decade earlier, but many of these disappeared in the mid-1990s "supercomputer market crash".
Today, supercomputers are typically one-of-a-kind custom designs produced by "traditional" companies such as Cray, IBM and Hewlett-Packard, who had purchased many of the 1980s companies to gain their experience. As of July 2009[update], the IBM Roadrunner, located at Los Alamos National Laboratory, is the fastest supercomputer in the world.
The term supercomputer itself is rather fluid, and today's supercomputer tends to become tomorrow's ordinary computer. CDC's early machines were simply very fast scalar processors, some ten times the speed of the fastest machines offered by other companies. In the 1970s most supercomputers were dedicated to running a vector processor, and many of the newer players developed their own such processors at a lower price to enter the market. The early and mid-1980s saw machines with a modest number of vector processors working in parallel to become the standard. Typical numbers of processors were in the range of four to sixteen. In the later 1980s and 1990s, attention turned from vector processors to massive parallel processing systems with thousands of "ordinary" CPUs, some being off the shelf units and others being custom designs. Today, parallel designs are based on "off the shelf" server-class microprocessors, such as the PowerPC, Opteron, or Xeon, and most modern supercomputers are now highly-tuned computer clusters using commodity processors combined with custom interconnects.
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Common uses
Supercomputers are used for highly calculation-intensive tasks such as problems involving quantum mechanical physics, weather forecasting, climate research, molecular modeling (computing the structures and properties of chemical compounds, biological macromolecules, polymers, and crystals), physical simulations (such as simulation of airplanes in wind tunnels, simulation of the detonation of nuclear weapons, and research into nuclear fusion), cryptanalysis, and many others. Major universities, military agencies and scientific research laboratories are heavy users.
A particular class of problems, known as Grand Challenge problems, are problems whose full solution requires semi-infinite computing resources.
Relevant here is the distinction between capability computing and capacity computing, as defined by Graham et al. Capability computing is typically thought of as using the maximum computing power to solve a 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. Capacity computing in contrast is typically thought of as using efficient cost-effective computing power to solve somewhat large problems or many small problems or to prepare for a run on a capability system.
Hardware and software design
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Supercomputers using custom CPUs traditionally gained their speed over conventional computers through the use of innovative designs that allow them to perform many tasks in parallel, as well as complex detail engineering. They tend to be specialized for certain types of computation, usually numerical calculations, and perform poorly at more general computing tasks. Their memory hierarchy is very carefully designed to ensure the processor is kept fed with data and instructions at all times — in fact, much of the performance difference between slower computers and supercomputers is due to the memory hierarchy. Their I/O systems tend to be designed to support high bandwidth, with latency less of an issue, because supercomputers are not used for transaction processing.
As with all highly parallel systems, Amdahl's law applies, and supercomputer designs devote great effort to eliminating software serialization, and using hardware to address the remaining bottlenecks.
Supercomputer challenges, technologies
- A supercomputer generates large amounts of heat and must be cooled. Cooling most supercomputers is a major HVAC problem.
- Information cannot move faster than the speed of light between two parts of a supercomputer. For this reason, a supercomputer that is many metres across must have latencies between its components measured at least in the tens of nanoseconds. Seymour Cray's supercomputer designs attempted to keep cable runs as short as possible for this reason: hence the cylindrical shape of his Cray range of computers. In modern supercomputers built of many conventional CPUs running in parallel, latencies of 1-5 microseconds to send a message between CPUs are typical.
- Supercomputers consume and produce massive amounts of data in a very short period of time. According to Ken Batcher, "A supercomputer is a device for turning compute-bound problems into I/O-bound problems." Much work on external storage bandwidth is needed to ensure that this information can be transferred quickly and stored/retrieved correctly.
Technologies developed for supercomputers include:
- Vector processing
- Liquid cooling
- Non-Uniform Memory Access (NUMA)
- Striped disks (the first instance of what was later called RAID)
- Parallel filesystems
Processing techniques
Vector processing techniques were first developed for supercomputers and continue to be used in specialist high-performance applications. Vector processing techniques have trickled down to the mass market in DSP architectures and SIMD (Single Instruction Multiple Data) processing instructions for general-purpose computers.
Modern video game consoles in particular use SIMD extensively and this is the basis for some manufacturers' claim that their game machines are themselves supercomputers. Indeed, some graphics cards have the computing power of several TeraFLOPS. The applications to which this power can be applied was limited by the special-purpose nature of early video processing. As video processing has become more sophisticated, Graphics processing units (GPUs) have evolved to become more useful as general-purpose vector processors, and an entire computer science sub-discipline has arisen to exploit this capability: General-Purpose Computing on Graphics Processing Units (GPGPU).
Operating systems
Supercomputer operating systems, today most often variants of Linux,[1] are at least as complex as those for smaller machines. Historically, their user interfaces tended to be less developed, as the OS developers had limited programming resources to spend on non-essential parts of the OS (i.e., parts not directly contributing to the optimal utilization of the machine's hardware). These computers, often priced at millions of dollars, are sold to a very small market and the R&D budget for the OS was often limited. The advent of Unix and Linux allows reuse of conventional desktop software and user interfaces.
Interestingly this has been a continuing trend throughout the supercomputer industry, with former technology leaders such as Silicon Graphics taking a back seat to such companies as AMD and NVIDIA, who have been able to produce cheap, feature-rich, high-performance, and innovative products due to the vast number of consumers driving their R&D.
Until the early-to-mid-1980s, supercomputers usually sacrificed instruction set compatibility and code portability for performance (processing and memory access speed). For the most part, supercomputers to this time (unlike high-end mainframes) had vastly different operating systems. The Cray-1 alone had at least six different proprietary OSs largely unknown to the general computing community. Similarly different and incompatible vectorizing and parallelizing compilers for Fortran existed. This trend would have continued with the ETA-10 were it not for the initial instruction set compatibility between the Cray-1 and the Cray X-MP, and the adoption of UNIX operating system variants (such as Cray's Unicos) and today's Linux.
In the future, the highest performance systems are likely to use a variant of Linux but with incompatible system-unique features (especially for the highest-end systems at secure facilities).[citation needed]
Programming
The parallel architectures of supercomputers often dictate the use of special programming techniques to exploit their speed. The base language of supercomputer code is generally Fortran or C, using special libraries to share data between nodes. Most commonly, 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 a problem for the interconnect characteristics of the machine it will be run on; the aim is to prevent any of the CPU's from wasting time waiting on data from other nodes.
Software tools
Software tools for distributed processing include standard APIs such as MPI and PVM, VTL and open source-based software solutions such as Beowulf, WareWulf and openMosix which facilitate the creation of a supercomputer from a collection of ordinary workstations or servers. Technology like ZeroConf (Rendezvous/Bonjour) can be used to create ad hoc computer clusters for specialized software such as Apple's Shake compositing application. An easy programming language for supercomputers remains an open research topic in computer science. Several utilities that would once have cost several thousands of dollars are now completely free thanks to the open source community which often creates disruptive technology in this arena.
Modern supercomputer architecture
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As of November 2006, the top ten supercomputers on the Top500 list (and indeed the bulk of the remainder of the list) have the same top-level architecture. Each of them is a cluster of MIMD multiprocessors, each processor of which is SIMD. The supercomputers vary radically with respect to the number of multiprocessors per cluster, the number of processors per multiprocessor, and the number of simultaneous instructions per SIMD processor. Within this hierarchy we have:
- A computer cluster is a collection of computers that are highly interconnected via a high-speed network or switching fabric. Each computer runs under a separate instance of an Operating System (OS).
- A multiprocessing computer is a computer, operating under a single OS and using more than one CPU, where the application-level software is indifferent to the number of processors. The processors share tasks using Symmetric multiprocessing (SMP) and Non-Uniform Memory Access (NUMA).
- A SIMD processor executes the same instruction on more than one set of data at the same time. The processor could be a general purpose commodity processor or special-purpose vector processor. It could also be high performance processor or a low power processor. As of 2007, the processor executes several SIMD instructions per nanosecond.
As of November 2008 the fastest heterogeneous machine is IBM Roadrunner. This machine is a cluster of 3240 computers, each with 40 processing cores and includes both AMD and Cell processors. The fastest homogeneous machine is the Cray XT5 Jaguar system at National Center for Computational Sciences with more than 19000 computers based on standard AMD processors. By contrast, Columbia is a cluster of 20 machines, each with 512 processors, each of which processes two data streams concurrently.
As of February 2009, IBM has announced work on "Sequoia" which will be a 20 petaflops supercomputer. This will be equivalent to 2 million laptops (whereas Roadrunner is comparable to a mere 100,000 laptops). It is slated for deployment in 2011. [2]
Moore's Law and economies of scale are the dominant factors in supercomputer design: a single modern desktop PC is now more powerful than a ten-year old supercomputer, and the design concepts that allowed past supercomputers to out-perform contemporaneous desktop machines have now been incorporated into commodity PCs. Furthermore, the costs of chip development and production make it uneconomical to design custom chips for a small run and favor mass-produced chips that have enough demand to recoup the cost of production. A current model quad-core Xeon workstation running at 2.66 GHz will outperform a multimillion dollar Cray C90 supercomputer used in the early 1990s; most workloads requiring such a supercomputer in the 1990s can now be done on workstations costing less than 4,000 US dollars. Supercomputing is taking a step of increasing density allowing for Desktop Supercomputers to become available, offering the computer power that in 1998 required a large room to require less than a Desktop footprint.
Additionally, many problems carried out by supercomputers are particularly suitable for parallelization (in essence, splitting up into smaller parts to be worked on simultaneously) and, particularly, fairly coarse-grained parallelization that limits the amount of information that needs to be transferred between independent processing units. For this reason, traditional supercomputers can be replaced, for many applications, by "clusters" of computers of standard design which can be programmed to act as one large computer.
Special-purpose supercomputers
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Special-purpose supercomputers are high-performance computing devices with a hardware architecture dedicated to a single problem. This allows the use of specially programmed FPGA chips or even custom VLSI chips, allowing higher price/performance ratios by sacrificing generality. They are used for applications such as astrophysics computation and brute-force codebreaking. Historically a new special-purpose supercomputer has occasionally been faster than the world's fastest general-purpose supercomputer, by some measure. For example, GRAPE-6 was faster than the Earth Simulator in 2002 for a particular special set of problems.
Examples of special-purpose supercomputers:
- Belle, Deep Blue, and Hydra, for playing chess
- Reconfigurable computing machines or parts of machines
- GRAPE, for astrophysics and molecular dynamics
- Deep Crack, for breaking the DES cipher
- MDGRAPE-3, for protein structure computation
- D.E. Shaw Research Anton, a specialised supercomputer suitable for home use [3]
The fastest supercomputers today
Measuring supercomputer speed
The speed of a supercomputer is generally measured in "FLOPS" (FLoating Point Operations Per Second), 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.) This measurement is based on a particular benchmark which does LU decomposition of a large matrix. This mimics a class of real-world problems, but is significantly easier to compute than a majority of actual real-world problems.
"Petascale" supercomputers can process one quadrilion (1015) (1000 trillion) FLOPS. Exascale is computing performance in the exaflops range. An exaflop is one quintillion (1018) FLOPS (one million teraflops).
The Top500 list
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.
Current fastest supercomputer system
On June 8, 2008, the Cell/AMD Opteron-based IBM Roadrunner at the Los Alamos National Laboratory (LANL) was announced as the fastest operational supercomputer, with a sustained processing rate of 1.026 PFLOPS.[4] The Roadrunner hardware and software was then optimized and the benchmark was re-run and submitted for the November 2008 TOP500 with an Rmax of 1.105 PFLOPS, barely surviving a challenge from the Cray XT5 Jaguar to remain the fastest computer on the "official" list.[5]
Quasi-supercomputing
Some types of large-scale distributed computing for embarrassingly parallel problems take the clustered supercomputing concept to an extreme.
The fastest, Folding@home, reported over 8.5 petaflops of processing power as of May, 2009. Of this, 2.5 petaflops of this processing power is contributed by clients running on PlayStation 3 systems and another 5.3 petaflops is contributed by their newly released GPU2 client.[6]
Another distributed computing project BOINC platform, a host for a number of distributed computing projects. As of February 2009[update], BOINC recorded a processing power of over 1.7 petaflops through over 530,000 active computers on the network.[7] One such project, SETI@home, reported processing power of over 508 teraflops through almost 317,000 active computers.[8]
As of May 2008[update], GIMPS's distributed Mersenne Prime search achieves currently 29 teraflops.[citation needed]
Also a “quasi-supercomputer” is Google's search engine system with estimated total processing power of between 126 and 316 teraflops, as of April 2004.[9] In June 2006 the New York Times estimated that the Googleplex and its server farms contain 450,000 servers.[10] According to recent estimations, the processing power of Google's cluster might reach from 20 to 100 petaflops.[11]
The PlayStation 3 Gravity Grid uses a network of 16 machines, and exploits the Cell processor for the intended application which is binary black hole coalescence using perturbation theory.[12][13] The Cell processor has a main CPU and 6 floating-point vector processors, giving the machine a net of 16 general-purpose machines and 96 vector processors. The machine has a one-time cost of $9,000 to build and is adequate for black-hole simulations which would otherwise cost $6,000 per run on a conventional supercomputer. The black hole calculations are not memory-intensive and are highly localizable, and so are well-suited to this architecture.
Other notable computer clusters are the flash mob cluster and the Beowulf cluster. The flash mob cluster allows the use of any computer in the network, while the Beowulf cluster still requires uniform architecture.
Research and development
IBM is developing the Cyclops64 architecture, intended to create a "supercomputer on a chip".
Other PFLOPS projects include one by Narendra Karmarkar in India,[14] a CDAC effort targeted for 2010,[15] and the Blue Waters Petascale Computing System funded by the NSF ($200 million) that is being built by the NCSA at the University of Illinois at Urbana-Champaign (slated to be completed by 2011).[16]
In May 2008 a collaboration was announced between NASA, SGI and Intel to build a 1 petaflops computer, Pleiades, in 2009, scaling up to 10 PFLOPs by 2012.[17] Meanwhile, IBM is constructing a 20 PFLOPs supercomputer at Lawrence Livermore National Laboratory, named Sequoia, which is scheduled to go online in 2011.
Given the current speed of progress, supercomputers are projected to reach 1 exaflops (1018) (one quintillion FLOPS) in 2019.[18] Futurist Ray Kurzweil expects supercomputers capable of human brain neural simulations, for which according to Kurzweil 10 exaflops (1019) would be required, in 2025.
Erik P. DeBenedictis of Sandia National Laboratories theorizes that a zettaflops (1021) (one sextillion FLOPS) computer is required to accomplish full weather modeling, which could cover a two week time span accurately.[19] Such systems might be built around 2030.[20]
Timeline of supercomputers
This is a list of the record-holders for fastest general-purpose supercomputer in the world, and the year each one set the record. For entries prior to 1993, this list refers to various sources[21][citation needed]. From 1993 to present, the list reflects the Top500 listing[22], and the "Peak speed" is given as the "Rmax" rating.
See also
Supercomputer Companies / Manufacturer
Supercomputer companies in operation
These companies make supercomputer hardware and/or software, either as their sole activity, or as one of several activities.
- Advanced Micro Devices, Inc.
- AVADirect
- Cray Inc.
- Dell
- Dawning
- Fujitsu
- Groupe Bull
- Hitachi
- HP
- IBM
- Intel Corporation
- Lenovo
- Linux Labs International
- Microsoft
- nCUBE
- NEC Corporation
- NVIDIA Corporation
- Quadrics
- SGI
- Sun Microsystems
- Supercomputing Systems
- Galactic Computing
- Supermicro Computer SMCI
Defunct supercomputer companies
These companies have either folded, or no longer operate in the supercomputer market.
General concepts and history
Notes
- ^ a b Top500 OS chart
- ^ IBM to build new monster supercomputerBy Tom Jowitt , TechWorld , 02/04/2009
- ^ D.E. Shaw Research Anton
- ^ "June 2008". cnet.com. http://news.cnet.com/Military-supercomputer-sets-record/2100-1010_3-6241145.html?tag=nefd.top. Retrieved on 2008-06-09.
- ^ "Jaguar Chases Roadrunner, but Can’t Grab Top Spot on Latest List of World’s TOP500 Supercomputers". TOP500. 2008-11-14. http://www.top500.org/lists/2008/11/press-release. Retrieved on 2008-11-18.
- ^ "Folding@home: OS Statistics". Stanford University. http://fah-web.stanford.edu/cgi-bin/main.py?qtype=osstats.
- ^ "BOINCstats: BOINC Combined". BOINC. http://www.boincstats.com/stats/project_graph.php?pr=bo. Retrieved on 2008-12-22.
- ^ "BOINCstats: SETI@Home". BOINC. http://www.boincstats.com/stats/project_graph.php?pr=sah. Retrieved on 2008-12-22.
- ^ How many Google machines, April 30, 2004
- ^ Markoff, John; Hensell, Saul (June 14, 2006). "Hiding in Plain Sight, Google Seeks More Power". New York Times. http://www.nytimes.com/2006/06/14/technology/14search.html. Retrieved on 2008-03-16.
- ^ Google Surpasses Supercomputer Community, Unnoticed?, May 20, 2008.
- ^ "PlayStation 3 tackles black hole vibrations", by Tariq Malik, January 28, 2009, MSNBC
- ^ PlayStation3 Gravity Grid
- ^ Athley, Gouri Agtey; Rajeshwari Adappa (30 October, 2006). ""Tatas get Karmakar to make super comp"". The Economic Times. http://economictimes.indiatimes.com/articleshow/msid-225517,curpg-2.cms. Retrieved on 2008-03-16.
- ^ C-DAC's Param programme sets to touch 10 teraflops by late 2007 and a petaflops by 2010.[dead link]
- ^ ""National Science Board Approves Funds for Petascale Computing Systems"". U.S. National Science Foundation. August 10, 2007. http://www.nsf.gov/news/news_summ.jsp?cntn_id=109850. Retrieved on 2008-03-16.
- ^ "NASA collaborates with Intel and SGI on forthcoming petaflops super computers". Heise online. 2008-05-09. http://www.heise.de/english/newsticker/news/107683.
- ^ Thibodeau, Patrick (2008-06-10). "IBM breaks petaflop barrier". InfoWorld. http://www.infoworld.com/article/08/06/10/IBM_breaks_petaflop_barrier_1.html.
- ^ DeBenedictis, Erik P. (2005). "Reversible logic for supercomputing". Proceedings of the 2nd conference on Computing frontiers. pp. 391–402. ISBN 1595930191.
- ^ "IDF: Intel says Moore's Law holds until 2029". Heise Online. 2008-04-04. http://www.heise.de/english/newsticker/news/106017.
- ^ CDC timeline at Computer History Museum
- ^ Directory page for Top500 lists. Result for each list since June 1993
External links
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Wikinews has related news: Canada's supercomputer goes online |
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| Translations: Supercomputer |
Dansk (Danish)
n. - supercomputer
Nederlands (Dutch)
supercomputer
Français (French)
n. - supercalculateur
Deutsch (German)
n. - Supercomputer
Ελληνική (Greek)
n. - (Η/Υ) υπερυπολογιστής
Italiano (Italian)
supercomputer
Português (Portuguese)
n. - supercomputador (m)
Русский (Russian)
суперкомпьютер, сверхмощная, сверхбыстро- действующая ЭВМ
Español (Spanish)
n. - superordenador
Svenska (Swedish)
n. - superdator
中文（简体）(Chinese (Simplified))
超型计算机
中文（繁體）(Chinese (Traditional))
n. - 超型電腦
한국어 (Korean)
n. - 슈퍼 컴퓨터, 초고속 전자 계산기
日本語 (Japanese)
n. - スーパーコンピューター
العربيه (Arabic)
(الاسم) الحاسوب العملاق
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