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What is a Super Computer?
A supercomputer is a computer at the frontline of current processing capacity, particularly speed of calculation.
Supercomputers are used for highly calculation-intensive tasks such as problems including quantum physics, weather forecasting, climate research, molecular modeling (computing the structures and properties of chemical compounds, biological macromolecules, polymers, and crystals), and physical simulations (such as simulation of airplanes in wind tunnels, simulation of the detonation of nuclear weapons, and research into nuclear fusion).
Supercomputers were introduced in the 1960s and were designed primarily by Seymour Cray at Control Data Corporation (CDC), which 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. Currently, Japan's K computer, built by Fujitsu in Kobe, Japan is the fastest in the world.[2] It is three times faster than previous one to hold that title, the Tianhe-1A supercomputer located in China.
The term supercomputer itself is rather fluid, and the speed of earlier "supercomputers" tends to become typical of future ordinary computers. 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 (see Transputer by instance). Today, parallel designs are based on "off the shelf" server-class microprocessors, such as the PowerPC, Opteron, or Xeon, and coprocessors like NVIDIA Tesla GPGPUs, AMD GPUs, IBM Cell, FPGAs. The architecture of today's supercomputers is implemented using highly-tuned computer clusters with thousands of commodity processors intercommunicating with custom interconnects.
******** 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.

Current fastest supercomputer system
The K computer is the worlds fastest supercomputer at 10.51 petaFLOPS. It consists of 88,000 SPARC64 VIIIfx CPUs, and spans 864 server racks. Fujitsu was not able to give the official power consumption of the completed K cluster, but in June, when it reached a one petaflop peak, it consumed 9.89 megawatts, costing $9.89 million dollars a year

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).
The current Top500 list (from May 2010) has 3 supercomputers based on GPGPUs. In particular, the number 4 supercomputer, Nebulae built by Dawning in China, is based on GPGPUs.

Operating Sysytems
Supercomputers today most often use variants of the Linux operating system as shown by the graph to the right.[36]
Until the early-to-mid-1980s, supercomputers usually sacrificed instruction set compatibility and code portability for performance (processing and memory access speed). For the most part, supercomputers to this time (unlike high-end mainframes) had vastly different operating systems. The Cray-1 alone had at least six different proprietary OSs largely unknown to the general computing community. In a similar manner, different and incompatible vectorizing and parallelizing compilers for Fortran existed. This trend would have continued with the ETA-10 were it not for the initial instruction set compatibility between the Cray-1 and the Cray X-MP, and the adoption of computer systems such as Cray's Unicos, or Linux.

Programing
The parallel architectures of supercomputers often dictate the use of special programming techniques to exploit their speed. The base language of supercomputer code is, in general, Fortran or C, using special libraries to share data between nodes. 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 and

Modern supercomputer architecture
Supercomputers today often have a similar top-level architecture consisting of a cluster of MIMD multiprocessors, each processor of which is SIMD, and with each multiprocessor controlling multiple co-processors. The supercomputers vary radically with respect to the number of multiprocessors per cluster, the number of processors per multiprocessor, the number of simultaneous instructions per SIMD processor, and the type and number of co-processors. 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 instance of an OS and using more than one CPU core, wherein the application-level software is indifferent to the number of CPU cores. The cores share tasks using Symmetric multiprocessing (SMP) and Non-Uniform Memory Access (NUMA). The cores may all be in from one to thousands of multicore processor devices.
A SIMD core executes the same instruction on more than one set of data at the same time. The core may be a general purpose commodity core or special-purpose vector processor. It may be in a high-performance processor or a low power processor. As of 2007, each core executes several SIMD instructions per nanosecond.
A co-processor is incapable of executing "standard" code, but with specialized programming can exceed the performance of the multiprocessor by several orders of magnitude for certain applications. Co-processors are often GPGPUs. The ratio of coprocessors to general-purpose processors varies dramatically, The benchmark used for measuring TOP500 performance disregards the contribution of co-processors.
As of October 2010 the fastest supercomputer in the world is the K computer which has over 68,000 8-core processors, while Tianhe-1A system at National University of Defense Technology comes at second number with more than 14,000 multi-core processors.
In February 2009, IBM also announced work on "Sequoia," which appears to 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 late 2011. The Sequoia will be powered by 1.6 million cores (specific 45-nanometer chips in development) and 1.6 petabytes of memory. It will be housed in 96 refrigerators spanning roughly 3,000 square feet (280 m2).
Moore's Law and economies of scale are the dominant factors in supercomputer design. The design concepts that allowed past supercomputers to out-perform desktop machines of the time tended to be gradually 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 be done on workstations costing less than 4,000 US dollars as of 2010. 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.
In addition, many problems carried out by supercomputers are particularly suitable for parallelization (in essence, splitting up into smaller parts to be worked on simultaneously) and, in particular, 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
A special-purpose supercomputer is a high-performance computing device 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.