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109-589: The WCCC is open to all types of computers including microprocessors, supercomputers , clusters , and dedicated chess hardware. Due to the requirement to be present on-site, play on a physical board, and strict rules of originality, many strong programs refrain from participating in the ICGA events. As the conditions of the software championship can easily be emulated by anyone with a high-end PC, there are now privately conducted tournaments, such as Top Chess Engine Championship , that have much broader attendance, as well as

218-506: A massively parallel processing architecture, with 514 microprocessors , including 257 Zilog Z8001 control processors and 257 iAPX 86/20 floating-point processors . It was mainly used for rendering realistic 3D computer graphics . Fujitsu's VPP500 from 1992 is unusual since, to achieve higher speeds, its processors used GaAs , a material normally reserved for microwave applications due to its toxicity. Fujitsu 's Numerical Wind Tunnel supercomputer used 166 vector processors to gain

327-437: A variable that is shared between them. Without synchronization, the instructions between the two threads may be interleaved in any order. For example, consider the following program: If instruction 1B is executed between 1A and 3A, or if instruction 1A is executed between 1B and 3B, the program will produce incorrect data. This is known as a race condition . The programmer must use a lock to provide mutual exclusion . A lock

436-532: A combination of parallelism and concurrency characteristics. Parallel computers can be roughly classified according to the level at which the hardware supports parallelism, with multi-core and multi-processor computers having multiple processing elements within a single machine, while clusters , MPPs , and grids use multiple computers to work on the same task. Specialized parallel computer architectures are sometimes used alongside traditional processors, for accelerating specific tasks. In some cases parallelism

545-580: A desktop computer has performance in the range of hundreds of gigaFLOPS (10 ) to tens of teraFLOPS (10 ). Since November 2017, all of the world's fastest 500 supercomputers run on Linux -based operating systems. Additional research is being conducted in the United States, the European Union, Taiwan, Japan, and China to build faster, more powerful and technologically superior exascale supercomputers. Supercomputers play an important role in

654-457: A high performance I/O system to achieve high levels of performance. 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 list of the computers which appeared at

763-432: A larger number of games to reduce the influence of chance. In 2007, the reigning champion Junior declined to defend its title. For the 2009 edition, the rules were changed to limit platforms to commodity hardware supporting at most eight cores , thereby excluding supercomputers and large clusters. However, this was reversed in the following year and a parallel Software Championship was held instead; unlimited hardware

872-659: A larger system such as a full Linux distribution on server and I/O nodes. While in a traditional multi-user computer system job scheduling is, in effect, a tasking problem for processing and peripheral resources, in a massively parallel system, the job management system needs to manage the allocation of both computational and communication resources, as well as gracefully deal with inevitable hardware failures when tens of thousands of processors are present. Although most modern supercomputers use Linux -based operating systems, each manufacturer has its own specific Linux distribution, and no industry standard exists, partly due to

981-472: A long chain of dependent calculations; there are usually opportunities to execute independent calculations in parallel. Let P i and P j be two program segments. Bernstein's conditions describe when the two are independent and can be executed in parallel. For P i , let I i be all of the input variables and O i the output variables, and likewise for P j . P i and P j are independent if they satisfy Violation of

1090-499: A lot of capacity but are not typically considered supercomputers, given that they do not solve a single very complex problem. In general, the speed of supercomputers is measured and benchmarked in FLOPS (floating-point operations per second), and not in terms of MIPS (million instructions per second), as is the case with general-purpose computers. These measurements are commonly used with an SI prefix such as tera- , combined into

1199-404: A more realistic assessment of the parallel performance. Understanding data dependencies is fundamental in implementing parallel algorithms . No program can run more quickly than the longest chain of dependent calculations (known as the critical path ), since calculations that depend upon prior calculations in the chain must be executed in order. However, most algorithms do not consist of just

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1308-520: A multi-core processor can issue multiple instructions per clock cycle from multiple instruction streams. IBM 's Cell microprocessor , designed for use in the Sony PlayStation 3 , is a prominent multi-core processor. Each core in a multi-core processor can potentially be superscalar as well—that is, on every clock cycle, each core can issue multiple instructions from one thread. Simultaneous multithreading (of which Intel's Hyper-Threading

1417-403: A node), or n-dimensional mesh . Parallel computers based on interconnected networks need to have some kind of routing to enable the passing of messages between nodes that are not directly connected. The medium used for communication between the processors is likely to be hierarchical in large multiprocessor machines. Parallel computers can be roughly classified according to the level at which

1526-543: A pipelined processor is a RISC processor, with five stages: instruction fetch (IF), instruction decode (ID), execute (EX), memory access (MEM), and register write back (WB). The Pentium 4 processor had a 35-stage pipeline. Most modern processors also have multiple execution units . They usually combine this feature with pipelining and thus can issue more than one instruction per clock cycle ( IPC > 1 ). These processors are known as superscalar processors. Superscalar processors differ from multi-core processors in that

1635-472: A processing power of over 166 petaFLOPS through over 762 thousand active Computers (Hosts) on the network. As of October 2016 , Great Internet Mersenne Prime Search 's (GIMPS) distributed Mersenne Prime search achieved about 0.313 PFLOPS through over 1.3 million computers. The PrimeNet server has supported GIMPS's grid computing approach, one of the earliest volunteer computing projects, since 1997. Quasi-opportunistic supercomputing

1744-471: A result, shared memory computer architectures do not scale as well as distributed memory systems do. Processor–processor and processor–memory communication can be implemented in hardware in several ways, including via shared (either multiported or multiplexed ) memory, a crossbar switch , a shared bus or an interconnect network of a myriad of topologies including star , ring , tree , hypercube , fat hypercube (a hypercube with more than one processor at

1853-412: A single address space ), or distributed memory (in which each processing element has its own local address space). Distributed memory refers to the fact that the memory is logically distributed, but often implies that it is physically distributed as well. Distributed shared memory and memory virtualization combine the two approaches, where the processing element has its own local memory and access to

1962-483: A single large problem in the shortest amount of time. Often a capability system is able to solve a problem of a size or complexity that no other computer can, e.g. a very complex weather simulation application. Capacity computing, in contrast, is typically thought of as using efficient cost-effective computing power to solve a few somewhat large problems or many small problems. Architectures that lend themselves to supporting many users for routine everyday tasks may have

2071-500: A sufficient amount of memory bandwidth exists. A distributed computer (also known as a distributed memory multiprocessor) is a distributed memory computer system in which the processing elements are connected by a network. Distributed computers are highly scalable. The terms " concurrent computing ", "parallel computing", and "distributed computing" have a lot of overlap, and no clear distinction exists between them. The same system may be characterized both as "parallel" and "distributed";

2180-572: 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 the Atlas was only 16,000 words, with a drum providing memory for a further 96,000 words. The Atlas Supervisor 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 program could be executed on

2289-512: A time from multiple threads. A symmetric multiprocessor (SMP) is a computer system with multiple identical processors that share memory and connect via a bus . Bus contention prevents bus architectures from scaling. As a result, SMPs generally do not comprise more than 32 processors. Because of the small size of the processors and the significant reduction in the requirements for bus bandwidth achieved by large caches, such symmetric multiprocessors are extremely cost-effective, provided that

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2398-511: Is a bare-metal compute model to execute code, but each user is given virtualized login node. POD computing nodes are connected via non-virtualized 10 Gbit/s Ethernet or QDR InfiniBand networks. User connectivity to the POD data center ranges from 50 Mbit/s to 1 Gbit/s. Citing Amazon's EC2 Elastic Compute Cloud, Penguin Computing argues that virtualization of compute nodes

2507-415: Is a form of distributed computing whereby the "super virtual computer" of many networked geographically disperse computers performs computing tasks that demand huge processing power. Quasi-opportunistic supercomputing aims to provide a higher quality of service than opportunistic grid computing by achieving more control over the assignment of tasks to distributed resources and the use of intelligence about

2616-416: Is a programming language construct that allows one thread to take control of a variable and prevent other threads from reading or writing it, until that variable is unlocked. The thread holding the lock is free to execute its critical section (the section of a program that requires exclusive access to some variable), and to unlock the data when it is finished. Therefore, to guarantee correct program execution,

2725-608: Is achieved by balancing enhancements to both parallelizable and non-parallelizable components of a task. Furthermore, it reveals that increasing the number of processors yields diminishing returns, with negligible speedup gains beyond a certain point. Amdahl's Law has limitations, including assumptions of fixed workload, neglecting inter-process communication and synchronization overheads, primarily focusing on computational aspect and ignoring extrinsic factors such as data persistence, I/O operations, and memory access overheads. Gustafson's law and Universal Scalability Law give

2834-466: Is an emerging direction, e.g. as in the Cyclops64 system. As the price, performance and energy efficiency of general-purpose graphics processing units (GPGPUs) have improved, a number of petaFLOPS supercomputers such as Tianhe-I and Nebulae have started to rely on them. However, other systems such as the K computer continue to use conventional processors such as SPARC -based designs and

2943-486: Is constructed and implemented as a serial stream of instructions. These instructions are executed on a central processing unit on one computer. Only one instruction may execute at a time—after that instruction is finished, the next one is executed. Parallel computing, on the other hand, uses multiple processing elements simultaneously to solve a problem. This is accomplished by breaking the problem into independent parts so that each processing element can execute its part of

3052-737: Is converted into heat, requiring cooling. For example, Tianhe-1A consumes 4.04  megawatts (MW) of electricity. The cost to power and cool the system can be significant, e.g. 4 MW at $ 0.10/kWh is $ 400 an hour or about $ 3.5 million per year. Heat management is a major issue in complex electronic devices and affects powerful computer systems in various ways. The thermal design power and CPU power dissipation issues in supercomputing surpass those of traditional computer cooling technologies. The supercomputing awards for green computing reflect this issue. The packing of thousands of processors together inevitably generates significant amounts of heat density that need to be dealt with. The Cray-2

3161-516: Is equivalent to an entirely sequential program. The single-instruction-multiple-data (SIMD) classification is analogous to doing the same operation repeatedly over a large data set. This is commonly done in signal processing applications. Multiple-instruction-single-data (MISD) is a rarely used classification. While computer architectures to deal with this were devised (such as systolic arrays ), few applications that fit this class materialized. Multiple-instruction-multiple-data (MIMD) programs are by far

3270-465: Is known as burst buffer , which is typically built from arrays of non-volatile memory physically distributed across multiple I/O nodes. Computer architectures in which each element of main memory can be accessed with equal latency and bandwidth are known as uniform memory access (UMA) systems. Typically, that can be achieved only by a shared memory system, in which the memory is not physically distributed. A system that does not have this property

3379-429: Is known as a non-uniform memory access (NUMA) architecture. Distributed memory systems have non-uniform memory access. Computer systems make use of caches —small and fast memories located close to the processor which store temporary copies of memory values (nearby in both the physical and logical sense). Parallel computer systems have difficulties with caches that may store the same value in more than one location, with

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3488-493: Is necessary, such as semaphores , barriers or some other synchronization method . Subtasks in a parallel program are often called threads . Some parallel computer architectures use smaller, lightweight versions of threads known as fibers , while others use bigger versions known as processes . However, "threads" is generally accepted as a generic term for subtasks. Threads will often need synchronized access to an object or other resource , for example when they must update

3597-409: Is not suitable for HPC. Penguin Computing has also criticized that HPC clouds may have allocated computing nodes to customers that are far apart, causing latency that impairs performance for some HPC applications. 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

3706-606: Is once again allowed in the championship proper. From 2010 a new tournament was introduced and held at the same location and during the same period as the World Computer Chess Championship. The rules for the World Chess Software Championship ( WCSC ) state that competing programs must run on machines with identical hardware specifications. Time control is game in 45 minutes with 15 second increment. From 1980 to 2001,

3815-684: Is quite difficult to debug and test parallel programs. Special techniques need to be used for testing and debugging such applications. 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. The fastest grid computing system

3924-428: Is the volunteer computing project Folding@home (F@h). As of April 2020 , F@h reported 2.5 exaFLOPS of x86 processing power. Of this, over 100 PFLOPS are contributed by clients running on various GPUs, and the rest from various CPU systems. The Berkeley Open Infrastructure for Network Computing (BOINC) platform hosts a number of volunteer computing projects. As of February 2017 , BOINC recorded

4033-429: Is the best known) was an early form of pseudo-multi-coreism. A processor capable of concurrent multithreading includes multiple execution units in the same processing unit—that is it has a superscalar architecture—and can issue multiple instructions per clock cycle from multiple threads. Temporal multithreading on the other hand includes a single execution unit in the same processing unit and can issue one instruction at

4142-529: Is the characteristic of a parallel program that "entirely different calculations can be performed on either the same or different sets of data". This contrasts with data parallelism, where the same calculation is performed on the same or different sets of data. Task parallelism involves the decomposition of a task into sub-tasks and then allocating each sub-task to a processor for execution. The processors would then execute these sub-tasks concurrently and often cooperatively. Task parallelism does not usually scale with

4251-455: Is the computing unit of the processor and in multi-core processors each core is independent and can access the same memory concurrently. Multi-core processors have brought parallel computing to desktop computers . Thus parallelization of serial programs has become a mainstream programming task. In 2012 quad-core processors became standard for desktop computers , while servers have 10+ core processors. From Moore's law it can be predicted that

4360-440: Is transparent to the programmer, such as in bit-level or instruction-level parallelism, but explicitly parallel algorithms , particularly those that use concurrency, are more difficult to write than sequential ones, because concurrency introduces several new classes of potential software bugs , of which race conditions are the most common. Communication and synchronization between the different subtasks are typically some of

4469-556: The Blue Gene system, IBM deliberately used low power processors to deal with heat density. The IBM Power 775 , released in 2011, has closely packed elements that require water cooling. The IBM Aquasar system uses hot water cooling to achieve energy efficiency, the water being used to heat buildings as well. The energy efficiency of computer systems is generally measured in terms of " FLOPS per watt ". In 2008, Roadrunner by IBM operated at 376  MFLOPS/W . In November 2010,

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4578-760: The Blue Gene/Q reached 1,684 MFLOPS/W and in June 2011 the top two spots on the Green 500 list were occupied by Blue Gene machines in New York (one achieving 2097 MFLOPS/W) with the DEGIMA cluster in Nagasaki placing third with 1375 MFLOPS/W. Because copper wires can transfer energy into a supercomputer with much higher power densities than forced air or circulating refrigerants can remove waste heat ,

4687-613: The DES cipher . Throughout the decades, the management of heat density has remained a key issue for most centralized supercomputers. The large amount of heat generated by a system may also have other effects, e.g. reducing the lifetime of other system components. There have been diverse approaches to heat management, from pumping Fluorinert through the system, to a hybrid liquid-air cooling system or air cooling with normal air conditioning temperatures. A typical supercomputer consumes large amounts of electrical power, almost all of which

4796-465: 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. In 1998, David Bader developed

4905-626: The grid computing approach, the processing power of many computers, organized as distributed, diverse administrative domains, is opportunistically used whenever a computer is available. In another approach, many processors are used in proximity to each other, e.g. in a computer cluster . In such a centralized massively parallel system the speed and flexibility of the interconnect becomes very important and modern supercomputers have used various approaches ranging from enhanced Infiniband systems to three-dimensional torus interconnects . The use of multi-core processors combined with centralization

5014-561: The speedup from parallelization would be linear—doubling the number of processing elements should halve the runtime, and doubling it a second time should again halve the runtime. However, very few parallel algorithms achieve optimal speedup. Most of them have a near-linear speedup for small numbers of processing elements, which flattens out into a constant value for large numbers of processing elements. The maximum potential speedup of an overall system can be calculated by Amdahl's law . Amdahl's Law indicates that optimal performance improvement

5123-519: 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. Since the end of the 20th century, supercomputer operating systems have undergone major transformations, based on the changes in supercomputer architecture . While early operating systems were custom tailored to each supercomputer to gain speed,

5232-423: The 1970s until about 1986, speed-up in computer architecture was driven by doubling computer word size —the amount of information the processor can manipulate per cycle. Increasing the word size reduces the number of instructions the processor must execute to perform an operation on variables whose sizes are greater than the length of the word. For example, where an 8-bit processor must add two 16-bit integers ,

5341-445: The 80 MHz Cray-1 in 1976, which became one of the most successful supercomputers in history. The Cray-2 was released in 1985. It had eight central processing units (CPUs), liquid cooling and the electronics coolant liquid Fluorinert was pumped through the supercomputer architecture . It reached 1.9  gigaFLOPS , making it the first supercomputer to break the gigaflop barrier. The only computer to seriously challenge

5450-520: 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

5559-520: The Cray. Another problem was that writing software for the system was difficult, and getting peak performance from it was a matter of serious effort. But the partial success of the ILLIAC IV was widely seen as pointing the way to the future of supercomputing. Cray argued against this, famously quipping that "If you were plowing a field, which would you rather use? Two strong oxen or 1024 chickens?" But by

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5668-611: The ICCA/ICGA organized a separate cycle of championships limited to programs running on microprocessors . In the first three championships, the winners were dedicated chess computers, and then in 1984, Richard Lang's Psion program shared first place, running on an IBM PC under MS-DOS . At the 14th WMCCC in Jakarta, the Israeli team Junior was denied entry to Indonesia and some other teams dropped out in protest. The 16th WMCCC

5777-650: The National Computational Science Alliance (NCSA) to ensure interoperability, as none of it had been run on Linux previously. Using the successful prototype design, he led the development of "RoadRunner," the first Linux supercomputer for open use by the national science and engineering community via the National Science Foundation's National Technology Grid. RoadRunner was put into production use in April 1999. At

5886-454: The ability of the cooling systems to remove waste heat is a limiting factor. As of 2015 , many existing supercomputers have more infrastructure capacity than the actual peak demand of the machine – designers generally conservatively design the power and cooling infrastructure to handle more than the theoretical peak electrical power consumed by the supercomputer. Designs for future supercomputers are power-limited –

5995-484: The above program can be rewritten to use locks: One thread will successfully lock variable V, while the other thread will be locked out —unable to proceed until V is unlocked again. This guarantees correct execution of the program. Locks may be necessary to ensure correct program execution when threads must serialize access to resources, but their use can greatly slow a program and may affect its reliability . Locking multiple variables using non-atomic locks introduces

6104-546: The achievable throughput, derived from the LINPACK benchmarks and shown as "Rmax" in the TOP500 list. The LINPACK benchmark typically performs LU decomposition of a large matrix. The LINPACK performance gives some indication of performance for some real-world problems, but does not necessarily match the processing requirements of many other supercomputer workloads, which for example may require more memory bandwidth, or may require better integer computing performance, or may need

6213-443: The algorithm simultaneously with the others. The processing elements can be diverse and include resources such as a single computer with multiple processors, several networked computers, specialized hardware, or any combination of the above. Historically parallel computing was used for scientific computing and the simulation of scientific problems, particularly in the natural and engineering sciences , such as meteorology . This led to

6322-486: The amount of power used in a processor. Increasing processor power consumption led ultimately to Intel 's May 8, 2004 cancellation of its Tejas and Jayhawk processors, which is generally cited as the end of frequency scaling as the dominant computer architecture paradigm. To deal with the problem of power consumption and overheating the major central processing unit (CPU or processor) manufacturers started to produce power efficient processors with multiple cores. The core

6431-424: The attention of high-performance computing (HPC) users and developers in recent years. Cloud computing attempts to provide HPC-as-a-service exactly like other forms of services 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

6540-505: 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 the implementation of grid-wise allocation agreements, co-allocation subsystems, communication topology-aware allocation mechanisms, fault tolerant message passing libraries and data pre-conditioning. Cloud computing with its recent and rapid expansions and development have grabbed

6649-462: The available cores. However, for a serial software program to take full advantage of the multi-core architecture the programmer needs to restructure and parallelize the code. A speed-up of application software runtime will no longer be achieved through frequency scaling, instead programmers will need to parallelize their software code to take advantage of the increasing computing power of multicore architectures. Main article: Amdahl's law Optimally,

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6758-460: The average time it takes to execute an instruction. An increase in frequency thus decreases runtime for all compute-bound programs. However, power consumption P by a chip is given by the equation P = C × V × F , where C is the capacitance being switched per clock cycle (proportional to the number of transistors whose inputs change), V is voltage , and F is the processor frequency (cycles per second). Increases in frequency increase

6867-444: The costs associated with merging data from multiple processes. Specifically, inter-process communication and synchronization can lead to overheads that are substantially higher—often by two or more orders of magnitude—compared to processing the same data on a single thread. Therefore, the overall improvement should be carefully evaluated. From the advent of very-large-scale integration (VLSI) computer-chip fabrication technology in

6976-419: 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 more quickly than about 200 MFLOPS while being much larger and more complex than

7085-430: The decade, increasing amounts of parallelism were added, with one to four processors being typical. In 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

7194-400: The design of parallel hardware and software, as well as high performance computing . Frequency scaling was the dominant reason for improvements in computer performance from the mid-1980s until 2004. The runtime of a program is equal to the number of instructions multiplied by the average time per instruction. Maintaining everything else constant, increasing the clock frequency decreases

7303-497: The dominant paradigm in computer architecture , mainly in the form of multi-core processors . In computer science , parallelism and concurrency are two different things: a parallel program uses multiple CPU cores , each core performing a task independently. On the other hand, concurrency enables a program to deal with multiple tasks even on a single CPU core; the core switches between tasks (i.e. threads ) without necessarily completing each one. A program can have both, neither or

7412-643: The early 1980s, several teams were working on parallel designs with thousands of processors, notably the Connection Machine (CM) that developed from research at MIT . The CM-1 used as many as 65,536 simplified custom microprocessors connected together in a network to share data. Several updated versions followed; the CM-5 supercomputer is a massively parallel processing computer capable of many billions of arithmetic operations per second. In 1982, Osaka University 's LINKS-1 Computer Graphics System used

7521-552: The early moments of the universe, airplane and spacecraft aerodynamics , the detonation of nuclear weapons , and nuclear fusion ). They have been essential in the field of cryptanalysis . Supercomputers were introduced in the 1960s, and for several decades the fastest was 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 more quickly than their more general-purpose contemporaries. Through

7630-457: The easiest to parallelize. Michael J. Flynn created one of the earliest classification systems for parallel (and sequential) computers and programs, now known as Flynn's taxonomy . Flynn classified programs and computers by whether they were operating using a single set or multiple sets of instructions, and whether or not those instructions were using a single set or multiple sets of data. The single-instruction-single-data (SISD) classification

7739-408: The fact that the differences in hardware architectures require changes to optimize the operating system to each hardware design. 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 software such as Beowulf . In

7848-410: 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

7957-569: The first Linux supercomputer using commodity parts. While at the University of New Mexico, Bader sought to build a supercomputer running Linux using consumer off-the-shelf parts and a high-speed low-latency interconnection network. The prototype utilized an Alta Technologies "AltaCluster" of eight dual, 333 MHz, Intel Pentium II computers running a modified Linux kernel. Bader ported a significant amount of software to provide Linux support for necessary components as well as code from members of

8066-436: The first condition introduces a flow dependency, corresponding to the first segment producing a result used by the second segment. The second condition represents an anti-dependency, when the second segment produces a variable needed by the first segment. The third and final condition represents an output dependency: when two segments write to the same location, the result comes from the logically last executed segment. Consider

8175-534: The first supercomputers was the IBM 7030 Stretch . The IBM 7030 was built by IBM for the Los Alamos National Laboratory , which then 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

8284-551: The following functions, which demonstrate several kinds of dependencies: In this example, instruction 3 cannot be executed before (or even in parallel with) instruction 2, because instruction 3 uses a result from instruction 2. It violates condition 1, and thus introduces a flow dependency. In this example, there are no dependencies between the instructions, so they can all be run in parallel. Bernstein's conditions do not allow memory to be shared between different processes. For that, some means of enforcing an ordering between accesses

8393-415: The greatest obstacles to getting optimal parallel program performance. A theoretical upper bound on the speed-up of a single program as a result of parallelization is given by Amdahl's law , which states that it is limited by the fraction of time for which the parallelization can be utilised. Traditionally, computer software has been written for serial computation . To solve a problem, an algorithm

8502-547: The hardware supports parallelism. This classification is broadly analogous to the distance between basic computing nodes. These are not mutually exclusive; for example, clusters of symmetric multiprocessors are relatively common. A multi-core processor is a processor that includes multiple processing units (called "cores") on the same chip. This processor differs from a superscalar processor, which includes multiple execution units and can issue multiple instructions per clock cycle from one instruction stream (thread); in contrast,

8611-608: The introduction of 32-bit processors, which has been a standard in general-purpose computing for two decades. Not until the early 2000s, with the advent of x86-64 architectures, did 64-bit processors become commonplace. A computer program is, in essence, a stream of instructions executed by a processor. Without instruction-level parallelism, a processor can only issue less than one instruction per clock cycle ( IPC < 1 ). These processors are known as subscalar processors. These instructions can be re-ordered and combined into groups which are then executed in parallel without changing

8720-511: The memory on non-local processors. Accesses to local memory are typically faster than accesses to non-local memory. On the supercomputers , distributed shared memory space can be implemented using the programming model such as PGAS . This model allows processes on one compute node to transparently access the remote memory of another compute node. All compute nodes are also connected to an external shared memory system via high-speed interconnect, such as Infiniband , this external shared memory system

8829-560: 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

8938-442: The most common type of parallel programs. According to David A. Patterson and John L. Hennessy , "Some machines are hybrids of these categories, of course, but this classic model has survived because it is simple, easy to understand, and gives a good first approximation. It is also—perhaps because of its understandability—the most widely used scheme." Parallel computing can incur significant overhead in practice, primarily due to

9047-441: The norm. 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, with China becoming increasingly active in the field. As of November 2024 , Lawrence Livermore National Laboratory's El Capitan is the world's fastest supercomputer. The US has five of

9156-474: The number of cores per processor will double every 18–24 months. This could mean that after 2020 a typical processor will have dozens or hundreds of cores, however in reality the standard is somewhere in the region of 4 to 16 cores, with some designs having a mix of performance and efficiency cores (such as ARM's big.LITTLE design) due to thermal and design constraints. An operating system can ensure that different tasks and user programs are run in parallel on

9265-612: 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. In 2016, Penguin Computing, Parallel Works, R-HPC, Amazon Web Services , Univa , Silicon Graphics International , Rescale , Sabalcore, and Gomput started to offer HPC cloud computing . The Penguin On Demand (POD) cloud

9374-457: 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 to it. However, GPUs are gaining ground, and in 2012 the Jaguar supercomputer

9483-511: The overall performance of a computer system, yet the goal of the Linpack benchmark is to approximate how fast the computer solves numerical problems and it is widely used in the industry. The FLOPS measurement is either quoted based on the theoretical floating point performance of a processor (derived from manufacturer's processor specifications and shown as "Rpeak" in the TOP500 lists), which is generally unachievable when running real workloads, or

9592-824: The overhead from resource contention or communication dominates the time spent on other computation, further parallelization (that is, splitting the workload over even more threads) increases rather than decreases the amount of time required to finish. This problem, known as parallel slowdown , can be improved in some cases by software analysis and redesign. Applications are often classified according to how often their subtasks need to synchronize or communicate with each other. An application exhibits fine-grained parallelism if its subtasks must communicate many times per second; it exhibits coarse-grained parallelism if they do not communicate many times per second, and it exhibits embarrassing parallelism if they rarely or never have to communicate. Embarrassingly parallel applications are considered

9701-541: The overheating problem was solved by introducing refrigeration to the supercomputer design. Thus, the CDC6600 became the fastest computer in the world. Given that the 6600 outperformed all the other contemporary computers by about 10 times, it was dubbed a supercomputer and defined the supercomputing market, when one hundred computers were sold at $ 8 million each. Cray left CDC in 1972 to form his own company, Cray Research . Four years after leaving CDC, Cray delivered

9810-452: The possibility of incorrect program execution. These computers require a cache coherency system, which keeps track of cached values and strategically purges them, thus ensuring correct program execution. Bus snooping is one of the most common methods for keeping track of which values are being accessed (and thus should be purged). Designing large, high-performance cache coherence systems is a very difficult problem in computer architecture. As

9919-495: The possibility of program deadlock . An atomic lock locks multiple variables all at once. If it cannot lock all of them, it does not lock any of them. If two threads each need to lock the same two variables using non-atomic locks, it is possible that one thread will lock one of them and the second thread will lock the second variable. In such a case, neither thread can complete, and deadlock results. Many parallel programs require that their subtasks act in synchrony . This requires

10028-564: The processor must first add the 8 lower-order bits from each integer using the standard addition instruction, then add the 8 higher-order bits using an add-with-carry instruction and the carry bit from the lower order addition; thus, an 8-bit processor requires two instructions to complete a single operation, where a 16-bit processor would be able to complete the operation with a single instruction. Historically, 4-bit microprocessors were replaced with 8-bit, then 16-bit, then 32-bit microprocessors. This trend generally came to an end with

10137-629: The result of the program. This is known as instruction-level parallelism. Advances in instruction-level parallelism dominated computer architecture from the mid-1980s until the mid-1990s. All modern processors have multi-stage instruction pipelines . Each stage in the pipeline corresponds to a different action the processor performs on that instruction in that stage; a processor with an N -stage pipeline can have up to N different instructions at different stages of completion and thus can issue one instruction per clock cycle ( IPC = 1 ). These processors are known as scalar processors. The canonical example of

10246-440: 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 has gained broader interest due to the physical constraints preventing frequency scaling . As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become

10355-473: The several execution units are not entire processors (i.e. processing units). Instructions can be grouped together only if there is no data dependency between them. Scoreboarding and the Tomasulo algorithm (which is similar to scoreboarding but makes use of register renaming ) are two of the most common techniques for implementing out-of-order execution and instruction-level parallelism. Task parallelisms

10464-605: The shorthand TFLOPS (10 FLOPS, pronounced teraflops ), or peta- , combined into the shorthand PFLOPS (10 FLOPS, pronounced petaflops .) Petascale supercomputers can process one quadrillion (10 ) (1000 trillion) FLOPS. Exascale is computing performance in the exaFLOPS (EFLOPS) range. An EFLOPS is one quintillion (10 ) FLOPS (one million TFLOPS). However, The performance of a supercomputer can be severely impacted by fluctuation brought on by elements like system load, network traffic, and concurrent processes, as mentioned by Brehm and Bruhwiler (2015). No single number can reflect

10573-432: The size of a problem. Superword level parallelism is a vectorization technique based on loop unrolling and basic block vectorization. It is distinct from loop vectorization algorithms in that it can exploit parallelism of inline code , such as manipulating coordinates, color channels or in loops unrolled by hand. Main memory in a parallel computer is either shared memory (shared between all processing elements in

10682-439: The supercomputer at any one time. Atlas was a joint venture between Ferranti and Manchester University and was designed to operate at processing speeds approaching one microsecond per instruction, about one million instructions per second. The CDC 6600 , designed by Seymour Cray , was finished in 1964 and marked the transition from germanium to silicon transistors. Silicon transistors could run more quickly and

10791-444: The time of its deployment, it was considered one of the 100 fastest supercomputers in the world. Though Linux-based clusters using consumer-grade parts, such as Beowulf , existed prior to the development of Bader's prototype and RoadRunner, they lacked the scalability, bandwidth, and parallel computing capabilities to be considered "true" supercomputers. Systems with a massive number of processors generally take one of two paths. In

10900-605: The top 10; Japan, Finland, Switzerland, Italy and Spain have one each. In June 2018, all combined supercomputers on the TOP500 list broke the 1 exaFLOPS mark. In 1960, UNIVAC built the Livermore Atomic Research Computer (LARC), today considered among the first supercomputers, for the US Navy Research and Development Center. It still used high-speed drum memory , rather than the newly emerging disk drive technology. Also, among

11009-604: The top of the TOP500 list since June 1993, and the "Peak speed" is given as the "Rmax" rating. In 2018, Lenovo became the world's largest provider for the TOP500 supercomputers with 117 units produced. Rpeak country system 1,685.65 (9,248 × 64-core Optimized 3rd Generation EPYC 64C @2.0 GHz) Parallel computing Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at

11118-406: The top spot in 1994 with a peak speed of 1.7  gigaFLOPS (GFLOPS) per processor. The Hitachi SR2201 obtained a peak performance of 600 GFLOPS in 1996 by using 2048 processors connected via a fast three-dimensional crossbar network. The Intel Paragon could have 1000 to 4000 Intel i860 processors in various configurations and was ranked the fastest in the world in 1993. The Paragon

11227-422: The trend has been to move away from in-house operating systems to the adaptation of generic software such as Linux . Since modern massively parallel supercomputers typically separate computations from other services by using multiple types of nodes , they usually run different operating systems on different nodes, e.g. using a small and efficient lightweight kernel such as CNK or CNL on compute nodes, but

11336-576: The use of a barrier . Barriers are typically implemented using a lock or a semaphore . One class of algorithms, known as lock-free and wait-free algorithms , altogether avoids the use of locks and barriers. However, this approach is generally difficult to implement and requires correctly designed data structures. Not all parallelization results in speed-up. Generally, as a task is split up into more and more threads, those threads spend an ever-increasing portion of their time communicating with each other or waiting on each other for access to resources. Once

11445-511: Was liquid cooled , and used a Fluorinert "cooling waterfall" which was forced through the modules under pressure. However, the submerged liquid cooling approach was not practical for the multi-cabinet systems based on off-the-shelf processors, and in System X a special cooling system that combined air conditioning with liquid cooling was developed in conjunction with the Liebert company . In

11554-624: Was a MIMD machine which connected processors via a high speed two-dimensional mesh, allowing processes to execute on separate nodes, communicating via the Message Passing Interface . Software development remained a problem, but the CM series sparked off considerable research into this issue. Similar designs using custom hardware were made by many companies, including the Evans & Sutherland ES-1 , MasPar , nCUBE , Intel iPSC and

11663-684: Was completed in 1961 and despite not meeting the challenge of a hundredfold increase in performance, it was purchased by the Los Alamos National Laboratory. Customers in England and France also bought the computer, and it became the basis for the IBM 7950 Harvest , a supercomputer built for cryptanalysis . The third pioneering supercomputer project in the early 1960s was the Atlas at the University of Manchester , built by

11772-520: Was the same as the 9th WCCC above. Supercomputer This is an accepted version of this page A supercomputer is a type of 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 2022, supercomputers have existed which can perform over 10  FLOPS, so called exascale supercomputers . For comparison,

11881-771: Was transformed into Titan by retrofitting CPUs with GPUs. High-performance computers have an expected life cycle of about three years before requiring an upgrade. The Gyoukou supercomputer is unique in that it uses both a massively parallel design and liquid immersion cooling . A number of special-purpose systems have been designed, dedicated to a single problem. This allows the use of specially programmed FPGA chips or even custom ASICs , allowing better price/performance ratios by sacrificing generality. Examples of special-purpose supercomputers include Belle , Deep Blue , and Hydra for playing chess , Gravity Pipe for astrophysics, MDGRAPE-3 for protein structure prediction and molecular dynamics, and Deep Crack for breaking

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