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IBM's.Parallel computing is a type of in which many calculations or the execution of are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing:,. Parallelism has long been employed in, but it's gaining broader interest due to the physical constraints preventing. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in, mainly in the form of.Parallel computing is closely related to —they are frequently used together, and often conflated, though the two are distinct: it is possible to have parallelism without concurrency (such as ), and concurrency without parallelism (such as multitasking by on a single-core CPU). In parallel computing, a computational task is typically broken down into several, often many, very similar sub-tasks that can be processed independently and whose results are combined afterwards, upon completion. In contrast, in concurrent computing, the various processes often do not address related tasks; when they do, as is typical in, the separate tasks may have a varied nature and often require some during execution.Parallel computers can be roughly classified according to the level at which the hardware supports parallelism, with and computers having multiple within a single machine, while, and use multiple computers to work on the same task.

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Specialized parallel computer architectures are sometimes used alongside traditional processors, for accelerating specific tasks.In some cases parallelism is transparent to the programmer, such as in bit-level or instruction-level parallelism, but explicitly, particularly those that use concurrency, are more difficult to write than ones, because concurrency introduces several new classes of potential, of which are the most common. And between the different subtasks are typically some of the greatest obstacles to getting good parallel program performance.A theoretical on the of a single program as a result of parallelization is given. Contents.Background Traditionally, has been written for. To solve a problem, an is constructed and implemented as a serial stream of instructions. These instructions are executed on a 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 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. This led to the design of parallel hardware and software, as well as.was the dominant reason for improvements in from the mid-1980s until 2004.

The 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 the average time it takes to execute an instruction. An increase in frequency thus decreases runtime for all programs.

However, power consumption P by a chip is given by the equation P = C × V 2 × F, where C is the being switched per clock cycle (proportional to the number of transistors whose inputs change), V is, and F is the processor frequency (cycles per second). Increases in frequency increase the amount of power used in a processor. Increasing processor power consumption led ultimately to 's May 8, 2004 cancellation of its 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 (CPU or processor) manufacturers started to produce power efficient processors with multiple cores.

The core is the computing unit of the processor and in multi-core processors each core is independent and can access the same memory concurrently. Have brought parallel computing to. Thus parallelisation of serial programmes has become a mainstream programming task. In 2012 quad-core processors became standard for, while have 10 and 12 core processors. From it can be predicted that 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.An can ensure that different tasks and user programmes are run in parallel on the available cores. However, for a serial software programme to take full advantage of the multi-core architecture the programmer needs to restructure and parallelise the code.

A speed-up of application software runtime will no longer be achieved through frequency scaling, instead programmers will need to parallelise their software code to take advantage of the increasing computing power of multicore architectures. Amdahl's law and Gustafson's law.

Main article:Parallel programming languages and parallel computers must have a (also known as a memory model). The consistency model defines rules for how operations on occur and how results are produced.One of the first consistency models was 's model. Sequential consistency is the property of a parallel program that its parallel execution produces the same results as a sequential program. Specifically, a program is sequentially consistent if 'the results of any execution is the same as if the operations of all the processors were executed in some sequential order, and the operations of each individual processor appear in this sequence in the order specified by its program'.is a common type of consistency model. Software transactional memory borrows from the concept of and applies them to memory accesses.Mathematically, these models can be represented in several ways. Introduced in 1962, were an early attempt to codify the rules of consistency models. Dataflow theory later built upon these, and were created to physically implement the ideas of dataflow theory.

Beginning in the late 1970s, such as and were developed to permit algebraic reasoning about systems composed of interacting components. More recent additions to the process calculus family, such as the, have added the capability for reasoning about dynamic topologies.

Logics such as Lamport's, and mathematical models such as and, have also been developed to describe the behavior of concurrent systems. See also: Flynn's taxonomy created one of the earliest classification systems for parallel (and sequential) computers and programs, now known as. 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.Single data stream.Multiple data streams.The single-instruction-single-data (SISD) classification 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 applications. Multiple-instruction-single-data (MISD) is a rarely used classification. While computer architectures to deal with this were devised (such as ), few applications that fit this class materialized.

Multiple-instruction-multiple-data (MIMD) programs are by far the most common type of parallel programs.According to and, '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.' Types of parallelism Bit-level parallelism. Main article:From the advent of (VLSI) computer-chip fabrication technology in the 1970s until about 1986, speed-up in computer architecture was driven by doubling —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 processor must add two, 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 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, microprocessors were replaced with 8-bit, then 16-bit, then 32-bit microprocessors. This trend generally came to an end with 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 architectures, did processors become commonplace.Instruction-level parallelism. A canonical five-stage processor. In the best case scenario, it takes one clock cycle to complete one instruction and thus the processor can issue scalar performance ( IPC = 1).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 ( IPC. A canonical five-stage processor. In the best case scenario, it takes one clock cycle to complete two instructions and thus the processor can issue superscalar performance ( IPC = 2 1).Most modern processors also have multiple. 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 processors. Instructions can be grouped together only if there is no between them.

And the (which is similar to scoreboarding but makes use of ) are two of the most common techniques for implementing out-of-order execution and instruction-level parallelism.Task parallelism. Main article:Task parallelisms 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 the size of a problem. Hardware Memory and communication Main memory in a parallel computer is either (shared between all processing elements in a single ), or (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.

And combine the two approaches, where the processing element has its own local memory and access to the memory on non-local processors. Accesses to local memory are typically faster than accesses to non-local memory. On the, distributed shared memory space can be implemented using the programming model such as. 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, this external shared memory system is known as, which is typically built from arrays of physically distributed across multiple I/O nodes. A logical view of a (NUMA) architecture.

Processors in one directory can access that directory's memory with less latency than they can access memory in the other directory's memory.Computer architectures in which each element of main memory can be accessed with equal and are known as (UMA) systems. Typically, that can be achieved only by a system, in which the memory is not physically distributed. A system that does not have this property is known as a (NUMA) architecture. Distributed memory systems have non-uniform memory access.Computer systems make use of —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 the possibility of incorrect program execution. These computers require a system, which keeps track of cached values and strategically purges them, thus ensuring correct program execution. 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 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 ) memory, a, a shared or an interconnect network of a myriad of including, fat hypercube (a hypercube with more than one processor at a node), or.Parallel computers based on interconnected networks need to have some kind of 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.Classes of parallel computers Parallel computers can be roughly classified according to the level at which 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.Multi-core computing.

Main article:A multi-core processor is a processor that includes multiple (called 'cores') on the same chip. This processor differs from a processor, which includes multiple and can issue multiple instructions per clock cycle from one instruction stream (thread); in contrast, a multi-core processor can issue multiple instructions per clock cycle from multiple instruction streams. 's, designed for use in the, 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.(of which Intel's 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. On the other hand includes a single execution unit in the same processing unit and can issue one instruction at a time from multiple threads.Symmetric multiprocessing. Main article:A symmetric multiprocessor (SMP) is a computer system with multiple identical processors that share memory and connect via a bus. 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 a sufficient amount of memory bandwidth exists. Distributed computing. Main article: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 ', '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'; the processors in a typical distributed system run concurrently in parallel. Cluster computing. A.A cluster is a group of loosely coupled computers that work together closely, so that in some respects they can be regarded as a single computer. Clusters are composed of multiple standalone machines connected by a network. While machines in a cluster do not have to be symmetric, is more difficult if they are not.

The most common type of cluster is the, which is a cluster implemented on multiple identical computers connected with a. Beowulf technology was originally developed. 87% of all supercomputers are clusters. The remaining are Massively Parallel Processors, explained below.Because grid computing systems (described below) can easily handle embarrassingly parallel problems, modern clusters are typically designed to handle more difficult problems—problems that require nodes to share intermediate results with each other more often. This requires a high bandwidth and, more importantly, a low- interconnection network. Many historic and current supercomputers use customized high-performance network hardware specifically designed for cluster computing, such as the Cray Gemini network.

As of 2014, most current supercomputers use some off-the-shelf standard network hardware, often, or.Massively parallel computing. A cabinet from 's massively parallel.A massively parallel processor (MPP) is a single computer with many networked processors.

MPPs have many of the same characteristics as clusters, but MPPs have specialized interconnect networks (whereas clusters use commodity hardware for networking). MPPs also tend to be larger than clusters, typically having 'far more' than 100 processors. In an MPP, 'each CPU contains its own memory and copy of the operating system and application.

Each subsystem communicates with the others via a high-speed interconnect.' 's, the fifth fastest in the world according to the June 2009 ranking, is an MPP.Grid computing. Main article:Grid computing is the most distributed form of parallel computing. It makes use of computers communicating over the to work on a given problem. Because of the low bandwidth and extremely high latency available on the Internet, distributed computing typically deals only with problems. Have been created, of which and are the best-known examples.Most grid computing applications use (software that sits between the operating system and the application to manage network resources and standardize the software interface). The most common distributed computing middleware is the (BOINC).

Often, distributed computing software makes use of 'spare cycles', performing computations at times when a computer is idling.Specialized parallel computers Within parallel computing, there are specialized parallel devices that remain niche areas of interest. While not, they tend to be applicable to only a few classes of parallel problems.Reconfigurable computing with field-programmable gate arrays is the use of a (FPGA) as a co-processor to a general-purpose computer. An FPGA is, in essence, a computer chip that can rewire itself for a given task.FPGAs can be programmed with such as.

However, programming in these languages can be tedious. Several vendors have created languages that attempt to emulate the syntax and semantics of the, with which most programmers are familiar. The best known C to HDL languages are,. Specific subsets of based on C can also be used for this purpose.AMD's decision to open its technology to third-party vendors has become the enabling technology for high-performance reconfigurable computing.

According to Michael R. D'Amour, Chief Operating Officer of, 'when we first walked into AMD, they called us 'the stealers.' Now they call us their partners.' General-purpose computing on graphics processing units (GPGPU). Nvidia'sGeneral-purpose computing on (GPGPU) is a fairly recent trend in computer engineering research.

GPUs are co-processors that have been heavily optimized for processing. Computer graphics processing is a field dominated by data parallel operations—particularly operations.In the early days, GPGPU programs used the normal graphics APIs for executing programs. However, several new programming languages and platforms have been built to do general purpose computation on GPUs with both and releasing programming environments with and respectively.

Other GPU programming languages include,. Nvidia has also released specific products for computation in their. The technology consortium Khronos Group has released the specification, which is a framework for writing programs that execute across platforms consisting of CPUs and GPUs., and others are supporting.Application-specific integrated circuits. Main article:Several (ASIC) approaches have been devised for dealing with parallel applications.Because an ASIC is (by definition) specific to a given application, it can be fully optimized for that application. As a result, for a given application, an ASIC tends to outperform a general-purpose computer. However, ASICs are created. This process requires a mask set, which can be extremely expensive.

A mask set can cost over a million US dollars. (The smaller the transistors required for the chip, the more expensive the mask will be.) Meanwhile, performance increases in general-purpose computing over time (as described by ) tend to wipe out these gains in only one or two chip generations. High initial cost, and the tendency to be overtaken by Moore's-law-driven general-purpose computing, has rendered ASICs unfeasible for most parallel computing applications.

However, some have been built. One example is the PFLOPS machine which uses custom ASICs for simulation.Vector processors. The is a vector processor.A vector processor is a CPU or computer system that can execute the same instruction on large sets of data. Vector processors have high-level operations that work on linear arrays of numbers or vectors. An example vector operation is A = B × C, where A, B, and C are each 64-element vectors of 64-bit numbers. They are closely related to Flynn's SIMD classification.computers became famous for their vector-processing computers in the 1970s and 1980s.

However, vector processors—both as CPUs and as full computer systems—have generally disappeared. Modern do include some vector processing instructions, such as with 's and 's (SSE).Software Parallel programming languages. Main article:, and (such as ) have been created for programming parallel computers. These can generally be divided into classes based on the assumptions they make about the underlying memory architecture—shared memory, distributed memory, or shared distributed memory.

Shared memory programming languages communicate by manipulating shared memory variables. Distributed memory uses. And are two of the most widely used shared memory APIs, whereas (MPI) is the most widely used message-passing system API. One concept used in programming parallel programs is the, where one part of a program promises to deliver a required datum to another part of a program at some future time.and are also coordinating their effort to make (HMPP) directives an open standard called. The OpenHMPP directive-based programming model offers a syntax to efficiently offload computations on hardware accelerators and to optimize data movement to/from the hardware memory. OpenHMPP directives describe remote procedure call (RPC) on an accelerator device (e.g. GPU) or more generally a set of cores.

The directives annotate or codes to describe two sets of functionalities: the offloading of procedures (denoted codelets) onto a remote device and the optimization of data transfers between the CPU main memory and the accelerator memory.The rise of consumer GPUs has led to support for, either in graphics APIs (referred to as ), in dedicated APIs (such as ), or in other language extensions.Automatic parallelization. Main article:Automatic parallelization of a sequential program by a is the 'holy grail' of parallel computing, especially with the aforementioned limit of processor frequency. Despite decades of work by compiler researchers, automatic parallelization has had only limited success.Mainstream parallel programming languages remain either or (at best), in which a programmer gives the compiler for parallelization. A few fully implicit parallel programming languages exist—, Parallel, (for ), and.Application checkpointing. Main article:As a computer system grows in complexity, the usually decreases. Is a technique whereby the computer system takes a 'snapshot' of the application—a record of all current resource allocations and variable states, akin to a —; this information can be used to restore the program if the computer should fail. Application checkpointing means that the program has to restart from only its last checkpoint rather than the beginning.

While checkpointing provides benefits in a variety of situations, it is especially useful in highly parallel systems with a large number of processors used in. Algorithmic methods As parallel computers become larger and faster, we are now able to solve problems that had previously taken too long to run. Fields as varied as (for and ) and economics (for ) have taken advantage of parallel computing. Common types of problems in parallel computing applications include:. Dense. Sparse linear algebra. Spectral methods (such as ).

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(such as ). problems (such as ). problems (such as found in ). (such as ). (such as ).

methods. (such as detecting and constructing ). simulationFault tolerance. Further information:Parallel computing can also be applied to the design of, particularly via systems performing the same operation in parallel. This provides in case one component fails, and also allows automatic and if the results differ. These methods can be used to help prevent single-event upsets caused by transient errors.

Although additional measures may be required in embedded or specialized systems, this method can provide a cost effective approach to achieve n-modular redundancy in commercial off-the-shelf systems.History. 'the most infamous of supercomputers'.The origins of true (MIMD) parallelism go back to and his Sketch of the Invented by.In April 1958, S. Gill (Ferranti) discussed parallel programming and the need for branching and waiting. Also in 1958, IBM researchers and discussed the use of parallelism in numerical calculations for the first time.

Introduced the D825 in 1962, a four-processor computer that accessed up to 16 memory modules through a. In 1967, Amdahl and Slotnick published a debate about the feasibility of parallel processing at American Federation of Information Processing Societies Conference. It was during this debate that was coined to define the limit of speed-up due to parallelism.In 1969, introduced its first system, a symmetric multiprocessor system capable of running up to eight processors in parallel., a multi-processor project at in the 1970s, was among the first multiprocessors with more than a few processors. The first bus-connected multiprocessor with snooping caches was the in 1984.SIMD parallel computers can be traced back to the 1970s. The motivation behind early SIMD computers was to amortize the of the processor's over multiple instructions. In 1964, Slotnick had proposed building a massively parallel computer for the. His design was funded by the, which was the earliest SIMD parallel-computing effort,.

The key to its design was a fairly high parallelism, with up to 256 processors, which allowed the machine to work on large datasets in what would later be known as. However, ILLIAC IV was called 'the most infamous of supercomputers', because the project was only one-fourth completed, but took 11 years and cost almost four times the original estimate. When it was finally ready to run its first real application in 1976, it was outperformed by existing commercial supercomputers such as the.Biological brain as massively parallel computer In the early 1970s, at the, and started developing the theory, which views the biological brain as. In 1986, Minsky published The Society of Mind, which claims that “mind is formed from many little agents, each mindless by itself”. The theory attempts to explain how what we call intelligence could be a product of the interaction of non-intelligent parts.

Minsky says that the biggest source of ideas about the theory came from his work in trying to create a machine that uses a robotic arm, a video camera, and a computer to build with children's blocks.Similar models (which also view the biological brain as a massively parallel computer, i.e., the brain is made up of a constellation of independent or semi-independent agents) were also described by:. Thomas R. Blakeslee,.,.,.,.,.,.

Neurocluster Brain Model.See also.References.