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The components interact with each other in order to achieve a common goal. Alternatively, each computer may have its own user with individual needs, and the purpose of the distributed system is to coordinate the use of shared resources or provide communication services to the users. Each computer has only a limited, incomplete view of the system. Each computer may know only one part of the input. Distributed systems are groups of networked computers, which have the same goal for their work. Information is exchanged by passing messages between the processors.
The figure on the right illustrates the difference between distributed and parallel systems. Nevertheless, as a rule of thumb, high-performance parallel computation in a shared-memory multiprocessor uses parallel algorithms while the coordination of a large-scale distributed system uses distributed algorithms. 1980s, both of which were used to support distributed discussion systems. The study of distributed computing became its own branch of computer science in the late 1970s and early 1980s. Various hardware and software architectures are used for distributed computing. At a lower level, it is necessary to interconnect multiple CPUs with some sort of network, regardless of whether that network is printed onto a circuit board or made up of loosely coupled devices and cables. Most web applications are three-tier.
Instead all responsibilities are uniformly divided among all machines, known as peers. Another basic aspect of distributed computing architecture is the method of communicating and coordinating work among concurrent processes. Moreover, a distributed system may be easier to expand and manage than a monolithic uniprocessor system. Instances are questions that we can ask, and solutions are desired answers to these questions.
The field of concurrent and distributed computing studies similar questions in the case of either multiple computers, or a computer that executes a network of interacting processes: which computational problems can be solved in such a network and how efficiently? However, it is not at all obvious what is meant by “solving a problem” in the case of a concurrent or distributed system: for example, what is the task of the algorithm designer, and what is the concurrent or distributed equivalent of a sequential general-purpose computer? The discussion below focuses on the case of multiple computers, although many of the issues are the same for concurrent processes running on a single computer. All processors have access to a shared memory. The algorithm designer chooses the program executed by each processor. However, the classical PRAM model assumes synchronous access to the shared memory.
Shared-memory programs can be extended to distributed systems if the underlying operating system encapsulates the communication between nodes and virtually unifies the memory across all individual systems. There is a wide body of work on this model, a summary of which can be found in the literature. The algorithm designer chooses the structure of the network, as well as the program executed by each computer. A Boolean circuit can be seen as a computer network: each gate is a computer that runs an extremely simple computer program. Similarly, a sorting network can be seen as a computer network: each comparator is a computer. The algorithm designer only chooses the computer program.
All computers run the same program. The system must work correctly regardless of the structure of the network. In the case of distributed algorithms, computational problems are typically related to graphs. This is illustrated in the following example. The computer program finds a coloring of the graph, encodes the coloring as a string, and outputs the result. However, multiple computers can access the same string in parallel. Each computer might focus on one part of the graph and produce a coloring for that part.
The main focus is on high-performance computation that exploits the processing power of multiple computers in parallel. Each computer must produce its own color as output. The main focus is on coordinating the operation of an arbitrary distributed system. While the field of parallel algorithms has a different focus than the field of distributed algorithms, there is a lot of interaction between the two fields. In parallel algorithms, yet another resource in addition to time and space is the number of computers. The class NC can be defined equally well by using the PRAM formalism or Boolean circuits—PRAM machines can simulate Boolean circuits efficiently and vice versa.
In the analysis of distributed algorithms, more attention is usually paid on communication operations than computational steps. Perhaps the simplest model of distributed computing is a synchronous system where all nodes operate in a lockstep fashion. In such systems, a central complexity measure is the number of synchronous communication rounds required to complete the task. However, there are also problems where we do not want the system to ever stop.