Principles Of Parallel And Distributed Computing Pdf

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Distributed computing is a field of computer science that studies distributed systems.

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Distributed systems constitute a large umbrella under which several different software systems are classified. This book will be helpful to students and IT professionals. That has led computing specialists to new computer system architecture and exploiting parallel computers, clusters of clusters, and distributed systems in the form of grids.

Parallel and Distributed Computing

Distributed computing is a field of computer science that studies distributed systems. A distributed system is a system whose components are located on different networked computers , which communicate and coordinate their actions by passing messages to one another from any system.

Three significant characteristics of distributed systems are: concurrency of components, lack of a global clock , and independent failure of components. A computer program that runs within a distributed system is called a distributed program and distributed programming is the process of writing such programs. Distributed computing also refers to the use of distributed systems to solve computational problems. In distributed computing , a problem is divided into many tasks, each of which is solved by one or more computers, [4] which communicate with each other via message passing.

The word distributed in terms such as "distributed system", "distributed programming", and " distributed algorithm " originally referred to computer networks where individual computers were physically distributed within some geographical area. While there is no single definition of a distributed system, [7] the following defining properties are commonly used as:. A distributed system may have a common goal, such as solving a large computational problem; [10] the user then perceives the collection of autonomous processors as a unit.

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.

Distributed systems are groups of networked computers which share a common goal for their work. The terms " concurrent computing ", " parallel computing ", and "distributed computing" have much overlap, and no clear distinction exists between them.

The figure on the right illustrates the difference between distributed and parallel systems. Figure a is a schematic view of a typical distributed system; the system is represented as a network topology in which each node is a computer and each line connecting the nodes is a communication link. Figure b shows the same distributed system in more detail: each computer has its own local memory, and information can be exchanged only by passing messages from one node to another by using the available communication links.

Figure c shows a parallel system in which each processor has a direct access to a shared memory. The situation is further complicated by the traditional uses of the terms parallel and distributed algorithm that do not quite match the above definitions of parallel and distributed systems see below for more detailed discussion. 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.

The use of concurrent processes which communicate through message-passing has its roots in operating system architectures studied in the s. E-mail became the most successful application of ARPANET, [23] and it is probably the earliest example of a large-scale distributed application. In addition to ARPANET and its successor, the global Internet , other early worldwide computer networks included Usenet and FidoNet from the s, 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 s and early s. 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.

At a higher level, it is necessary to interconnect processes running on those CPUs with some sort of communication system. Distributed programming typically falls into one of several basic architectures: client—server , three-tier , n -tier , or peer-to-peer ; or categories: loose coupling , or tight coupling.

Another basic aspect of distributed computing architecture is the method of communicating and coordinating work among concurrent processes.

Alternatively, a "database-centric" architecture can enable distributed computing to be done without any form of direct inter-process communication , by utilizing a shared database. This enables distributed computing functions both within and beyond the parameters of a networked database.

Examples of distributed systems and applications of distributed computing include the following: [33]. Many tasks that we would like to automate by using a computer are of question—answer type: we would like to ask a question and the computer should produce an answer.

In theoretical computer science , such tasks are called computational problems. Formally, a computational problem consists of instances together with a solution for each instance. Instances are questions that we can ask, and solutions are desired answers to these questions. Theoretical computer science seeks to understand which computational problems can be solved by using a computer computability theory and how efficiently computational complexity theory.

Traditionally, it is said that a problem can be solved by using a computer if we can design an algorithm that produces a correct solution for any given instance. Such an algorithm can be implemented as a computer program that runs on a general-purpose computer: the program reads a problem instance from input , performs some computation, and produces the solution as output. Formalisms such as random access machines or universal Turing machines can be used as abstract models of a sequential general-purpose computer executing such an algorithm.

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. In the case of distributed algorithms, computational problems are typically related to graphs.

Often the graph that describes the structure of the computer network is the problem instance. This is illustrated in the following example. Consider the computational problem of finding a coloring of a given graph G. Different fields might take the following approaches:. While the field of parallel algorithms has a different focus than the field of distributed algorithms, there is much interaction between the two fields. For example, the Cole—Vishkin algorithm for graph coloring [41] was originally presented as a parallel algorithm, but the same technique can also be used directly as a distributed algorithm.

Moreover, a parallel algorithm can be implemented either in a parallel system using shared memory or in a distributed system using message passing. In parallel algorithms, yet another resource in addition to time and space is the number of computers. Indeed, often there is a trade-off between the running time and the number of computers: the problem can be solved faster if there are more computers running in parallel see speedup.

If a decision problem can be solved in polylogarithmic time by using a polynomial number of processors, then the problem is said to be in the class NC. 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. This complexity measure is closely related to the diameter of the network. Let D be the diameter of the network.

On the one hand, any computable problem can be solved trivially in a synchronous distributed system in approximately 2 D communication rounds: simply gather all information in one location D rounds , solve the problem, and inform each node about the solution D rounds. On the other hand, if the running time of the algorithm is much smaller than D communication rounds, then the nodes in the network must produce their output without having the possibility to obtain information about distant parts of the network.

In other words, the nodes must make globally consistent decisions based on information that is available in their local D-neighbourhood. Many distributed algorithms are known with the running time much smaller than D rounds, and understanding which problems can be solved by such algorithms is one of the central research questions of the field. Another commonly used measure is the total number of bits transmitted in the network cf. Traditional computational problems take the perspective that the user asks a question, a computer or a distributed system processes the question, then produces an answer and stops.

However, there are also problems where the system is required not to stop, including the dining philosophers problem and other similar mutual exclusion problems. In these problems, the distributed system is supposed to continuously coordinate the use of shared resources so that no conflicts or deadlocks occur.

There are also fundamental challenges that are unique to distributed computing, for example those related to fault-tolerance. Examples of related problems include consensus problems , [48] Byzantine fault tolerance , [49] and self-stabilisation.

Much research is also focused on understanding the asynchronous nature of distributed systems:. Coordinator election or leader election is the process of designating a single process as the organizer of some task distributed among several computers nodes. Before the task is begun, all network nodes are either unaware which node will serve as the "coordinator" or leader of the task, or unable to communicate with the current coordinator.

After a coordinator election algorithm has been run, however, each node throughout the network recognizes a particular, unique node as the task coordinator. The network nodes communicate among themselves in order to decide which of them will get into the "coordinator" state. For that, they need some method in order to break the symmetry among them. For example, if each node has unique and comparable identities, then the nodes can compare their identities, and decide that the node with the highest identity is the coordinator.

The definition of this problem is often attributed to LeLann, who formalized it as a method to create a new token in a token ring network in which the token has been lost. Coordinator election algorithms are designed to be economical in terms of total bytes transmitted, and time. The algorithm suggested by Gallager, Humblet, and Spira [56] for general undirected graphs has had a strong impact on the design of distributed algorithms in general, and won the Dijkstra Prize for an influential paper in distributed computing.

Many other algorithms were suggested for different kind of network graphs , such as undirected rings, unidirectional rings, complete graphs, grids, directed Euler graphs, and others. A general method that decouples the issue of the graph family from the design of the coordinator election algorithm was suggested by Korach, Kutten, and Moran.

In order to perform coordination, distributed systems employ the concept of coordinators. The coordinator election problem is to choose a process from among a group of processes on different processors in a distributed system to act as the central coordinator. Several central coordinator election algorithms exist. So far the focus has been on designing a distributed system that solves a given problem. A complementary research problem is studying the properties of a given distributed system.

The halting problem is an analogous example from the field of centralised computation: we are given a computer program and the task is to decide whether it halts or runs forever. The halting problem is undecidable in the general case, and naturally understanding the behaviour of a computer network is at least as hard as understanding the behaviour of one computer.

However, there are many interesting special cases that are decidable. In particular, it is possible to reason about the behaviour of a network of finite-state machines. One example is telling whether a given network of interacting asynchronous and non-deterministic finite-state machines can reach a deadlock.

From Wikipedia, the free encyclopedia. System whose components are located on different networked computers. Not to be confused with Decentralized computing. For trustless applications, see Decentralized application. For the computer company, see DIP Research. Main article: Distributed algorithm.

Dijkstra Prize in Distributed Computing Fog computing Folding home Grid computing Inferno Jungle computing Layered queueing network Library Oriented Architecture LOA List of distributed computing conferences List of distributed computing projects List of important publications in concurrent, parallel, and distributed computing Model checking Parallel distributed processing Parallel programming model Plan 9 from Bell Labs Shared nothing architecture. Distributed systems: principles and paradigms.

Dolev Ghosh , p.

Parallel and distributed computing education: A software engineering approach

The Internet. In the initial days, computer systems were huge and also very expensive. In what fol-lows, we will show why these principles are mistaken, and why it is important to recognize the fundamental differ-ences between distributed computing and local computing. Principles of Distributed Systems. PageNumber 1 This course introduces the basic principles of distributed computing, high-lighting common themes and techniques.

principles of parallel and distributed computing pdf

This paper discusses an approach, based on software engineering principles, to introduce parallel and distributed computing into the CS curriculum. The basic assumptions are outlined, followed by a discussion of topics and their implementation in core courses. Innovations in the teaching method are also presented.

Guide for Authors

Parallel Computing

Parallel computing is a type of computing architecture in which several processors simultaneously execute multiple, smaller calculations broken down from an overall larger, complex problem. Parallel computing refers to the process of breaking down larger problems into smaller, independent, often similar parts that can be executed simultaneously by multiple processors communicating via shared memory, the results of which are combined upon completion as part of an overall algorithm. The primary goal of parallel computing is to increase available computation power for faster application processing and problem solving. Parallel computing infrastructure is typically housed within a single datacenter where several processors are installed in a server rack; computation requests are distributed in small chunks by the application server that are then executed simultaneously on each server. There are generally four types of parallel computing, available from both proprietary and open source parallel computing vendors -- bit-level parallelism, instruction-level parallelism, task parallelism, or superword-level parallelism:.

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs General Purpose Units , network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer netwo Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs General Purpose Units , network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing.

Parallel Computing : In parallel computing multiple processors performs multiple tasks assigned to them simultaneously. Memory in parallel systems can either be shared or distributed. Parallel computing provides concurrency and saves time and money. Distributed Computing : In distributed computing we have multiple autonomous computers which seems to the user as single system. In distributed systems there is no shared memory and computers communicate with each other through message passing.

curriculum. This text introduces the important principles of parallel processing. Spectrum of Parallel Computing and Distributed Computing. There is no.

Introduction to Parallel Computing.


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