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- Distributed computing
- An introduction to distributed and parallel computing
- Distributed and Parallel 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.
This is the first tutorial in the "Livermore Computing Getting Started" workshop. It is intended to provide only a brief overview of the extensive and broad topic of Parallel Computing, as a lead-in for the tutorials that follow it. As such, it covers just the very basics of parallel computing, and is intended for someone who is just becoming acquainted with the subject and who is planning to attend one or more of the other tutorials in this workshop. It is not intended to cover Parallel Programming in depth, as this would require significantly more time.
The tutorial begins with a discussion on parallel computing - what it is and how it's used, followed by a discussion on concepts and terminology associated with parallel computing.
The topics of parallel memory architectures and programming models are then explored. These topics are followed by a series of practical discussions on a number of the complex issues related to designing and running parallel programs. The tutorial concludes with several examples of how to parallelize simple serial programs. References are included for further self-study.
In the simplest sense, parallel computing is the simultaneous use of multiple compute resources to solve a computational problem:. A standalone "computer in a box". Nodes are networked together to comprise a supercomputer. This varies, depending upon who you talk to. Then, multiple CPUs were incorporated into a node. Then, individual CPUs were subdivided into multiple "cores", each being a unique execution unit.
CPUs with multiple cores are sometimes called "sockets" - vendor dependent. The result is a node with multiple CPUs, each containing multiple cores. The nomenclature is confused at times. Wonder why? A logically discrete section of computational work. A task is typically a program or program-like set of instructions that is executed by a processor.
A parallel program consists of multiple tasks running on multiple processors. Breaking a task into steps performed by different processor units, with inputs streaming through, much like an assembly line; a type of parallel computing. From a strictly hardware point of view, describes a computer architecture where all processors have direct usually bus based access to common physical memory. In a programming sense, it describes a model where parallel tasks all have the same "picture" of memory and can directly address and access the same logical memory locations regardless of where the physical memory actually exists.
Shared memory hardware architecture where multiple processors share a single address space and have equal access to all resources. In hardware, refers to network based memory access for physical memory that is not common.
As a programming model, tasks can only logically "see" local machine memory and must use communications to access memory on other machines where other tasks are executing. Parallel tasks typically need to exchange data. There are several ways this can be accomplished, such as through a shared memory bus or over a network, however the actual event of data exchange is commonly referred to as communications regardless of the method employed.
The coordination of parallel tasks in real time, very often associated with communications. Often implemented by establishing a synchronization point within an application where a task may not proceed further until another task s reaches the same or logically equivalent point.
Synchronization usually involves waiting by at least one task, and can therefore cause a parallel application's wall clock execution time to increase. In parallel computing, granularity is a qualitative measure of the ratio of computation to communication. The amount of time required to coordinate parallel tasks, as opposed to doing useful work. Parallel overhead can include factors such as:.
Refers to the hardware that comprises a given parallel system - having many processing elements. The meaning of "many" keeps increasing, but currently, the largest parallel computers are comprised of processing elements numbering in the hundreds of thousands to millions.
Solving many similar, but independent tasks simultaneously; little to no need for coordination between the tasks.
Factors that contribute to scalability include:. Machine memory was physically distributed across networked machines, but appeared to the user as a single shared memory global address space. Generically, this approach is referred to as "virtual shared memory".
However, the ability to send and receive messages using MPI, as is commonly done over a network of distributed memory machines, was implemented and commonly used. In both cases, the programmer is responsible for determining the parallelism although compilers can sometimes help.
Calculate the potential energy for each of several thousand independent conformations of a molecule. When done, find the minimum energy conformation. This problem is able to be solved in parallel.
Each of the molecular conformations is independently determinable. The calculation of the minimum energy conformation is also a parallelizable problem. Calculation of the Fibonacci series 0,1,1,2,3,5,8,13,21, The calculation of the F n value uses those of both F n-1 and F n-2 , which must be computed first. Each program calculates the population of a given group, where each group's growth depends on that of its neighbors.
As time progresses, each process calculates its current state, then exchanges information with the neighbor populations. All tasks then progress to calculate the state at the next time step. An audio signal data set is passed through four distinct computational filters. Each filter is a separate process. The first segment of data must pass through the first filter before progressing to the second.
When it does, the second segment of data passes through the first filter. By the time the fourth segment of data is in the first filter, all four tasks are busy. Each model component can be thought of as a separate task. Arrows represent exchanges of data between components during computation: the atmosphere model generates wind velocity data that are used by the ocean model, the ocean model generates sea surface temperature data that are used by the atmosphere model, and so on.
There are a number of important factors to consider when designing your program's inter-task communications:. Skip to main content. Search form. Introduction to Parallel Computing Tutorial.
Why Use Parallel Computing? Who is Using Parallel Computing? Overview What is Parallel Computing? N-body simulations - particles may migrate across task domains requiring more work for some tasks.
An introduction to distributed and parallel computing
This is the first tutorial in the "Livermore Computing Getting Started" workshop. It is intended to provide only a brief overview of the extensive and broad topic of Parallel Computing, as a lead-in for the tutorials that follow it. As such, it covers just the very basics of parallel computing, and is intended for someone who is just becoming acquainted with the subject and who is planning to attend one or more of the other tutorials in this workshop. It is not intended to cover Parallel Programming in depth, as this would require significantly more time. The tutorial begins with a discussion on parallel computing - what it is and how it's used, followed by a discussion on concepts and terminology associated with parallel computing. The topics of parallel memory architectures and programming models are then explored.
It seems that you're in Germany. We have a dedicated site for Germany. There are many applications that require parallel and distributed processing to allow complicated engineering, business and research problems to be solved in a reasonable time. Parallel and distributed processing is able to improve company profit, lower costs of design, production, and deployment of new technologies, and create better business environments. The major lesson learned by car and aircraft engineers, drug manufacturers, genome researchers and other specialist is that a computer system is a very powerful tool that is able to help them solving even more complicated problems.
This was written as a unit for an introductory algorithms course. It's material that often doesn't appear in textbooks for such courses, which is a pity because distributed algorithms is an important topic in today's world. PDF version available. Classically, algorithm designers assume a computer with only one processing element; the algorithms they design are said to be sequential , since the algorithms' steps must be performed in a particular sequence , one after another. But today's computers often have multiple processors, each of which performs its own sequence of steps. Even basic desktop computers often have multicore processors that include a handful of processing elements. But also significant are vast clusters of computers, such as those used by the Department of Defense to simulate nuclear explosions and by Google to process search engine queries.
Distributed and Parallel Computing
In the simplest sense, parallel computing is the simultaneous use of multiple compute resources to solve a computational problem: A problem is broken into discrete parts that can be solved concurrently; Each part is further broken down to a series of instructions. ISBN hardback 1. Introduction Definitions. Application, steps for building an RM] application, testing and debugging.
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.
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