Tuesday, April 25, 2023

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Windows 10 parallel desktop image free.Introduction to Parallel Computing Tutorial



   

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 several simple problems. 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:.

Historically, parallel computing has been considered to be "the high end of computing," and has been used to model difficult problems in many areas of science and engineering:. Today, commercial applications provide an equal or greater driving force in the development of faster computers.

These applications require the processing of large amounts of data in sophisticated ways. For example:. Parallel computers still follow this basic design, just multiplied in units. The basic, fundamental architecture remains the same. Contemporary CPUs consist of one or more cores - a distinct execution unit with its own instruction stream. Cores with a CPU may be organized into one or more sockets - each socket with its own distinct memory. When a CPU consists of two or more sockets, usually hardware infrastructure supports memory sharing across sockets.

A standalone "computer in a box. Nodes are networked together to comprise a supercomputer. 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.

Describes a computer architecture where all processors have direct 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 - memory, disk, etc.

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. 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 quantitative or qualitative measure of the ratio of computation to communication. Required execution time that is unique to parallel tasks, as opposed to that for 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 first 10, members 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. An example of a parallel algorithm for solving this problem using Binet's formula :. In this type of partitioning, the data associated with a problem is decomposed. Each parallel task then works on a portion of the data. In this approach, the focus is on the computation that is to be performed rather than on the data manipulated by the computation.

The problem is decomposed according to the work that must be done. Each task then performs a portion of the overall work. Functional decomposition lends itself well to problems that can be split into different tasks. 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:. Why Use Parallel Computing? Who Is Using Parallel Computing? Overview What Is Parallel Computing? Serial Computing Traditionally, software has been written for serial computation: A problem is broken into a discrete series of instructions Instructions are executed sequentially one after another Executed on a single processor Only one instruction may execute at any moment in time.

Image Sparse arrays - some tasks will have actual data to work on while others have mostly "zeros. Image Adaptive grid methods - some tasks may need to refine their mesh while others don't. Image N-body simulations - particles may migrate across task domains requiring more work for some tasks.



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