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MATLAB Parallel Computing Toolbox

MATLAB Parallel Computing Toolbox. A.Hosseini Email: s.a.hosseini86@gmail.com Course : Professional Architecture. Product Overview. solve computationally and data intensive problems using multicore processors, GPUs, and computer clusters.

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MATLAB Parallel Computing Toolbox

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  1. MATLAB Parallel Computing Toolbox A.Hosseini Email: s.a.hosseini86@gmail.com Course : Professional Architecture

  2. Product Overview • solve computationally and data intensive problems using multicore processors, GPUs, and computer clusters. • The toolbox provides twelve workers to execute applications locally without changing the code • MDCS software allows you to run as many MATLAB workers on a remote cluster

  3. Key Problems Addressed by Parallel Computing • Running Parallel for-Loops (parfor) • Executing Batch Jobs in Parallel • Partitioning Large Data Sets

  4. Running Parallel for-Loops (parfor) • Many applications involve multiple repetitive segments of code. • Often you can use for-loops to solve these cases. • The ability to execute code in parallel, on one computer or on a cluster of computers, can significantly improve performance in many cases • Many iterations • Long iterations

  5. Executing Batch Jobs in Parallel • When working interactively in a MATLAB session, you can offload work to a MATLAB worker session to run as a batch job. • MATLAB session is not blocked. • The MATLAB worker can run either on the same machine as the client, or if using MATLAB Distributed Computing Server, on a remote cluster machine.

  6. Partitioning Large Data Sets • If you have an array that is too large for your computer's memory, it cannot be easily handled in a single MATLAB session. Parallel Computing Toolbox software allows you to distribute that array among multiple MATLAB workers, so that each worker contains only a part of the array.

  7. Introduction to Parallel Solutions • Interactively Run a Loop in Parallel • Run a Batch Job • Run a Batch Parallel Loop • Distributing Arrays and Running SPMD

  8. Interactively Run a Loop in Parallel • This section shows how to modify a simple for-loop so that it runs in parallel. for i=1:1024 A(i) = sin(i*2*pi/1024); End plot(A) matlabpool open local 3 parfori=1:1024 A(i) = sin(i*2*pi/1024); End plot(A) matlabpool close

  9. Run a Batch Job • To offload work from your MATLAB session to another session, you can use the batch command. This example uses the for-loop from the last section inside a script. edit mywave for i=1:1024 A(i) = sin(i*2*pi/1024); End job = batch('mywave') wait(job) load(job, 'A') plot(A) destroy(job)

  10. Run a Batch Parallel Loop • combine the abilities to offload a job and run a parallel loop

  11. Distributing Arrays and Running SPMD • Distributed Arrays (see page 9 on document) • Single Program Multiple Data(SPMD) (see page 34 on document)

  12. Interactive Parallel Computation with pmode(P45) • pmode lets you work interactively with a parallel job running simultaneously on several labs. • provides a desktop with a display for each lab running the job. • When you exit your pmode session, its job is effectively destroyed, and all information and data on the labs is lost. Starting another pmode session always begins from a clean state.

  13. Run Parallel Jobs Interactively Using pmode

  14. Profiling Parallel Code • The parallel profiler provides an extension of the profile command and the profile viewer specifically for parallel jobs, to enable you to see how much time each lab spends evaluating each function and how much time communicating or waiting for communications with the other labs. • mpiprofile on • Execution time of each function on each lab • Execution time of each line of code in each function • Amount of data transferred between each lab • Amount of time each lab spends waiting for communications

  15. Viewing Parallel Profile Data • P>> R1 = rand(16, codistributor()) • P>> R2 = rand(16, codistributor()) • P>> mpiprofile on • P>> P = R1*R2 • P>> mpiprofile off • P>> mpiprofile viewer

  16. The function summary report

  17. Parallel computing with MATLAB • Parallel Computing Toolbox (formerly Distributed Computing Toolbox) should be installed on the computer where you write your applications. • MATLAB Distributed Computing Server (formerly MATLAB Distributed Computing Engine) should be installed on each computer of the cluster that performs the computation.

  18. job • A job is some large operation that you need to perform in your MATLAB session. A job is broken down into segments called tasks. You decide how best to divide your job into tasks. You could divide your job into identical tasks, but tasks do not have to be identical.

  19. client • this is on the machine where you program MATLAB. The client uses Parallel Computing Toolbox software to perform the definition of jobs and tasks. MATLAB Distributed Computing Server software is the product that performs the execution of your job by evaluating each of its tasks and returning the result to your client session.

  20. job manager • the part of the engine that coordinates the execution of jobs and the evaluation of their tasks. The job manager distributes the tasks for evaluation to the server's individual MATLAB sessions called workers.

  21. Basic Parallel Computing Configuration

  22. Life Cycle of a Job

  23. Parallel Configurations for Cluster Access • You create and modify configurations through the Configurations Manager. You access the Configurations Manager using the Parallel pull-down menu on the MATLAB desktop. Select Parallel > Manage Configurations to open the Configurations Manger.

  24. The first time you open the Configurations Manager, it lists only one configuration called local, which at first is the default configuration and has only default settings.

  25. Creating and Modifying User Configurations • In the Configurations Manager, select New > jobmanager. This specifies that you want a new configuration whose type of scheduler is a job manager

  26. Creating and Modifying User Configurations • Enter a configuration name MyJMconfig1 and a description as shown in the following figure. In the Scheduler tab, enter the host name for the machine on which the job manager is running and the name of the job manager.

  27. Creating and Modifying User Configurations • In the Jobs tab, enter the maximum and minimum number of workers. This specifies that for jobs using this configuration, they require at least four workers and use no more than four workers. Therefore, the job runs on exactly four workers, even if it has to wait until four workers are available before starting.

  28. Creating and Modifying User Configurations • To create a similar configuration with just a few differences, you can duplicate an existing configuration and modify only the parts you need to change: • In the Configurations Manager, right-click the configuration MyJMconfig1 in the list and select Duplicate.

  29. Exporting and Importing Configurations • To export a parallel configuration: In the Configurations Manager, select (highlight) the configuration you want to export. • Click File > Export. • Configurations saved in this way can then be imported by other MATLAB software users: In the Configuration Manager, • click File > Import.

  30. Validate Configurations • Open the Configurations Manager by selecting on the desktop Parallel > Manage Configurations. • In the Configurations Manager, click the name of the configuration you want to test in the list of those available. • Click Start Validation.

  31. The configuration listing displays the overall validation result for each configuration. The following figure shows overall validation results for one configuration that passed and one that failed. The selected configuration is the one that failed.

  32. How to submit program for run? • Create a Scheduler Object • Create a Job • Create Tasks • Submit a Job to the Scheduler • Retrieve the Job's Results

  33. Creating Jobs • sched = findResource('scheduler','type','local'); (create an object in your local MATLAB session representing the local scheduler.) • job1 = createJob(sched) (This statement creates a job in the scheduler's data location) • get(job1) ( see all the properties of this job object) • Sched(The scheduler's display now indicates the existence of your job) • createTask(job1, @rand, 1, {{3,3} {3,3} {3,3} {3,3} {3,3}}); (Tasks define the functions to be evaluated by the workers during the running of the job) • get(job1,'Tasks') • submit(job1) (To run your job and have its tasks evaluated) • waitForState(job1) • results = getAllOutputArguments(job1); (to retrieve the results from all the tasks in the job) • sched • job1 • get(job1,'Tasks')

  34. Books

  35. Books(Cont.)

  36. Thanks for your attention Any Question?

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