Learn more about Parallel Computing Toolbox

Product Introduction

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Overview Toolbox and Server Components

Overview

Parallel Computing Toolbox and MATLAB Distributed Computing Server software let you solve computationally and data-intensive problems using MATLAB and Simulink on multicore and multiprocessor computers. Parallel processing constructs such as parallel for-loops and code blocks, distributed arrays, parallel numerical algorithms, and message-passing functions let you implement task-parallel and data-parallel algorithms at a high level in MATLAB without programming for specific hardware and network architectures.

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.

The MATLAB session in which the job and its tasks are defined is called the client session. Often, 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.

The job manager is 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. Use of the MathWorks™ job manager is optional; the distribution of tasks to workers can also be performed by a third-party scheduler, such as Microsoft^® Windows HPC Server (including CCS) or Platform LSF^® schedulers.

See the Glossary for definitions of the parallel computing terms used in this manual.

Basic Parallel Computing Configuration

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Toolbox and Server Components

• Job Managers, Workers, and Clients

• Local Scheduler

• Third-Party Schedulers

• Components on Mixed Platforms or Heterogeneous Clusters

• mdce Service

• Components Represented in the Client

Job Managers, Workers, and Clients

The job manager can be run on any machine on the network. The job manager runs jobs in the order in which they are submitted, unless any jobs in its queue are promoted, demoted, canceled, or destroyed.

Each worker is given a task from the running job by the job manager, executes the task, returns the result to the job manager, and then is given another task. When all tasks for a running job have been assigned to workers, the job manager starts running the next job with the next available worker.

A MATLAB Distributed Computing Server software setup usually includes many workers that can all execute tasks simultaneously, speeding up execution of large MATLAB jobs. It is generally not important which worker executes a specific task. The workers evaluate tasks one at a time, returning the results to the job manager. The job manager then returns the results of all the tasks in the job to the client session.

Note   For testing your application locally or other purposes, you can configure a single computer as client, worker, and job manager. You can also have more than one worker session or more than one job manager session on a machine.

Interactions of Parallel Computing Sessions

A large network might include several job managers as well as several client sessions. Any client session can create, run, and access jobs on any job manager, but a worker session is registered with and dedicated to only one job manager at a time. The following figure shows a configuration with multiple job managers.

Configuration with Multiple Clients and Job Managers

Local Scheduler

A feature of Parallel Computing Toolbox software is the ability to run a local scheduler and up to eight workers on the client machine, so that you can run distributed and parallel jobs without requiring a remote cluster or MATLAB Distributed Computing Server software. In this case, all the processing required for the client, scheduling, and task evaluation is performed on the same computer. This gives you the opportunity to develop, test, and debug your distributed or parallel application before running it on your cluster.

Third-Party Schedulers

As an alternative to using the MathWorks job manager, you can use a third-party scheduler. This could be a Microsoft Windows HPC Server (including CCS), Platform LSF scheduler, PBS Pro^® scheduler, TORQUE scheduler, mpiexec, or a generic scheduler.

Choosing Between a Third-Party Scheduler and Job Manager.   You should consider the following when deciding to use a scheduler or the MathWorks job manager for distributing your tasks:

• Does your cluster already have a scheduler?

  If you already have a scheduler, you may be required to use it as a means of controlling access to the cluster. Your existing scheduler might be just as easy to use as a job manager, so there might be no need for the extra administration involved.

• Is the handling of parallel computing jobs the only cluster scheduling management you need?

  The MathWorks job manager is designed specifically for MathWorks parallel computing applications. If other scheduling tasks are not needed, a third-party scheduler might not offer any advantages.

• Is there a file sharing configuration on your cluster already?

  The MathWorks job manager can handle all file and data sharing necessary for your parallel computing applications. This might be helpful in configurations where shared access is limited.

• Are you interested in batch mode or managed interactive processing?

  When you use a job manager, worker processes usually remain running at all times, dedicated to their job manager. With a third-party scheduler, workers are run as applications that are started for the evaluation of tasks, and stopped when their tasks are complete. If tasks are small or take little time, starting a worker for each one might involve too much overhead time.

• Are there security concerns?

  Your own scheduler may be configured to accommodate your particular security requirements.

• How many nodes are on your cluster?

  If you have a large cluster, you probably already have a scheduler. Consult your MathWorks representative if you have questions about cluster size and the job manager.

• Who administers your cluster?

  The person administering your cluster might have a preference for how jobs are scheduled.

• Do you need to monitor your job's progress or access intermediate data?

  A job run by the job manager supports events and callbacks, so that particular functions can run as each job and task progresses from one state to another.

Components on Mixed Platforms or Heterogeneous Clusters

Parallel Computing Toolbox software and MATLAB Distributed Computing Server software are supported on Windows^®, UNIX^®, and Macintosh^® operating systems. Mixed platforms are supported, so that the clients, job managers, and workers do not have to be on the same platform. The cluster can also be comprised of both 32-bit and 64-bit machines, so long as your data does not exceed the limitations posed by the 32-bit systems. Other limitations are described at http://www.mathworks.com/products/parallel-computing/requirements.html.

In a mixed-platform environment, system administrators should be sure to follow the proper installation instructions for the local machine on which you are installing the software.

mdce Service

If you are using the MathWorks job manager, every machine that hosts a worker or job manager session must also run the mdce service.

The mdce service controls the worker and job manager sessions and recovers them when their host machines crash. If a worker or job manager machine crashes, when the mdce service starts up again (usually configured to start at machine boot time), it automatically restarts the job manager and worker sessions to resume their sessions from before the system crash. These processes are covered more fully in the MATLAB Distributed Computing Server System Administrator's Guide.

Components Represented in the Client

A client session communicates with the job manager by calling methods and configuring properties of a job manager object. Though not often necessary, the client session can also access information about a worker session through a worker object.

When you create a job in the client session, the job actually exists in the job manager or in the scheduler's data location. The client session has access to the job through a job object. Likewise, tasks that you define for a job in the client session exist in the job manager or in the scheduler's data location, and you access them through task objects.

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Programming Overview

Using Parallel Computing Toolbox Software

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