# Difference between revisions of "Using the MATLAB service"

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Quick setup: | Quick setup: | ||

− | # Install MATLAB version | + | # Install MATLAB version 9.3 (R2017b) on your workstation. |

# (optional but recommended) Open your firewall from [[Cluster_IP_ranges|10.205.0.0/19 and 10.205.96.0/19]] to ports 27370–27470 on your workstation. | # (optional but recommended) Open your firewall from [[Cluster_IP_ranges|10.205.0.0/19 and 10.205.96.0/19]] to ports 27370–27470 on your workstation. | ||

− | # Unpack the [https:// | + | # Unpack the [https://scicomp.ethz.ch/public/config/MDCS/Euler_MDCS_012.zip MATLAB interface files for Euler] into <tt>Documents\MATLAB</tt> (Windows) or <tt>~/Documents/MATLAB</tt> (Linux, Mac). |

− | # | + | # Import the <tt>Euler_R2017b_9.3.settings</tt> cluster profile into MATLAB. |

# [[Accessing_the_clusters|Log in to Euler]] to accept the usage agreement (if you are not already an Euler user) | # [[Accessing_the_clusters|Log in to Euler]] to accept the usage agreement (if you are not already an Euler user) | ||

− | # | + | # Validate the Euler cluster profile. |

− | + | Did you encounter problems? [[Setting_up_the_MDCS|The full setup instructions and Troubleshooting are on a separate page.]] | |

== Usage == | == Usage == | ||

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=== Setting job time limits === | === Setting job time limits === | ||

− | The default settings of how long a job can run (24 hours) or how much memory it needs (2500 MB/core) should suffice for most cases. You can specify other values for how long the jobs can run or how much memory it needs. Set the global calculusTimeLimit variable to the maximum allowable time (in | + | The default settings of how long a job can run (24 hours) or how much memory it needs (2500 MB/core) should suffice for most cases. You can specify other values for how long the jobs can run or how much memory it needs. Set the global calculusTimeLimit variable to the maximum allowable time (in seconds) a job can run: |

global calculusTimeLimit; calculusTimeLimit=60 | global calculusTimeLimit; calculusTimeLimit=60 | ||

Set the global calculusMemory variable to the maximum memory (RAM) a worker will use (in MB/core): | Set the global calculusMemory variable to the maximum memory (RAM) a worker will use (in MB/core): | ||

global calculusMemory; calculusMemory=2000 | global calculusMemory; calculusMemory=2000 | ||

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## Latest revision as of 11:47, 3 July 2019

## Contents

## Introduction

The MATLAB Distributed Computing Server (MDCS) is a service for offloading computationally-intensive calculations from your workstation to the Euler computer cluster as transparently as possible.

The most common use case is offloading a computationally-intensive `parfor` loop, which takes more than several hours to run on a normal workstation.

## Setup

Quick setup:

- Install MATLAB version 9.3 (R2017b) on your workstation.
- (optional but recommended) Open your firewall from 10.205.0.0/19 and 10.205.96.0/19 to ports 27370–27470 on your workstation.
- Unpack the MATLAB interface files for Euler into
`Documents\MATLAB`(Windows) or`~/Documents/MATLAB`(Linux, Mac). - Import the
`Euler_R2017b_9.3.settings`cluster profile into MATLAB. - Log in to Euler to accept the usage agreement (if you are not already an Euler user)
- Validate the Euler cluster profile.

Did you encounter problems? The full setup instructions and Troubleshooting are on a separate page.

## Usage

Refer to Mathwork's Parallel Computing Toolbox (PCT) documentation on how to make use of Euler in your code.

### The `batch()` function

The `batch()` function runs a script or function on Calculus. Its use is pretty straightforward:

cluster = parcluster('Euler'); job = batch(cluster, 'my_script'); wait(job); diary(job); delete(job);

For a simple function, such as sin, returning a variable and with a single argument:

job = batch(cluster,@sin,1,{0.5}); job.wait(); results=job.fetchOutputs(); ans=results{1}; delete(job);

### The `submit()` function

The `submit()` function submits a job or several jobs to Calculus. You have to prepare tasks, add them to a job, then submit the job. A useful example is if you have a function with an argument that needs to be evaluated for many different values:

cluster = parcluster('Euler'); job = createJob(cluster); for i = 1:10 createTask(job,@(x)x^2,1,{i}); end submit(job); wait(job); squares = job.fetchOutputs(); delete(job); squares

### The `parfor` statement and `parpool`

Code that uses `parfor` or other PCT constructs can use the Euler MDCS by referring to the Euler cluster profile. For example,

cluster = parcluster('Euler'); squares = zeros(10,1); parpool(cluster,4); parfor i = 1:10 squares(i) = i^2; end disp(squares)

If you set Euler to be the default cluster profile, then you need no changes to your code: all of the PCT constructs will use Euler by default.

### Setting job time limits

The default settings of how long a job can run (24 hours) or how much memory it needs (2500 MB/core) should suffice for most cases. You can specify other values for how long the jobs can run or how much memory it needs. Set the global calculusTimeLimit variable to the maximum allowable time (in seconds) a job can run:

global calculusTimeLimit; calculusTimeLimit=60

Set the global calculusMemory variable to the maximum memory (RAM) a worker will use (in MB/core):

global calculusMemory; calculusMemory=2000