# SW:Matlab

## Contents

- 1 Running Matlab interactively on login nodes
- 2 Running (parallel) Matlab Scripts on HPRC compute nodes
- 3 Using Matlab Parallel Toolbox on HPRC Resources

# Running Matlab interactively on login nodes

Matlab is accessible to all HPRC users within the terms of our license agreement. If you have particular concerns about whether specific usage falls within the TAMU HPRC license, please send an email to HPRC Helpdesk.

To be able to use matlab, the Matlab module needs to be loaded first. This can be done using the following command:

[ netID@cluster ~]$module load Matlab/R2017a

This will setup the environment for Matlab version R2017a. To see a list of all installed versions, use the following command:

[ netID@cluster ~]$module spider Matlab

**Note:** New versions of software become available periodically. Version numbers may change.

To start matlab, use the following command:

[ netID@cluster ~]$matlab

Depending on your X server settings, this will start either the Matlab GUI or the Matlab command line interface. To start Matlab in command line interface mode, use the following command with the appropriate flags:

[ netID@cluster ~]$matlab -nosplash -nodisplay

By default, Matlab will execute a large number of built-in operators and functions multi-threaded and will use as many threads (i.e. cores) as are available on the node. Since login nodes are shared among all users, HPRC restricts the number of computational threads to 8. This should suffice for most cases. Speedup achieved through multi-threading depends on many factors and in certain cases, it is possible that using 8 thread might negatively affect runtime.To explicitly change the number of computational threads, use the following Matlab command:

>>feature('NumThreads',4);

This will set the number of computational threads to 4.

To completely disable multi-threading, use the -singleCompThread option when starting Matlab:

[ netID@cluster ~]$matlab -singleCompThread

## Usage on the Login Nodes

Please limit interactive processing to short, non-intensive usage. Use non-interactive batch jobs for resource-intensive and/or multiple-core processing. Users are requested to be **responsible** and **courteous to other users** when using software on the login nodes.

The most important processing limits here are:

**ONE HOUR**of**PROCESSING TIME**per login session.**EIGHT CORES**per login session on the same node or (cumulatively) across all login nodes.

**Anyone found violating the processing limits will have their processes killed without warning. Repeated violation of these limits will result in account suspension.**

**Note:** Your login session will disconnect after **one hour** of inactivity.

# Running (parallel) Matlab Scripts on HPRC compute nodes

When your Matlab script needs more resources than are allowed during an interactive session (e.g. cpu time, number of cores) you are not allowed to run it on a login node. In this section, we will introduce a number of options to run your Matlab scripts on HPRC compute nodes.

## Run Matlab Scripts Remotely Using the HPRC Matlab App

HPRC developed an app to run your Matlab script directly to HPRC (ada and terra) compute nodes from your own laptop and/or desktop.

### Installing the App

You can download the app here. After downloading install it from within the Matlab GUI by double-clicking on the file (or right-click and select **install**). After installing, the app should show up in the **APPS** tab under the name *TAMU HPRC*.

### Using the App

You can start the HPRC app from the **APPS** tab. Alternatively, you can start the App by typing **HPRC** on the Matlab command line. The App window will popup and will look like:

- Click on the
**Browse**button to select the MATLAB script you want to run. It will open a file selection dialog. You can also type the name of the script directly.**NOTE:**the file does not have to be in the current directory. - Select the cluster where you want to run (terra or ada) and provide your username (should be your TAMU netid).
- In case your MATLAB script uses GPU commands/functions, check the
**Code Uses GPU**box - Click on the
**Attach Input Files**button to include any input files your script needs. It will open a file selection window where you can (multi) select files to include.

In addition to selecting the script name and the cluster/user there are many other options you can specify in the app (e.g. #workers in case you are running a parallel script, expected walltime needed, amount of memory needed, etc). For a detailed description of all the options, click here

To submit the script click on the **SUBMIT** button. A new window will popup that will show information about the job submission process.

**NOTE:** The first time you submit a script You will be asked to select a local directory where MATLAB will store Job information (a directory selection dialog will popup)

### Retrieve results

Once the script has been successfully submitted a variable named **myjob** of MATLAB type **Job** will be copied to the workspace. You can use this variable to retrieve information about the job and retrieve varialbes. For example:

**myjob.State**will show the current status of the job. This can be*queued*,*running*, or*finished***myjob.diary**will display all the redirected screen output from your Matlab run.**myjob.load**will load all the variables from your Matlab run into the current workspace

In addition, you can also get the Job information through the Parallel Job monitor (click **Parallel --> Monitor Jobs**). Use **TAMUREMOTE** cluster profile to see the corresponding jobs.

If you have any **save** commands in your script, the files will be saved in remote directory **/scratch/user/${USER}/MatlabJobs/WORKDIR/**. These files will not be copied back to your desktop/laptop.

### Considerations for Using the App

The app can be used to run any general serial and/or parallel script. It's especially recommended for serial/multithreaded runs, MATLAB scripts that use GPU code, need a long time to run and/or need a large amount of memory. For license considerations, the app is most suitable when either a limited number of workers are requested or more than 20 workers (on ada, 28 on terra). The reason for this is that every additional worker requires an additional MDCS license token. If you run your code directly on the cluster and all workers can be started on a single node only a single license token is required)

In addition, consider the following:

- input data (and script) needs to be transferred from the local host to the cluster. If data is large, it might take some time to transfer.
- scripts and input data will be stored in the local temp dir, which has limited capacity. If input data is too large it might not fit in the temp dir
- generated variables will be transferred back to the local host. If data is large it might take some to transfer

## Submit Matlab Scripts Remotely or Locally From the Matlab Command Line

Instead of using the App you can also call Matlab functions (developed by HPRC) directly to run your Matlab script on HPRC compute nodes. There are two steps involved in submitting your Matlab script:

- Define the properties for your Matlab script (e.g. #workers). HPRC created a class named
**TAMUClusterProperties**for this - Submit the Matlab script to run on HPRC compute nodes. HPRC created a function named
**tamu_run_batch**for this.

For example, suppose you have a script named *mysimulation.m*, you want to use 4 workers and estimate it will need less than 7 hours of computing time:

>> tp=TAMUClusterProperties(); >> tp.workers(4); >> tp.walltime('07:00'); >> myjob=tamu_run_batch(tp,'mysimulation.m');

**NOTE:** **TAMUClusterProperties** will use all default values for any of the properties that have not been set explicitly.

In case you want to submit your Matlab script remotely from your local Matlab GUI, you also have to specify the HPRC cluster name you want to run on and your username. For example, suppose you have a script that uses Matlab GPU functions and you want to run it on terra:

>> tp=TAMUClusterProperties(); >> tp.gpu(1); >> tp.hostname('terra.tamu.edu'); >> tp.user('<USERNAME>'); >> myjob=tamu_run_batch(tp,'mysimulation.m');

To see all available methods on objects of type **TAMUClusterProperties** you can use the Matlab **help** or **doc** functions: E.g.

>> help TAMUClusterProperties/doc

To see help page for **tamu_run_batch**, use:

>> help tamu_run_batch tamu_run_batch runs Matlab script on worker(s). j = TAMU_RUN_BATH(tp,'script') runs the script script.m on the worker(s) using the TAMUClusterProperties object tp. Returns j, a handle to the job object that runs the script.

**tamu_run_batch** returns a variable of type **Job**. See the section *"Retrieve results and information from Submitted Job"* how to get results and information from the submitted job.

## Submit Matlab Scripts Directly from HPRC Login Shell

HPRC developed a tool named **matlabsubmit** to run Matlab simulations on the HPRC compute nodes without the need to create your own batch script and without the need to start a Matlab session. **matlabsubmit** will automatically generate a batch script with the correct requirements. **matlabsubmit** will also generate boilerplate Matlab code to set up the environment (e.g. st number of computational threads) and if needed will start a *parpool* using the correct Cluster Profile (*local* if all workers fit on a single node and a *TAMU* cluster profile otherwise)

To submit your Matlab script, use the following command:

[ netID@cluster ~]$ matlabsubmit myscript.m

For parallel processing, Matlab uses Cluster profiles. A cluster profile acts as an interface between Matlab and the batch scheduler (e.g. LSF, SLURM) and lets you define certain properties of your cluster (e.g. how to submit jobs, submission parameters, job requirements, etc). Matlab will use the cluster profile to offload parallel (or sequential) Matlab code to one or more workers.

For your convenience, HPRC already created a custom Cluster Profile. You can use this profile to define how many workers you want, how you want to distribute the workers over the nodes Before you can use this profile you need to import it first (you only need to do this once). This can be done using by calling the following Matlab function.

When executing, matlabsubmit will do the following:

- generate boiler plate Matlab code to setup the matlab environment (e.g. #threads, #workers)
- generate a batch script with all resources set correctly and the command to run matlab
- submit the generated batch script to the batch scheduler and return control back to the user

To see all options for **matlabsubmit** type:

[ netID@cluster ~]$ matlabsubmit -h

### Example 1: basic use

The following example shows the simplest use of matlabsubmit. It will execute matlab script *test.m* using default values for batch resources and Matlab resources. matlabsubmit will also print some useful information to the screen. As can be seen in the example, it will show the Matlab resources requested (e.g. #threads, #workers), the submit command that will be used to submit the job, the batch scheduler JobID, and the location of output generated by Matlab and the batch scheduler.

-bash-4.1$ matlabsubmit test.m =============================================== Running Matlab script with following parameters ----------------------------------------------- Script : test.m Workers : 0 Nodes : 1 Mem/proc : 2500 #threads : 8 =============================================== bsub -e MatlabSubmitLOG1/lsf.err -o MatlabSubmitLOG1/lsf.out -L /bin/bash -n 8 -R span[ptile=8] -W 02:00 -M 2500 -R rusage[mem=2500] -J test1 MatlabSubmitLOG1/submission_script Verifying job submission parameters... Verifying project account... Account to charge: 082839397478For parallel processing, Matlab uses Cluster profiles. A cluster profile acts as an interface between Matlab and the batch scheduler (e.g. LSF, SLURM) and lets you define certain properties of your cluster (e.g. how to submit jobs, submission parameters, job requirements, etc). Matlab will use the cluster profile to offload parallel (or sequential) Matlab code to one or more workers. For your convenience, HPRC already created a custom Cluster Profile. You can use this profile to define how many workers you want, how you want to distribute the workers over the nodes Before you can use this profile you need to import it first (you only need to do this once). This can be done using by calling the following Matlab function.For parallel processing, Matlab uses Cluster profiles. A cluster profile acts as an interface between Matlab and the batch scheduler (e.g. LSF, SLURM) and lets you define certain properties of your cluster (e.g. how to submit jobs, submission parameters, job requirements, etc). Matlab will use the cluster profile to offload parallel (or sequential) Matlab code to one or more workers. For your convenience, HPRC already created a custom Cluster Profile. You can use this profile to define how many workers you want, how you want to distribute the workers over the nodes Before you can use this profile you need to import it first (you only need to do this once). This can be done using by calling the following Matlab function. Balance (SUs): 81535.6542 SUs to charge: 16.0000 Job <2847580> is submitted to default queue <sn_regular>. ----------------------------------------------- matlabsubmit ID : 1 matlab output file : MatlabSubmitLOG1/matlab.log LSF/matlab output file : MatlabSubmitLOG1/lsf.out LSF/matlab error file : MatlabSubmitLOG1/lsf.err -bash-4.1$

The matlab script *test.m* has to be in the current directory. Control will be returned immediately after executing the matlabsubmit command. To check the run status or kill a job, use the respective batch scheduler commands (e.g. **bjobs** and **bkill** on ada). matlabsubmit will create a sub directory named **MatlabSubmitLOG<N>** (where **N** is the matlabsubmit ID). In this directory matlabsubmit will store all its relevant files; the generated batch script, matlab driver, redirected output and error, and a copy of the workspace (after the job is done). A listing of this directory will show the following files:

**lsf.err**redirected error**lsf.out**redirected output (both LSF and Matlab)**matlab.log**redirected Matlab screen output**matlabsubmit_wrapper.m**Matlab code that sets #threads and calls user function**submission_script**the generated LSF batch script**workspace.mat**a copy of the matlab workspace (after execution has finished)

### Options with matlabsubmit

The example above showed the most simple case of using matlabsubmit. No options where specified and matlabsubmit used default values for requested resources. However, matlabsubmit provides a number of options to set batch resources (e.g. walltime, memory) as well as matlab related options (e.g. number of threads to use, number of workers, etc). To see all the available options you can use the "**-h**" option. See below for the output of "**matlabsubmit -h**":

-bash-4.1$ matlabsubmit -h /software/hprc/Matlab/bin/matlabsubmit: option requires an argument -- h Usage: /software/hprc/Matlab/bin/matlabsubmit [options] SCRIPTNAME This tools automates the process of running matlab codes on the compute nodes. OPTIONS: -h Shows this message -m set the amount of requested memory in MEGA bytes(e.g. -m 20000) -t sets the walltime; form hh:mm Cluster profiles let you define certain properties for your cluster(e.g. -t 03:27) -w sets the number of ADDITIONAL workers -g indicates script needs GPU (no value needed) -b sets the billing account to use -s set number of threads for multithreading (default: 8 ( 1 when -w > 0) -p set number of workers per node -f run function call instead of script -x add explicit batch scheduler option DEFAULT VALUES: memory : 2500 per core time : 02:00 workers : 0 gpu : no gpu threading: on, 8 threads We will discuss briefly some of the more common parallel matlab concepts. For more detailed information about these constructs, as well as additional parallel constructs consult the Parallel Computing Toolbox User Guide. -bash-4.1$

For example, the command matlabsubmit -t "03:27" -m 17000 -s 20 myscript.m will request 17gb of memory and 3 hours and 27 minutes of computing time. It will also set the number of computational threads in Matlab to 20 and execute the Matlab script myscript.m.

**NOTE** when using the **-f** flag to execute a function instead of a script, the function call must be enclosed with double quotes when it contains parentheses. For example: **matlabsubmit -f "myfunc(21)"**

### Example 2: Utilizing Matlab workers (single node)

To utilize additional workers used by Matlab's parallel features such as *parfor*,*spmd*, and *distributed* matlabsubmit provides the option to specify the number of workers. This is done using the *-w <N>* flag (where <N> represents the number of workers). The following example shows a simple case of using additional workers; in this case 8 workers

-bash-4.1$ matlabsubmit -w 8 test.m =============================================== Running Matlab script with following parameters ----------------------------------------------- Script : test.m Workers : 8 Nodes : 1 Mem/proc : 2500 #threads : 1 =============================================== bsub -e MatlabSubmitLOG5/lsf.err -o MatlabSubmitLOG5/lsf.out -L /bin/bash -n 9 -R span[ptile=9] -W 02:00 -M 2500 -R rusage[mem=2500] -J test5 MatlabSubmitLOG5/submission_script Verifying job submission parameters... Verifying project account... Account to charge: 082839397478 Balance (SUs): 80533.2098 SUs to charge: 18.0000 Job <2901543> is submitted to default queue <sn_regular>. ----------------------------------------------- matlabsubmit ID : 5 matlab output file : MatlabSubmitLOG5/matlab.log LSF/matlab output file : MatlabSubmitLOG5/lsf.out LSF/matlab error file : MatlabSubmitLOG5/lsf.err -bash-4.1$

In this example, matlabsubmit will first execute matlab code to create a *parpool* with 8 workers (using the local profile). As can be seen in the output, in this case, matlabsubmit requests 9 cores: 1 core for the client and 8 cores for the workers. The only exception is when the user requests 20 workers. In that case, matlabsubmit will request 20 cores.

### Example 3: Utilizing Matlab workers (multi node)

matlabsubmit provides excellent options for Matlab runs that need more than 20 workers (maximum for single node) and/or when the Matlab workers need to be distributed among multiple nodes. Reasons for distributing workers among different nodes include: need to use certain resources such as gpu on multiple nodes, enable multi threading on every worker, and use the available memory on multiple nodes. The following example shows how to run a matlab simulation that utilizes 24 workers, where every node will run 4 workers (i.e. the workers will be distributed among 24/4 = 6 nodes).

-bash-4.1$ matlabsubmit -w 24 -p 4 test.m =============================================== Running Matlab script with following parameters ----------------------------------------------- Script : test.m Workers : 24 Nodes : 6 Mem/proc : 2500 #threads : 1 =============================================== ... starting matlab batch. This might take some time. See MatlabSubmitLOG8/matlab-batch-commands.log ...Starting Matlab from host: login4 MATLAB is selecting SOFTWARE OPENGL rendering. < M A T L A B (R) > Copyright 1984-2016 The MathWorks, Inc. R2016a (9.0.0.341360) 64-bit (glnxa64) February 11, 2016 To get started, type one of these: helpwin, helpdesk, or demo. For product information, visit www.mathworks.com. ... Interactive Matlab session, multi threading reduced to 4 Academic License commandToRun = bsub -L /bin/bash -J Job1 -o '/general/home/pennings/Job1/Job1.log' -n 25 -M 2500 -R rusage[mem=2500] -R "span[ptile=4]" -W 02:00 "source /general/home/pennings/Job1/mdce_envvars ; /general/software/x86_64/tamusc/Matlab/toolbox/tamu/profiles/lsfgeneric/communicatingJobWrapper.sh" job = Job Properties: ID: 1 Type: pool Username: pennings State: running SubmitTime: Mon Aug 01 12:15:15 CDT 2016 StartTime: Running Duration: 0 days 0h 0m 0s NumWorkersRange: [25 25] AutoAttachFiles: true Auto Attached Files: /general/home/pennings/MatlabSubmitLOG8/matlabsubmit_wrapper.m /general/home/pennings/test.m AttachedFiles: {} AdditionalPaths: {} Associated Tasks: Number Pending: 25 Number Running: 0 Number Finished: 0 Task ID of Errors: [] Task ID of Warnings: [] ----------------------------------------------- matlabsubmit JOBID : 8 batch output file (client) : Job1/Task1.diary.txt batch output files (workers) : Job1/Task[2-25].diary.txt Done -bash-4.1$

As can be seen the output is very different from the previous examples. When a job uses multiple nodes the approach matlabsubmit uses is a bit different. matlabsubmit will start a regular *interactive* matlab session and from within it will run the Matlab *batch* command using the **TAMUG** cluster profile. It will then exit Matlab while the Matlab script is executed on the compute nodes.

The contents of the MatlabSubmitLOG directory are also slightly different. A listing will show the following files:

**matlab-batch-commands.log**screen output from Matlab**matlabsubmit_driver.m**Matlab code that sets up the cluster profile and calls Matlab*batch***matlabsubmit_wrapper.m**Matlab code that sets #threads and calls user function**submission_script**The actual command to start Matlab

In addition to the MatlabSubmitLOG directory created by matlabsubmit, Matlab will also create a directory named **Job<N>** used by the cluster profile to store meta data, log files, and screen output. The ***.diary.txt** text files will show screen output for the client and all the workers.

# Using Matlab Parallel Toolbox on HPRC Resources

*THIS SECTION IS UNDER CONSTRUCTION*

In this section, we will discuss some common concepts from the Matlab Parallel Toolbox and the convenience functions HPRC created to utilize the Parallel toolbox. We will give a brief introduction into Matlab Cluster profiles, parallel pools, the parallel constructs *parfor* and *spmd* , and how to utilize GPUs using Matlab.

The central concept in most of the convenience functions is the **TAMUClusterProperties** class introduced in the *Submit Matlab Scripts Remotely or Locally From the Matlab Command Line* section above.

## Cluster Profiles

Cluster profiles define properties on where and how you want to do the parallel processing. There are two kinds of profiles.

- local profiles: parallel processing is limited to the same node the Matlab client is running.
- cluster profiles: parallel processing can span multiple nodes; profile interacts with batch scheduler (e.g. LSF on ada, SLURM on terra).

**NOTE:** we will not discuss *local profiles* any further here. Processing using a local profile is exactly the same as processing using cluster profiles.

### Importing Cluster Profile

For your convenience, HPRC already created a custom Cluster Profile. You can use this profile to define how many workers you want, how you want to distribute the workers over the nodes Before you can use this profile you need to import it first. This can be done using by calling the following Matlab function.

>>tamu_import_TAMU_clusterprofile()

This function imports the cluster profile and it creates a directory structure in your scratch directory where Matlab will store meta information during parallel processing. The default location is */scratch/$USER/MatlabJobs/TAMU* ( */scratch/$USER/MatlabJobs/TAMUREMOTE* for remote jobs)

**NOTE:** convenience function **tamu_import_TAMU_clusterprofile** is a wrapper around the Matlab function
parallel.importprofile

You only need to import the cluster profile once. However, the imported profile is just a skeleton. It doesn't contain information how many resources (e.g. #workers) you want to use for parallel processing. In the next section, we will discuss how to create a fully populated cluster object that can be used for parallel processing.

For more information about **tamu_import_TAMU_clusterprofile()** you can use the Matlab *help// and *doc* functions.*

### Retrieving fully populated Cluster Profile Object

To return a fully completed cluster object (i.e. with attached resource information) HPRC created the **tamu_set_profile_properties** convenience function. There are two steps to follow:

- define the properties using the TAMUClusterProperties class
- call
**tamu_set_profile_properties**using the created TAMUClusterProperties object.

For example, suppose you have Matlab code and want to use 4 workers for parallel processing.

>> tp=TAMUClusterProperties; >> tp.workers(4); >> clusterObject=tamu_set_profile_properties(tp);

Variable *clusterObject* is a fully populated cluster object that can be used for parallel processing.

**NOTE:** convenience function **tamu_set_profile_properties** is a wrapper around Matlab function
parcluster. It also uses HPRC convenience function **tamu_import_TAMU_clusterprofile** to check if the **TAMU** profile has been imported already.

## Starting a Parallel Pool

To start a parallel pool you can use the HPRC convenience function **tamu_parpool**. It takes as argument a **TAMUClustrerProperties** object that specifies all the resources that are requested.

The **parpool** functions enables the full functionality of the parallel language features (parfor and spmd, will be discussed below). A parpool creates a special job on a pool of workers, and connects the pool to the MATLAB client. For example:

mypool = parpool 4 : delete(mypool)

This code starts a worker pool using the default cluster profile, with 4 additional workers.

NOTE: only instructions within parfor and spmd blocks are executed on the workers. All other instructions are executed on the client.

NOTE: all variables declared inside the matlabpool block will be destroyed once the block is finished.

## Common Parallel constructs

### parfor

The concept of a parfor-loop is similar to the standard Matlab for-loop. The difference is that parfor partitions the iterations among the available workers to run in parallel. For example:

parfor i=1:1024 A(i)=sin((i/1024)*2*pi); end

This code will open a parallel pool with 2 workers using the default cluster profile and execute the loop in parallel.

For more information please visit the Matlab parfor page.

### spmd

spmd runs the same program on all workers concurrently. A typical use of spmd is when you need to run the same program on multiple sets of input. For example, Suppose you have 4 inputs named data1,data2,data3,data4 and you want run function myfun on all of them:

spmd (4) data = load(['data' num2str(labindex)]) myresult = myfun(data) end

NOTE: labindex is a Matlab variable and is set to the worker id, values range from 1 to number of workers.

Every worker will have its own version of variable myresult. To access these variables outside the spmd block you append {i} to the variable name, e.g. myresult{3} represents variable myresult from worker 3.

For more information please visit the Matlab spmd page.

## Using GPU

Normally all variables reside in the client workspace and matlab operations are executed on the client machine. However, Matlab also provides options to utilize available GPUs to run code faster. Running code on the gpu is actually very straightforward. Matlab provides GPU versions for many build-in operations. These operations are executed on the GPU automatically when the variables involved reside on the GPU. The results of these operations will also reside on the GPU. To see what functions can be run on the GPU type:

methods('gpuArray') This will show a list of all available functions that can be run on the GPU, as well as a list of available static functions to create data on the GPU directly (will be discussed later).

NOTE: There is significant overhead of executing code on the gpu because of memory transfers.

Another useful function is: gpuDevice This functions shows all the properties of the GPU. When this function is called from the client (or a node without a GPU) it will just print an error message.

To copy variables from the client workspace to the GPU, you can use the gpuArray command. For example:

carr = ones(1000); garr = gpuArray(carr);

will copy variable carr to the GPU wit name garr.

In the example above the 1000x1000 matrix needs to be copied from the client workspace to the GPU. There is a significant overhead involved in doing this.

To create the variables directly on the GPU, Matlab provides a number of convenience functions. For example:

garr=gpuArray.ones(1000)

This will create a 1000x1000 matrix directly on the GPU consisting of all ones.

To copy data back to the client workspace Matlab provides the gather operation.

carr2 = gather(garr)

This will copy the array garr on the GPU back to variable carr2 in the client workspace.

The next example performs a matrix multiplication on the client, a matrix multiplication on the GPU, and prints out elapsed times for both. The actual cpu-gpu matrix multiplication code can be written as:

ag = gpuArray.rand(1000); bg = ag*ag; c = gather(bg);