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== Parallel GRASS jobs ==
== Parallel GRASS jobs ==


NOTE: GRASS 6 libraries are NOT thread safe.
The idea of parallel GRASS GIS jobs is to speed up the computation.


=== OpenMosix ===
=== Background ===


If you want to launch several GRASS jobs in parallel, you have to launch each job in its own mapset. Be sure to indicate the mapset correctly in the GISRC file (see above). You can use the process ID (PID, get with $$ or use PBS jobname) to generate a almost unique number which you can add to the mapset name.
This presentation from 2022 is a "must see":


'''[https://htmlpreview.github.io/?https://github.com/petrasovaa/FUTURES-CONUS-talk/blob/main/foss4g2022.html Tips for parallelization in GRASS GIS]'''


Now you could launch the jobs on an [http://openmosix.sourceforge.net/ openMosix cluster] (just install openMosix on your colleague's computers...).
This you should know about GRASS' behaviour concerning multiple jobs:
* You can run '''multiple processes''' in '''multiple locations''' ([https://grass.osgeo.org/grass-stable/manuals/helptext.html what's that?]). ''Peaceful coexistence.''
* You can run multiple processes in the same mapset, but only if the region is untouched. If you are unsure, it's recommended to  launch each job in its own mapset within the location.
* You can run '''multiple processes''' in the '''same location''', but in '''different mapsets'''. ''Peaceful coexistence.''


=== PBS ===
=== Approaches ===


You need essentially two scripts:
See also the [[Parallelizing Scripts]] wiki page


* GRASS job script (which takes the name(s) of map(s) to elaborate from environmental variables
== File locking ==
* script to launch this GRASS-script as job for each map to elaborate


Steps (for multiple serial jobs on many CPUs):
GRASS doesn't perform any locking on the files within a GRASS
database, so the user may end up with one process reading a file while
another process is in the middle of writing it. The most problematic
case is the WIND file, which contains the [[current region]], although
there are others.


* Job definition
If a user wants to run multiple commands concurrently, steps need to
** PBS setup (in the header): define calculation time, number of nodes, number of processors, amount of RAM for individual job;
be taken to ensure that this type of conflict doesn't happen. For the
** data are stored in centralized directory which is seen by all nodes;
current region, the user can use the WIND_OVERRIDE environment variable to
* Job execution (launch of jobs)
specify a named region which should be used instead of the WIND file.
** user launches all jobs ("qsub"), they are submitted to the queue. Use the [[GRASS and Shell#GRASS_Batch_jobs|GRASS_BATCH_JOB]] variable to define the name of the elaboration script.
 
** the scheduler optimizes among all user the execution of the jobs according to available resources and requested resources;
Or the user can use the GRASS_REGION environment variable to specify the
** for the user this means that 0..max jobs are executed in parallel (unless the administrators didn't define either priority or limits). The user can then observe the job queue ("showq") to see other jobs ahead and scheduling of own jobs. Once a job is running, the cluster possibly sends a notification email to the user, the same again when a job is terminating.
region parameters (the syntax is the same as the WIND file, but with
* Job planning
newlines replaced with semicolons). With this approach, the region can
** The challenging part for the user is to estimate the execution time since PBS kills jobs which exceed the requested time. The same applies to the request for number of nodes and CPUs per node as well as the amount of needed RAM. Usually tests are needed to see the performance.
only be read, not modified.
 
Problems can also arise if the user reads files from another mapset while
another session is modifying those files. The WIND file isn't an issue
here, nor are the files containing raster data (which are updated
atomically), but the various support files may be.
 
See below for ways around these limitations.
 
== Working with tiles ==
 
Huge map reprojection example:
 
'''Q:''' I'd like to try splitting a large raster into small chunks and then projecting each one separately, sending the project command to the background. The problem is that, if the GRASS command changes the region settings, things might not work.
 
'''A:''' {{cmd|r.proj}} doesn't change the region.
 
Processing the map in chunks requires setting a different region for
each command. That can be done by creating named regions and using the
WIND_OVERRIDE environment variable, e.g.:
 
<source lang="bash">
      g.region ... save=region1
      g.region ... save=region2
      ...
      WIND_OVERRIDE=region1 r.proj ... &
      WIND_OVERRIDE=region2 r.proj ... &
      ...
</source>
 
(for python see the grass.use_temp_region() function)
 
The main factor which is likely to affect parallelism is the fact that the processes won't share their caches, so there'll be some degree of inefficiency if there's substantial overlap between the source areas for the processes.
 
If you have more than one such map to project, processing entire maps in parallel might be a better choice (so that you get N maps projected in 10 hours rather than 1 map in 10/N hours).
 
== Parallelized code ==
 
=== OpenMP ===
 
Good for a single system with a multi-core CPU.
 
Configure GRASS 7 with:
./configure --with-openmp
 
==== GPDE using OpenMP ====
 
The only parallelized library in GRASS >=6.3 is GRASS Partial Differential Equations Library (GPDE) and the gmath library in GRASS 7. Read more in [[OpenMP]].
 
=== Python ===
 
[http://grass.osgeo.org/grass73/manuals/libpython/pygrass.modules.interface.html?highlight=parallelmodulequeue#pygrass.modules.interface.module.ParallelModuleQueue PyGRASS ParallelModuleQueue]
 
=== pthreads ===
 
Note: only used in the r.mapcalc ''parser''!
 
Good for a single system with a multi-core CPU.
 
Configure GRASS 7 with:
./configure --with-pthread
 
The ''parser'' of {{cmd|r.mapcalc}} in GRASS 7 has been parallelized using GNU {{wikipedia|pthreads}}. The computation itself is executed serially.
 
=== Bourne and Python Scripts ===
 
Good for a single system with a multi-core CPU.
 
Often very easy & can be done without modification to the main source code.
 
* See the [[Parallelizing Scripts]] wiki page
 
=== OpenMPI ===
 
Good for a multi-system cluster connected by a fast network.
 
The {{AddonCmd|GIPE}} ''i.vi.mpi'' addon module has been created as a MPI ({{wikipedia|Message Passing Interface}}) implementation of the {{AddonCmd|GIPE}} ''i.vi'' addon module.
* See also the [[Agriculture and HPC]] wiki page.
 
=== MPI Programming ===
 
There is a sample implementation at module level in [https://github.com/OSGeo/grass-addons/tree/grass8/src/imagery/i.vi.mpi i.vi.mpi]
 
=== GPU Programming ===
 
Good for certain kinds of calculations (e.g. ray-tracing) on a single system with a fast graphics card.
 
There is a version of the {{cmd|r.sun}} module which has been modified to use {{wikipedia|OpenCL}}. (works; still experimental)
 
* See [[GPU]]
 
Configure GRASS GIS with:
  ./configure --with-opencl
 
== Cluster and Grid computing ==
 
A cluster or grid computing system  consists of a number of computers that are tightly coupled together. The manager or master controls the utilization of compute nodes.
 
=== Job scheduler ===
 
Common job schedulers are [https://slurm.schedmd.com/ SLURM], [http://www.adaptivecomputing.com/products/open-source/torque/ TORQUE], [http://www.pbspro.org/ PBS], [https://arc.liv.ac.uk/trac/SGE Son of Grid Engine], and [https://kubernetes.io/ kubernetes]
 
See also [https://slurm.schedmd.com/rosetta.html Rosetta Stone of Workload Managers]
 
A job consists of tasks, e.g. processing of a single raster map in a time series of many raster maps. Jobs are assigned to a queue and started as soon as a slot in the queue is free. Jobs are removed from the queue once they finished.
 
=== GRASS on a cluster ===
 
If you want to launch several GRASS jobs in parallel, you might consider to launch each job in its own mapset.
 
* set up chunks of data to be processed (temporal or spatial, temporal chunks are usually easier to handle)
* write a script with the actual processing of one chunk
* write a script that initializes GRASS, creates a unique mapset, executes the script with the actual processing, and copies the results to a common mapset
* add that script as a task to a job, create one job for each data chunk
 
The general concept is to create a script that
 
# creates a unique temporary mapset
# creates a unique temporary GISRC file for this mapset
# does the processing in this mapset
# changes to the target mapset by updating the GISRC file, verify with g.gisenv
# copies results from the temporary mapset to the target mapset
# deletes the temporary mapset and GISRC file
 
Such a script should not call grassXY and must be executable outside GRASS, because it is establishing a GRASS session, performing some processing, and closing the GRASS session, all by itself. See also [[GRASS_and_Shell|GRASS and Shell]].
 
Such a script will take arguments to specify the particular data to be processed.
 
A job specification for an HPC job scheduler would then contain this script with specific arguments.
 
The common bottleneck when using GRASS on a cluster is often disk I/O. Try to start the jobs with nice/ionice to reduce strain on the storage devices.
 
== Cloud computing ==
 
GRASS GIS is running in the cloud as web processing service backend. Have a look at:
 
{{YouTube|jg2pb_Xjq8Y|desc=GRASS 7 in the cloud (by Sören Gebbert)}}
 
This Open Cloud GIS has been set up in a private Amazon compatible cloud environment using:
* Ubuntu 10.04 LTS and 10.10 cloud server edition
* Eucalyptus Cloud
* GRASS GIS 7 latest svn
* PyWPS latest svn
* wps-grass-bridge latest svn
* QGIS 1.7 with a modified QWPS plugin
 
For latest development, visit: https://github.com/actinia-org/actinia-core
 
== GRASS GIS on VPS ==
 
Instructions to run GRASS GIS on a commercial VPS to do some memory-intensive operations:
 
https://plantarum.ca/2014/08/19/medium-performance-cluster-computing/
 
== Hints for NFS users ==
* AVOID script forking on the cluster (but inclusion via ". script.sh" works ok). This means that the GRASS_BATCH_JOB approach is prone to '''fail'''. It is highly recommended to simply set a series of environmental variables to define the GRASS session, see [[GRASS_and_Shell#Automated_batch_jobs:_Setting_the_GRASS_environmental_variables|here]] how to do that.
* be careful with concurrent file writing (use "lockfile" locking, the lockfile command is provided by [http://www.procmail.org/ procmail]);
* store as much temporary data as possible (even the final maps) on the local blade disks if you have.
* finally collect all results from local blade disks *after* the parallel job execution in a sequential collector job (I am writing that now) to not kill NFS. For example, Grid Engine offers a "hold" function to only execute the collector job after having done the rest.
* If all else fails, and the I/O load is not too great, consider using {{wikipedia|sshfs}} with ssh passkeys instead of NFS.
* In some situations it is necessary to preserve the same directory structure on all nodes, and symlinks are a nice way to do that, but some (closed source 3rd party which will remain nameless) software insists on expanding symlinks. In this situation the [http://code.google.com/p/bindfs/ bindfs] FUSE extension can help. It is safer to use than "mount" binds, and you don't have to be root to set them up. As with ''sshfs'' there is a performance penalty so it may not be appropriate in high I/O situations.
 
== Error: Too many open files ==
 
When working with long time series and {{cmd|r.series}} starts to complain that files are missing/not readable or the message
Too many open files
 
For a solution, see [[Large_raster_data_processing#Number_of_open_files_limitation]]
 
== Misc Tips & Tricks ==
See the poor-man's multi-processing script on the [[Parallelizing Scripts]] wiki page. This approach has been used in the {{cmd|r3.in.xyz}} script.
 
== Workshop on Parallelization ==
 
Haedrich, C., Petrasova, A. Parallelization for big EO data processing, OpenGeoHub Summer School 2023
* https://doi.org/10.5446/63123
 
'''Schedule'''
 
Session 1
* [Slides] Introduction to Parallelization, GRASS GIS and Python
* [Lab] Introduction to Parallelization with GRASS GIS and Python Notebook
 
Session 2
* [Lab] Parallelization Case Study: Urban Growth Modeling
 
Jupyter Notebook:
* https://github.com/ncsu-geoforall-lab/opengeohub-2023
** https://github.com/ncsu-geoforall-lab/opengeohub-2023/blob/main/01_intro_to_GRASS_parallelization.ipynb
 
== See also ==
 
This Wiki:
* [[GRASS_and_Shell#Automated_batch_jobs:_Setting_the_GRASS_environmental_variables|GRASS batch jobs]] (by settings env. variables)
* The [[OpenMP]] wiki page.
* The [[Parallelizing Scripts]] wiki page.
* [[GPU]] computing
 
Elsewhere:
* [https://neteler.org/blog/building-a-cluster-for-grass-gis-and-other-software-from-the-osgeo-stack/ Building a cluster for GRASS GIS and other software from the OSGeo stack]
* [https://research.csc.fi/geocomputing Using supercomputers for spatial analysis]
* [https://github.com/csc-training/geocomputing/tree/master/grass GRASS batch job and parallelization examples for a supercomputer with SLURM]
 
[[Category:Parallelization]]
[[Category: massive data analysis]]

Latest revision as of 19:43, 27 December 2023

Parallel GRASS jobs

The idea of parallel GRASS GIS jobs is to speed up the computation.

Background

This presentation from 2022 is a "must see":

Tips for parallelization in GRASS GIS

This you should know about GRASS' behaviour concerning multiple jobs:

  • You can run multiple processes in multiple locations (what's that?). Peaceful coexistence.
  • You can run multiple processes in the same mapset, but only if the region is untouched. If you are unsure, it's recommended to launch each job in its own mapset within the location.
  • You can run multiple processes in the same location, but in different mapsets. Peaceful coexistence.

Approaches

See also the Parallelizing Scripts wiki page

File locking

GRASS doesn't perform any locking on the files within a GRASS database, so the user may end up with one process reading a file while another process is in the middle of writing it. The most problematic case is the WIND file, which contains the current region, although there are others.

If a user wants to run multiple commands concurrently, steps need to be taken to ensure that this type of conflict doesn't happen. For the current region, the user can use the WIND_OVERRIDE environment variable to specify a named region which should be used instead of the WIND file.

Or the user can use the GRASS_REGION environment variable to specify the region parameters (the syntax is the same as the WIND file, but with newlines replaced with semicolons). With this approach, the region can only be read, not modified.

Problems can also arise if the user reads files from another mapset while another session is modifying those files. The WIND file isn't an issue here, nor are the files containing raster data (which are updated atomically), but the various support files may be.

See below for ways around these limitations.

Working with tiles

Huge map reprojection example:

Q: I'd like to try splitting a large raster into small chunks and then projecting each one separately, sending the project command to the background. The problem is that, if the GRASS command changes the region settings, things might not work.

A: r.proj doesn't change the region.

Processing the map in chunks requires setting a different region for each command. That can be done by creating named regions and using the WIND_OVERRIDE environment variable, e.g.: 

       g.region ... save=region1
       g.region ... save=region2
       ...
       WIND_OVERRIDE=region1 r.proj ... &
       WIND_OVERRIDE=region2 r.proj ... &
       ...

(for python see the grass.use_temp_region() function)

The main factor which is likely to affect parallelism is the fact that the processes won't share their caches, so there'll be some degree of inefficiency if there's substantial overlap between the source areas for the processes.

If you have more than one such map to project, processing entire maps in parallel might be a better choice (so that you get N maps projected in 10 hours rather than 1 map in 10/N hours).

Parallelized code

OpenMP

Good for a single system with a multi-core CPU.

Configure GRASS 7 with: 

./configure --with-openmp

GPDE using OpenMP

The only parallelized library in GRASS >=6.3 is GRASS Partial Differential Equations Library (GPDE) and the gmath library in GRASS 7. Read more in OpenMP.

Python

PyGRASS ParallelModuleQueue

pthreads

Note: only used in the r.mapcalc parser!

Good for a single system with a multi-core CPU.

Configure GRASS 7 with: 

./configure --with-pthread

The parser of r.mapcalc in GRASS 7 has been parallelized using GNU pthreads. The computation itself is executed serially.

Bourne and Python Scripts

Good for a single system with a multi-core CPU.

Often very easy & can be done without modification to the main source code.

OpenMPI

Good for a multi-system cluster connected by a fast network.

The GIPE i.vi.mpi addon module has been created as a MPI (Message Passing Interface) implementation of the GIPE i.vi addon module.

MPI Programming

There is a sample implementation at module level in i.vi.mpi

GPU Programming

Good for certain kinds of calculations (e.g. ray-tracing) on a single system with a fast graphics card.

There is a version of the r.sun module which has been modified to use OpenCL. (works; still experimental)

Configure GRASS GIS with: 

 ./configure --with-opencl

Cluster and Grid computing

A cluster or grid computing system consists of a number of computers that are tightly coupled together. The manager or master controls the utilization of compute nodes.

Job scheduler

Common job schedulers are SLURM, TORQUE, PBS, Son of Grid Engine, and kubernetes

See also Rosetta Stone of Workload Managers

A job consists of tasks, e.g. processing of a single raster map in a time series of many raster maps. Jobs are assigned to a queue and started as soon as a slot in the queue is free. Jobs are removed from the queue once they finished.

GRASS on a cluster

If you want to launch several GRASS jobs in parallel, you might consider to launch each job in its own mapset.

  • set up chunks of data to be processed (temporal or spatial, temporal chunks are usually easier to handle)
  • write a script with the actual processing of one chunk
  • write a script that initializes GRASS, creates a unique mapset, executes the script with the actual processing, and copies the results to a common mapset
  • add that script as a task to a job, create one job for each data chunk

The general concept is to create a script that

  1. creates a unique temporary mapset
  2. creates a unique temporary GISRC file for this mapset
  3. does the processing in this mapset
  4. changes to the target mapset by updating the GISRC file, verify with g.gisenv
  5. copies results from the temporary mapset to the target mapset
  6. deletes the temporary mapset and GISRC file

Such a script should not call grassXY and must be executable outside GRASS, because it is establishing a GRASS session, performing some processing, and closing the GRASS session, all by itself. See also GRASS and Shell.

Such a script will take arguments to specify the particular data to be processed.

A job specification for an HPC job scheduler would then contain this script with specific arguments.

The common bottleneck when using GRASS on a cluster is often disk I/O. Try to start the jobs with nice/ionice to reduce strain on the storage devices.

Cloud computing

GRASS GIS is running in the cloud as web processing service backend. Have a look at:


Load video
YouTube
GRASS 7 in the cloud (by Sören Gebbert)

This Open Cloud GIS has been set up in a private Amazon compatible cloud environment using:

  • Ubuntu 10.04 LTS and 10.10 cloud server edition
  • Eucalyptus Cloud
  • GRASS GIS 7 latest svn
  • PyWPS latest svn
  • wps-grass-bridge latest svn
  • QGIS 1.7 with a modified QWPS plugin

For latest development, visit: https://github.com/actinia-org/actinia-core

GRASS GIS on VPS

Instructions to run GRASS GIS on a commercial VPS to do some memory-intensive operations:

https://plantarum.ca/2014/08/19/medium-performance-cluster-computing/

Hints for NFS users

  • AVOID script forking on the cluster (but inclusion via ". script.sh" works ok). This means that the GRASS_BATCH_JOB approach is prone to fail. It is highly recommended to simply set a series of environmental variables to define the GRASS session, see here how to do that.
  • be careful with concurrent file writing (use "lockfile" locking, the lockfile command is provided by procmail);
  • store as much temporary data as possible (even the final maps) on the local blade disks if you have.
  • finally collect all results from local blade disks *after* the parallel job execution in a sequential collector job (I am writing that now) to not kill NFS. For example, Grid Engine offers a "hold" function to only execute the collector job after having done the rest.
  • If all else fails, and the I/O load is not too great, consider using sshfs with ssh passkeys instead of NFS.
  • In some situations it is necessary to preserve the same directory structure on all nodes, and symlinks are a nice way to do that, but some (closed source 3rd party which will remain nameless) software insists on expanding symlinks. In this situation the bindfs FUSE extension can help. It is safer to use than "mount" binds, and you don't have to be root to set them up. As with sshfs there is a performance penalty so it may not be appropriate in high I/O situations.

Error: Too many open files

When working with long time series and r.series starts to complain that files are missing/not readable or the message 

Too many open files

For a solution, see Large_raster_data_processing#Number_of_open_files_limitation

Misc Tips & Tricks

See the poor-man's multi-processing script on the Parallelizing Scripts wiki page. This approach has been used in the r3.in.xyz script.

Workshop on Parallelization

Haedrich, C., Petrasova, A. Parallelization for big EO data processing, OpenGeoHub Summer School 2023

Schedule

Session 1

  • [Slides] Introduction to Parallelization, GRASS GIS and Python
  • [Lab] Introduction to Parallelization with GRASS GIS and Python Notebook

Session 2

  • [Lab] Parallelization Case Study: Urban Growth Modeling

Jupyter Notebook:

See also

This Wiki:

Elsewhere:

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