[GRASS-dev] Does multi-threading apply to r.series.lwr?

[Nikos Alexandris]

Before processing tens of thousands of Landsat8 TIRS bands, a script
tests the workflow described below, for 74 scenes, that correspond to one
WRS2 tile.


The script takes as inputs:

<Landsat8Pool> path to directory with Landsat scenes
<ScenePattern> regex pattern to match a set of Landsat scene identifiers
<LandCover> land cover map
<grassdb> path to the GRASS GIS data base
<Location> name for the GRASS GIS Location
<TargetMapset> name for the GRASS GIS Mapset to host maps to build a time series
<WindowSize> an odd integer, parameter for a split-window algorithm (SW)


Part 1 of the workflow derives Land Surface Temperature maps by:

1. Creating the target Location
2. Linking pseudo GRASS raster maps to Landsat8 GeoTIFF files
3. Exporting a TGIS-compliant list of maps and timestamps
4. Importing a land cover map required for the SW algorithm
5. Estimating Land Surface Temperature (LST) maps for given scenes (i.landsat8.swlst)
6. Creating a dedicated Mapset for LST maps
7. Copying LST maps in the "LST" Mapset
8. Removing initial LST maps from individual scene Mapsets


With a somewhat strong CPU, producing one LST map (7771 rows by 7651
columns = 59455921 cells), takes ~34 minutes.

For 74 Landsat8 input scenes, first trials took about 42 hours, running
grass-7.3.svn inside a docker container, albeit assigned one CPU.


Part 2 concerns building Time Series by:

1. Creating and LST Spatio-Temporal Raster Dara Set (STRDS)
2. Registering LST maps in TGIS' data base
3. Smoothing the LST STRDS via Local Weighted Regression (r.series.lwr)
4. Timestamping Local-Weight-Regression derived maps
5. Creating an STRDS for LWR maps
6. Registering LWR maps in TGIS' data base


Part 2 took about 130 minutes including all steps. Obviously, step 3 is
practically the consumer.


Overall it took about 44 hours to build an LWR Smoothed LST STRDS.

After proofing the concept, good use of the cluster concerns steps 2, 5,
7 and 8 (of Part 1). Processes that can/should run in parallel, in an
(admittedly heterogeneous) cluster that consists of 912 cores.

Final concern is if multi-threading applies to r.series.lwr (step 3 of
Part 2).

Thank you, Nikos
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