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# Practical guide for using startR at BSC
In this guide some practical examples are shown for you to see how to use startR to process large data sets in parallel on your workstation at the BSC ES or on the BSC's HPCs.
In order to do so, you will need to understand and use four functions, all of them included in the startR package:
- **Start()**, for declaing the data sets to process
- **Step()** and **AddStep()**, for specifying the operation to be applied to the data
- **Compute()**, for specifying the HPC to be employed, the number of chunks and cores, and to trigger the computation
But, in first place, you must follow the deployment steps to make sure startR will work on your workstation with the HPC of your choice, and follow some tricks for a better experience.
The full deployment steps are detailed in the [**Deployment**](inst/doc/deployment.md) section. However at BSC you do not need to follow all of them since all dependencies are already installed for you.
You only need to set up passwordless, userless access from your machine to the HPC login node, and from the HPC login node to your machine if at all possible. To establish the connection in one of the directions, you can do the following:
1- generate an ssh pair of keys on the origin host if you do not have one, using `ssh-keygen -t rsa`
2- ssh to the destionation node and create a directory where to store it, using `ssh username@hostname_or_ip mkdir -p .ssh`
3- dump your public key on a new file under that folder, using `cat .ssh/id_rsa.pub | ssh username@hostname_or_ip 'cat >> .ssh/authorized_keys'`
4- adjust the permissions, using `ssh username@hostname_or_ip "chmod 700 .ssh; chmod 640 .ssh/authorized_keys"`
5- if your username is different on your workstation and on the HPC login node, add an entry in the file .ssh/config in your workstation as follows:
```
Host short_name_of_the_host
HostName hostname_or_ip
User username
IdentityFile ~/.ssh/id_rsa
```
If you have followed these steps for the connections in the two directions (from HPC to workstation might not be possible), you are almost good to go.
Do not forget adding the following lines on your .bashrc on CTE-Power, if you are planning to run on CTE-Power:
```
if [[ $BSC_MACHINE == "power" ]] ; then
module unuse /apps/modules/modulefiles/applications
module use /gpfs/projects/bsc32/software/rhel/7.4/ppc64le/POWER9/modules/all/
fi
```
Also, you can add the following lines on your .bashrc on your workstation for convenience:
```
alias ctp='ssh -X username@hostname_or_ip'
alias start='module load R CDO ecFlow'
```
Then, when you open a new terminal session, you will just need to run the following commands and a fresh R session will pop up with the startR environment ready to use.
```
start
R
```
In order to declare the data sets you want to process, you first need to build a special path that shows where all the involved NetCDF files you want to process are stored, containing some wildcards in those parts of the path that vary across files. This special path is also called "path pattern".
Before defining an example path pattern, let's introduce some target NetCDF files. In esarchive, we can find the following files:
```
/esarchive/exp/ecmwf/system5_m1/6hourly/
|--tas/
| |--tas_19930101.nc
| |--tas_19930201.nc
| | ...
| |--tas_20171201.nc
|--tos/
|--tos_19930101.nc
|--tos_19930201.nc
| ...
|--tos_20171201.nc
```
A path pattern that could be used to define the location of these files in a compact way is the following:
```r
repos <- '/esarchive/exp/ecmwf/system5_m1/6hourly/$var$/$var$_$sdate$.nc'
```
The wildcards used (the pieces wrapped between '$' symbols) can be given any names you like, they do not necessarily need to be 'var' or 'sdate' or match any other keyword.
Once the path pattern is specified, a `Start()` call can be built, which requests the values of interest for each of the wildcards (also called outer dimensions), as well as for each of the dimensions inside the NetCDF files (inner dimensions).
You can check in advance which dimensions are inside the NetCDF files by using the basic NetCDF tools:
```
ncdump -h /esarchive/exp/ecmwf/system5_m1/6hourly/tas/tas_19930101.nc
```
This would reveal the following inner dimensions: 'ensemble', 'time', 'latitude', and 'longitude'.
We now have all the information we need to put the `Start()` call together:
```r
data <- Start(dat = repos,
# outer dimensions
var = 'tas',
sdate = '19930101',
# inner dimensions
ensemble = 'all',
time = 'all',
latitude = 'all',
longitude = 'all')
```
This will display some progress and information messages:
```r
* Exploring files... This will take a variable amount of time depending
* on the issued request and the performance of the file server...
* Detected dimension sizes:
* dat: 1
* var: 1
* sdate: 1
* ensemble: 25
* time: 860
* latitude: 640
* longitude: 1296
* Total size of involved data:
* 1 x 1 x 1 x 25 x 860 x 640 x 1296 x 8 bytes = 132.9 Gb
* Successfully discovered data dimensions.
Warning messages:
1: ! Warning: Parameter 'pattern_dims' not specified. Taking the first dimension,
! 'dat' as 'pattern_dims'.
2: ! Warning: Could not find any pattern dim with explicit data set descriptions (in
! the form of list of lists). Taking the first pattern dim, 'dat', as
! dimension with pattern specifications.
```
The warnings shown are normal, and could be avoided with a more wordy specification of the parameters to the `Start()` function.
The dimensions of the selected data set and the total size are shown. As you have probably noticed, this `Start()` call is very fast, even though several GB of data are involved. This is because `Start()` is simply discovering the location and dimension of the involved data.
You can give a quick look to the collected metadata with `str(data)`.
```r
Class 'startR_header' length 9 Start(dat = "/esarchive/exp/ecmwf/system5_m1/6hourly/$var$/$var$_$sdate$.nc", var = "tas", sdate = "19930101", ensemble = "all", time = "all", latitude = "all", ...
..- attr(*, "Dimensions")= Named num [1:7] 1 1 1 25 860 ...
.. ..- attr(*, "names")= chr [1:7] "dat" "var" "sdate" "ensemble" ...
..- attr(*, "Variables")=List of 2
.. ..$ common: NULL
.. ..$ dat1 : NULL
..- attr(*, "ExpectedFiles")= chr [1, 1, 1] "/esarchive/exp/ecmwf/system5_m1/6hourly/tas/tas_19930101.nc"
..- attr(*, "FileSelectors")=List of 1
.. ..$ dat1:List of 3
.. .. ..$ dat :List of 1
.. .. .. ..$ : chr "dat1"
.. .. ..$ var :List of 1
.. .. .. ..$ : chr "tas"
.. .. ..$ sdate:List of 1
.. .. .. ..$ : chr "19930101"
..- attr(*, "PatternDim")= chr "dat"
```
There are no constrains for the numer or names of the outer or inner dimensions. In other words, `Start()` will handle NetCDF files with any number of dimensions with any name, as well as files distributed across folders in complex ways, since you can use customized wildcards in the path pattern.
If you are interested in actually loading the entire data set in your machine you can do so in two ways (*be careful, doing so with the `Start()` call used in this example will most likely stall your machine. Try it with a smaller region or a subset of forecast time steps*):
- adding the parameter `retrieve = TRUE` in your `Start()` call.
- evaluating the object returned by `Start()`: `data_load <- eval(data)`
You may realize that this functionality is similar to the `Load()` function in the s2dverification package. In fact, `Start()` is more advanced and flexible, although `Load()` is more mature and consistent for loading typical seasonal to decadal forecasting data. `Load()` will be adapted in the future to use `Start()` internally.
As you can see in the issued `Start()` call, we have requested specific values for the outer dimensions (e.g. `var = 'tas'` or `sdate = '19930101'`), but vectors of multiple values, numeric indices, or keywords can be used. For example, `var = c('tas', 'tos')`, `sdate = 1:5` or `sdate = 'all'`. See the documentation on the `Start()` function (https://earth.bsc.es/gitlab/es/startR/blob/master/vignettes/start.md) or in `?Start` for more information.
## Step() and AddStep()
Once the data sources are declared, we can define the operation to be applied. The operation needs to be encapsulated in the form of an R function receiving one or more multidimensional arrays (plus additional helper parameters) and returning one or more multidimensional arrays. For example:
```r
fun <- function(x) {
r <- sqrt(sum(x ^ 2) / length(x))
for (i in 1:100) {
r <- sqrt(sum(x ^ 2) / length(x))
}
dim(r) <- c(time = 1)
r
}
```
This function receives only one multidimensional array (with dimensions c('ensemble' and 'time'), although not expressed in the code), and returns one multidimensional array (with a single dimension c('time') of length 1).
Having the function, the startR Step for this operation can be defined with the function `Step()` which requires, for a proper functioning, to specify the names of the dimensions of the input arrays expected by the function (in this example, a single array with the dimensions 'ensemble' and 'time'), as well as the names of the dimensions of the arrays the function returns:
```r
step <- Step(fun = fun,
target_dims = c('ensemble', 'time'),
output_dims = c('time'))
```
Finally, a workflow of steps can be assembled as follows:
```r
wf <- AddStep(data, step)
```
Functions that receive or return multiple multidimensional arrays are also supported by specifying lists of vectors of dimension names as `target_dims` or `output_dims`.
It is not possible for now to define workflows with more than one step. This is pending future work.
Since functions wrapped with the `Step()` function will potentially be called thousands of times, it is recommended to keep them as light as possible by, for example, avoiding calls to the `library()` function to load other packages.
Once the data sources are declared and the workflow is defined, we can proceed to specify the execution parameters (including which platform to run on) and trigger the execution.
Next, a few examples show examples of StartR codes to process datasets locally and on two example HPCs at BSC: the fat nodes and CTE-Power.
### Compute() locally
When only your own workstation is available, StartR can still be useful to process a very large dataset by chunks, avoiding a RAM memory overload and crash of the workstation. StartR will simply load the dataset by chunks and each of them will be processed sequentially. The operations defined in the workflow will be applied to each chunk, and the results will be stored on a temporary file. `Compute()` will finally gather and merge the results of each chunk and return a single data object, including one or multiple multidimensional data arrays, and additional metadata.
```r
res <- Compute(wf,
chunks = list(latitude = 2,
longitude = 2),
threads_load = 1,
threads_compute = 2,
silent = FALSE,
debug = FALSE,
wait = FALSE)
```
compute will return a data array, as if it was a variable in your R session
discuss ecFlow
discuss plotProfiling
```r
res <- Compute(wf,
chunks = list(latitude = 2,
longitude = 2),
threads_load = 1,
threads_compute = 2,
cluster = list(queue_host = 'bsceslogin01.bsc.es',
queue_type = 'slurm',
temp_dir = '/home/Earth/nmanuben/startR_tests/',
r_module = 'R/3.2.0',
cores_per_job = 2,
job_wallclock = '00:10:00',
max_jobs = 4
),
ecflow_suite_dir = '/home/Earth/nmanuben/test_remove/',
silent = FALSE,
debug = FALSE,
wait = FALSE)
```
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```r
library(startR)
#repos <- '/esarchive/exp/ecmwf/system5_m1/6hourly/$var$/$var$_$sdate$.nc'
repos <- '/esarchive/exp/ecmwf/system5_m1/6hourly/$var$-longitudeS1latitudeS1all/$var$_$sdate$.nc'
data <- Start(dat = repos,
var = 'tas',
#sdate = 'all',
sdate = indices(1),
ensemble = 'all',
time = 'all',
#latitude = 'all',
latitude = indices(1:40),
#longitude = 'all',
longitude = indices(1:40),
retrieve = FALSE)
lons <- attr(data, 'Variables')$common$longitude
lats <- attr(data, 'Variables')$common$latitude
fun <- function(x) apply(x + 1, 2, mean)
step <- Step(fun, c('ensemble', 'time'), c('time'))
wf <- AddStep(data, step)
res <- Compute(wf,
chunks = list(latitude = 2,
longitude = 2),
threads_load = 1,
threads_compute = 2,
cluster = list(queue_host = 'p9login1.bsc.es',
queue_type = 'slurm',
data_dir = '/gpfs/projects/bsc32/share/startR_data_repos/gpfs/archive/bsc32/',
temp_dir = '/gpfs/scratch/bsc32/bsc32473/startR_tests/',
lib_dir = '/gpfs/projects/bsc32/share/R_libs/3.5/',
#init_commands = list('module load intel/16.0.1'),
r_module = 'R/3.5.0-foss-2018b',
#ecflow_module = 'ecFlow/4.9.0-foss-2015a',
#node_memory = NULL, #not working
cores_per_job = 2,
job_wallclock = '00:10:00',
max_jobs = 4,
extra_queue_params = list('#SBATCH --qos=bsc_es'),
bidirectional = FALSE,
polling_period = 10#,
#special_setup = 'marenostrum4'
),
ecflow_suite_dir = '/home/Earth/nmanuben/test_remove/',
ecflow_server = NULL,
silent = FALSE,
debug = FALSE,
wait = TRUE)
```
What to do if my function requires all dimensions
Computation of weekly means with startR is still pending future work. By now, it is not possible to do that because the metadata associated to each chunk, such as the dates, is not being sent to the `Compute()` function.
### Example using experimental and (date-corresponding) observational data
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```r
repos <- paste0('/esnas/exp/ecmwf/system4_m1/6hourly/',
'$var$/$var$_$sdate$.nc')
system4 <- Start(dat = repos,
var = 'sfcWind',
#sdate = paste0(1981:2015, '1101'),
sdate = paste0(1981:1984, '1101'),
#time = indices((30*4+1):(120*4)),
time = indices((30*4+1):(30*4+4)),
ensemble = 'all',
#ensemble = indices(1:6),
#latitude = 'all',
latitude = indices(1:10),
#longitude = 'all',
longitude = indices(1:10),
return_vars = list(latitude = NULL,
longitude = NULL,
time = c('sdate')))
repos <- paste0('/esnas/recon/ecmwf/erainterim/6hourly/',
'$var$/$var$_$file_date$.nc')
dates <- attr(system4, 'Variables')$common$time
dates_file <- sort(unique(gsub('-', '', sapply(as.character(dates),
substr, 1, 7))))
erai <- Start(dat = repos,
var = 'sfcWind',
file_date = dates_file,
time = values(dates),
#latitude = 'all',
latitude = indices(1:10),
#longitude = 'all',
longitude = indices(1:10),
time_var = 'time',
time_tolerance = as.difftime(1, units = 'hours'),
time_across = 'file_date',
return_vars = list(latitude = NULL,
longitude = NULL,
time = 'file_date'),
merge_across_dims = TRUE,
split_multiselected_dims = TRUE)
step <- Step(eqmcv_atomic,
list(a = c('ensemble', 'sdate'),
b = c('sdate')),
list(c = c('ensemble', 'sdate')))
res <- Compute(step, list(system4, erai),
chunks = list(latitude = 5,
longitude = 5,
time = 2),
cluster = list(queue_host = 'bsceslogin01.bsc.es',
max_jobs = 4,
cores_per_job = 2),
shared_dir = '/esnas/scratch/nmanuben/test_bychunk',
wait = FALSE)
```
```r
library(startR)
repos <- paste0('/esarchive/exp/ecmwf/system5_m1/6hourly/',
'$var$-longitudeS1latitudeS1all/$var$_$sdate$.nc')
# Slower alternative, using files with a less efficient NetCDF
# compression configuration
#repos <- '/esarchive/exp/ecmwf/system5_m1/6hourly/$var$/$var$_$sdate$.nc'
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data <- Start(dat = repos,
var = 'tas',
sdate = indices(1),
ensemble = 'all',
time = 'all',
latitude = indices(1:40),
longitude = indices(1:40),
retrieve = FALSE)
lons <- attr(data, 'Variables')$common$longitude
lats <- attr(data, 'Variables')$common$latitude
fun <- function(x) apply(x + 1, 2, mean)
step <- Step(fun, c('ensemble', 'time'), c('time'))
wf <- AddStep(data, step)
res <- Compute(wf,
chunks = list(latitude = 2,
longitude = 2),
threads_load = 1,
threads_compute = 2,
cluster = list(queue_host = 'mn2.bsc.es',
queue_type = 'slurm',
data_dir = '/gpfs/projects/bsc32/share/startR_data_repos/',
temp_dir = '/gpfs/scratch/pr1efe00/pr1efe03/startR_tests/',
lib_dir = '/gpfs/projects/bsc32/share/R_libs/3.4/',
r_module = 'R/3.4.0',
cores_per_job = 2,
job_wallclock = '00:10:00',
max_jobs = 4,
extra_queue_params = list('#SBATCH --qos=prace'),
bidirectional = FALSE,
polling_period = 10,
special_setup = 'marenostrum4'
),
ecflow_suite_dir = '/home/Earth/nmanuben/test_remove/',
ecflow_server = NULL,
silent = FALSE,
debug = FALSE,
wait = TRUE)
```
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## Seasonal forecast verification example on cca
```r
crps <- function(x, y) {
mean(SpecsVerification::EnsCrps(x, y, R.new = Inf))
}
library(startR)
repos <- '/perm/ms/spesiccf/c3ah/qa4seas/data/seasonal/g1x1/ecmf-system4/msmm/atmos/seas/tprate/12/ecmf-system4_msmm_atmos_seas_sfc_$date$_tprate_g1x1_init12.nc'
data <- Start(dat = repos,
var = 'tprate',
date = 'all',
time = 'all',
number = 'all',
latitude = 'all',
longitude = 'all',
return_vars = list(time = 'date'))
dates <- attr(data, 'Variables')$common$time
repos <- '/perm/ms/spesiccf/c3ah/qa4seas/data/ecmf-ei_msmm_atmos_seas_sfc_19910101-20161201_t2m_g1x1_init02.nc'
obs <- Start(dat = repos,
var = 't2m',
time = values(dates),
latitude = 'all',
longitude = 'all',
split_multiselected_dims = TRUE)
s <- Step(crps, target_dims = list(c('date', 'number'), c('date')),
output_dims = NULL)
wf <- AddStep(list(data, obs), s)
r <- Compute(wf,
chunks = list(latitude = 10,
longitude = 3),
cluster = list(queue_host = 'cca',
queue_type = 'pbs',
max_jobs = 10,
init_commands = list('module load ecflow'),
r_module = 'R/3.3.1',
extra_queue_params = list('#PBS -l EC_billing_account=spesiccf')),
ecflow_output_dir = '/perm/ms/spesiccf/c3ah/startR_test/',
is_ecflow_output_dir_shared = FALSE
)
```
## Compute() cluster template for Nord III
```r
cluster = list(queue_host = 'nord1.bsc.es',
queue_type = 'lsf',
data_dir = '/gpfs/projects/bsc32/share/startR_data_repos/',
temp_dir = '/gpfs/scratch/bsc32/bsc32473/startR_tests/',
lib_dir = '/gpfs/projects/bsc32/share/R_libs/3.3/',
init_commands = list('module load intel/16.0.1'),
r_module = 'R/3.3.0',
cores_per_job = 2,
job_wallclock = '00:10',
max_jobs = 4,
extra_queue_params = list('#BSUB -q bsc_es'),
bidirectional = FALSE,
polling_period = 10,
special_setup = 'marenostrum4'
)
```
## Compute() cluster template for MinoTauro
```r
cluster = list(queue_host = 'mt1.bsc.es',
queue_type = 'slurm',
data_dir = '/gpfs/projects/bsc32/share/startR_data_repos/',
temp_dir = '/gpfs/scratch/bsc32/bsc32473/startR_tests/',
lib_dir = '/gpfs/projects/bsc32/share/R_libs/3.3/',
r_module = 'R/3.3.3',
cores_per_job = 2,
job_wallclock = '00:10:00',
max_jobs = 4,
extra_queue_params = list('#SBATCH --qos=bsc_es'),
bidirectional = FALSE,
polling_period = 10,
special_setup = 'marenostrum4'
)
```