User-guide.md

 # Job monitoring and optimization with EAR
 
-EAR was first designed to be 100% transparent by users, which means that
-you can run your applications enabling/disabling/tuning EAR with minimal effort
+EAR was first designed to be usable 100% transparently by users, which means that
+you can run your applications enabling/disabling/tuning EAR with the less effort to
 for changing your workflow, e.g., submission scripts.
 This is achieved by providing integrations (e.g., plug-ins, hooks) with system batch
 schedulers, which do all the effort to set-up EAR at job submission.
-By now, **[SLURM](https://slurm.schedmd.com/documentation.html) is the batch scheduler fully compatible with EAR** thanks to EAR's SLURM SPANK plug-in.
+**[SLURM](https://slurm.schedmd.com/documentation.html) is the main batch scheduler fully compatible with EAR** thanks to EAR's SLURM SPANK plug-in.
 
-With EAR's SLURM plug-in, running an application with EAR is as easy as submitting
-a job with either `srun`, `sbatch` or `mpirun`. The EAR Library (EARL) is automatically
-loaded with some applications when EAR is enabled by default.
+With EAR's SLURM plug-in, running an application with EAR is as easy as submitting a job with either `srun`, `sbatch` or `mpirun`. 
+
+EAR is also compatible with **[PBSPro](https://2025.help.altair.com/2025.2.0/PBS%20Professional/PBSReference2025.2.0.pdf)**, thorugh the EAR PBRPro Hook.
+
+The EAR Library (EARL) is automatically loaded with some applications when EAR is enabled by default.
 
 Check with the [`ear-info`](EAR-commands#ear-info) command if EARL is `on`/`off` by default.
 If it’s `off`, use `--ear=on` option offered by EAR SLURM plug-in to enable it.
@@ -22,13 +24,13 @@ The EAR development team had worked also with OAR and PBSPro batch schedulers, b
 # Use cases
 
 Since EAR was targetting computational applications, some applications are automatically loaded and others are not, avoiding running EAR with, for example, bash processes.
-The following list resumes the application use cases where the EARL can be loaded transparently with them:
+The following list summarizes the application use cases where the EARL can be loaded transparently with them:
 
 - MPI applications: IntelMPI, OpenMPI, Fujitsu and CRAY versions.
 - Non-MPI applications: OpenMP, CUDA, MKL and OneAPI. 
 - Python applications.
 
-Other use cases not listed here might be still supported.
+Other use cases not listed here might still be supported.
 See the [dedicated section](#other-application-types-or-frameworks).
 
 ## MPI applications
@@ -42,7 +44,7 @@ When using specific MPI flavour commands to start applications (e.g., `mpirun`,
 `mpiexec.hydra`), there are some key points which you must take account.
 See [next sections](#using-mpirunmpiexec-command) for examples and more details.
 
-Review SLURM's [MPI Users Guide](https://slurm.schedmd.com/mpi_guide.html), read your cluster documetation or ask your system administrator to see how SLURM is integrated with the MPI Library in your system.
+Review SLURM's [MPI Users Guide](https://slurm.schedmd.com/mpi_guide.html), read your cluster documentation or ask your system administrator to see how SLURM is integrated with the MPI Library in your system.
 
 ### Hybrid MPI + (OpenMP, CUDA, MKL) applications
 
@@ -54,7 +56,7 @@ EARL automatically supports this use case.
 EARL cannot detect automatically MPI symbols when some of these languages are used.
 On that case, an environment variable is provided to give EARL a hint of the MPI flavour being used.
 
-Export the [`EAR_LOAD_MPI_VERSION`](EAR-environment-variables#ear_load_mpi_version) environment with the value from the following table depending on the MPI implementation you are loading:
+Export [`EAR_LOAD_MPI_VERSION`](EAR-environment-variables#ear_load_mpi_version) environment with the value from the following table depending on the MPI implementation you are loading:
 
 | MPI flavour | Value |
 | ----------- | ----- |
@@ -101,7 +103,8 @@ You can read [here](https://www.intel.com/content/www/us/en/develop/documentatio
 For joining OpenMPI and EAR it is highly recommended to use SLURM's `srun` command.
 When using `mpirun`, as OpenMPI is not fully coordinated with the scheduler, EARL
 is not automatilly loaded on all nodes.
-Therefore EARL will be disabled and only basic energy metrics will be reported.
+Therefore EARL will be disabled and only basic energy metrics.
+will be reported.
 To provide support for this workflow, EAR provides [`erun`](EAR-commands#erun) command.
 Read the corresponding [examples section](User-guide.md#openmpi-1) for more information about how to use this command.
 
@@ -129,7 +132,7 @@ See [EAR submission flags](#ear-job-submission-flags) provided by EAR SLURM inte
 ### OpenMP, CUDA, Intel MKL and OneAPI
 
 To load EARL automatically with non-MPI applications it is required to have it compiled with dynamic symbols and also it must be executed with `srun` command.
-For example, for CUDA applications you must use the `--cudart=shared` option at compile time.
+For example, for CUDA applications the `--cudart=shared` option must be used at compile time.
 EARL is loaded for OpenMP, MKL and CUDA programming models when symbols are dynamically detected.
 
 ## Other application types or frameworks
@@ -199,14 +202,14 @@ mpirun -np 64 singularity exec $IMAGE $EAR_INSTALL_PATH/bin/erun \
     --program=$BENCH_PATH/bt-mz.D.64
 ```
 
-Note that the example exports `APPTAINERENV_EAR_REPORT_ADD` to set the environment variable [`EAR_REPORT_ADD`](EAR-environment-variables#report-plug-ins) to load [`sysfs`](https://gitlab.bsc.es/ear_team/ear/-/wikis/Report#sysfs-report-plugin) report plug-in.
+Note that the example exports `APPTAINERENV_EAR_REPORT_ADD` to set the environment variable [`EAR_REPORT_ADD`](EAR-environment-variables#report-plug-ins) to load [`sysfs`](https://gitlab.bsc.es/ear_team/ear_private/-/wikis/Report#sysfs-report-plugin) report plug-in.
 See [next section](#runtime-report-plug-ins) about report plug-ins.
 
 ## Using EARL through the COMPSs Framework 
 
 COMP Superscalar ([COMPSs](https://compss-doc.readthedocs.io/en/latest/index.html)) is a task-based programming model which aims to ease the development of applications for distributed infrastructures, such as large High-Performance clusters (HPC), clouds and container managed clusters.
 COMPSs provides a programming interface for the development of the applications and a runtime system that exploits the inherent parallelism of applications at execution time.
-**Since version 5.0 EAR supports monitoring and optimization of workflows** and the COMPSs Framework includes integration with EAR.
+**Since version 5.0 EAR supports monitoring and optimization of workflows** and the COMPSs Framework includes the integration with EAR.
 Check out the [dedicated section](https://compss-doc.readthedocs.io/en/latest/Sections/05_Tools/05_EAR.html#) from the official COMPSs documentation for more information about how to measure the energy consumption of your workflows.
 
 EARL loading is **only available** using `enqueue_compss` and with Python applications.
@@ -221,7 +224,7 @@ get to see or know your applications workload.
 The Library is doted with several modules and options to be able to provide different
 kind of information.
 
-As a very simple hint of your application workload, you can enable EARL verbosity (i.e., `--ear-verbose=1`) to get loop data at runtime.
+As a very simple hint of your application's workload, you can enable EARL verbosity (i.e., `--ear-verbose=1`) to get loop data at runtime.
 **The information is shown at _stderr_ by default.**
 Read how to set up verbosity at [submission time](#ear-job-submission-flags) and
 [verbosity environment variables](EAR-environment-variables#verbosity) provided
@@ -229,8 +232,9 @@ for a more advanced tuning of this EAR feature.
 
 ## Post-mortem application data
 
-To get offline job data EAR provides [**eacct**](EAR-commands#ear-job-accounting-eacct) command, a tool to provide the monitored job data stored in the Database.
-You can request information in different ways, so you can read aggregated job data, per-node or per-loop information among other things.
+To get offline EAR job data you can use the [**eacct**](EAR-commands#ear-job-accounting-eacct) command, a tool to provide the monitored job data stored in the EAR Database.
+You can request information in different ways.
+Thus, you can read either per-node or aggregated job datas averaged along the execution time or get metrics collected at runtime.
 See [eacct usage examples](User-guide#ear-job-accounting-eacct) for a better overview of what `eacct` provides.
 
 ## Runtime report plug-ins
@@ -243,7 +247,7 @@ You can load this plug-in in two ways:
 1. By setting [`--ear-user-db`](#ear-job-submission-flags) flag at submission time.
 2. [Loading directly the report plug-in](EAR-environment-variables#ear_report_add) through an environment variable: `export EAR_REPORT_ADD=csv_ts.so`.
 
-Read [report plug-ins](https://gitlab.bsc.es/ear_team/ear/-/wikis/Report) dedicated section for more information.
+Read [report plug-ins](https://gitlab.bsc.es/ear_team/ear_private/-/wikis/Report) dedicated section for more information.
 
 ## Other EARL events
 
@@ -361,7 +365,7 @@ variable and one file per node will be generated only with EAR output.
 The environemnt variable must be set with the path (a directory) where you want
 the output files to be generated, it will be automatically created if needed.
 
-> You can always check the avaiable EAR submission flags provided by EAR's SLURM SPANK
+> You can always check the available EAR submission flags provided by EAR's SLURM SPANK
 plug-in by typing `srun --help`.
 
 ## CPU frequency selection
@@ -452,7 +456,7 @@ identification.
 #SBATCH -N 1
 #SBATCH -e test.%j.err
 #SBATCH -o test.%j.out
-#SBTACH --ntasks=24
+#SBATCH --ntasks=24
 #SBATCH --tasks-per-node=24
 #SBATCH --cpus-per-task=1
 #SBATCH --ear-verbose=1