EAR-environment-variables.md

@@ -20,7 +20,7 @@ All examples showing the usage of below environment variables assume a system us
 
 ## Loading EAR Library
 
-### EAR_LOADER_APPLICATION
+### EAR\_LOADER\_APPLICATION
 
 Rules the EAR Loader to load the EAR Library for a specific application that does not follow any of the current programming models (or maybe a sequential app) supported by EAR.
 Your system must have installed the non-MPI version of the Library (ask your system administrator).
@@ -38,7 +38,7 @@ srun --ntasks 1 --job-name=my_job_name ./my_exec_file
 
 See the [Use cases](User-guide#use-cases) section to read more information about how to run jobs with EAR.
 
-### EAR_LOAD_MPI_VERSION
+### EAR\_LOAD\_MPI\_VERSION
 
 Forces to load a specific MPI version of the EAR Library.
 This is needed, for example, when you want to load the EAR Library for Python + MPI applications, where the Loader is not able to detect the MPI implementation the application is going to use.
@@ -68,7 +68,7 @@ See the [Use cases](User-guide#use-cases) section to read more information about
 
 ## Report plug-ins
 
-### EAR_REPORT_ADD
+### EAR\_REPORT\_ADD
 
 Specify a report plug-in to be loaded. The value must be a shared object file, and it must be located at `$EAR_INSTALL_PATH/lib/plugins/report` or at the path from where the job was launched.
 Alternatively, you can provide the full path (absolute or relative) of the report plug-in.
@@ -83,7 +83,7 @@ srun -n 10 my_mpi_app
 
 ## Verbosity
 
-### EARL_VERBOSE_PATH
+### EARL\_VERBOSE\_PATH
 
 Specify a path to create a file (one per node involved in a job) where to print messages from the EAR Library.
 This is useful when you run a job in multiple nodes, as EAR verbose information for each of them can result in lots of messages mixed at stderr (EAR messages default channel).
@@ -113,7 +113,7 @@ After the above job example completion, in the same directory where the applicat
 
 ## Frequency management
 
-### EAR_GPU_DEF_FREQ
+### EAR\_GPU\_DEF\_FREQ
 
 Set a GPU frequency (in kHz) to be fixed while your job is running.
 The same frequency is set for all GPUs used by the job.
@@ -137,7 +137,7 @@ srun --cpu-bind=core --ear-policy=min_energy gmx_mpi mdrun \
     -s $GROMACS_INPUT -noconfout -ntomp 1
 ```
 
-### EAR_JOB_EXCLUSIVE_MODE
+### EAR\_JOB\_EXCLUSIVE\_MODE
 
 Indicate whether the job will run in a node exclusively (non-zero value).
 EAR will reduce the CPU frequency of those cores not used by the job.
@@ -163,7 +163,7 @@ On this page we will use the term *uncore* to refer both of them.
 Environment variables related to uncore control covers [policy specific settings](#ear_set_imcfreq) or
 the chance for a user to [fix it](#ear_max_imcfreq-and-ear_min_imcfreq) during an entire job.
 
-#### EAR_SET_IMCFREQ
+#### EAR\_SET\_IMCFREQ
 
 Enables/disables EAR's [eUFS](Home#publications) feature.
 Type `ear-info` to see whehter eUFS is enabled by default.
@@ -185,7 +185,7 @@ export SLURM_EAR_POLICY_IMC_TH=0.035
 srun [...] my_app
 ```
 
-#### EAR_MAX_IMCFREQ and EAR_MIN_IMCFREQ
+#### EAR\_MAX\_IMCFREQ and EAR\_MIN\_IMCFREQ
 
 Set the maximum and minimum values (in kHz) at which *uncore* frequency should be.
 Two variables were designed because Intel(R) architectures let to set a range of
@@ -216,7 +216,7 @@ Please, contact with [ear-support@bsc.es](mailto:ear-support@bsc.es) if you want
 
 > A correct CPU binding it's required to get the most benefit of this feature. Check the documentation of your application programming model/vendor/flavour or yur system batch scheduler.
 
-#### EAR_LOAD_BALANCE
+#### EAR\_LOAD\_BALANCE
 
 Enables/Disables EAR's Load Balance strategy in energy policies.
 Type `ear-info` to see whether this feature is enabled by default.
@@ -263,7 +263,7 @@ If you enable [EARL verbosity](User-guide#ear-job-submission-flags) you will see
 the mapping of the CLOS set for each CPU in the node.
 Note that a `-1` value means that no change was done on the specific CPU.
 
-#### EAR_PRIO_TASKS
+#### EAR\_PRIO\_TASKS
 
 A list that specifies the CLOS that CPUs assigned to tasks must be set.
 This variable is useful because you can configure your application transparently
@@ -341,7 +341,7 @@ PRIO3: MAX GHZ - 0.0 GHz (low)
 [120,-1] [121,-1] [122,-1] [123,-1] [124,-1] [125,-1] [126,-1] [127,-1] 
 ```
 
-#### EAR_PRIO_CPUS
+#### EAR\_PRIO\_CPUS
 
 A list of priorities that should have the same length as the number of CPUs your
 job is using.
@@ -355,6 +355,12 @@ But it becomes more flexible when the user has more control over the affinity se
 to its application, because you can discriminate between different CPUs assigned to the same task.
 Moreover, this is the only way to set different priorities over different threads in no-MPI applications.
 
+### EAR\_MIN\_CPUFREQ
+
+This variable can only be set by **authorized users**, and modifies the minimum CPU frequency the EAR Library can set.
+The [EAR configuration](Configuration) file has a field called `cpu_max_pstate` which sets this limits on the tag it is configured.
+Authorized users can modify this limit at submission time by using this environment to test, for example, the best value for the `ear.conf` field.
+
 ### Disabling EAR's affinity masks usage
 
 For both [Load Balancing](load-balancing) and [Intel(R) SST](#support-for-intel-r-speed-select-technology)
@@ -364,7 +370,7 @@ To avoid any unexpected problem, **we highly recommend you** to export `EAR_NO_A
 
 ## Data gathering
 
-### EAR_GET_MPI_STATS
+### EAR\_GET\_MPI\_STATS
 
 Use this variable to generate two files at the end of the job execution that will contain global, per process MPI information.
 You must specify the prefix (optionally with a path) of the filename. One file (*[path/]prefix.ear_mpi_stats.full_nodename.csv*) will contain a resume about MPI throughput (per-process), while the other one (*[path/]prefix.ear_mpi_calls_stats.full_nodename.csv*) will contain a more fine grained information about different MPI call types.