tuning

docs/sphinx/tuning.rst

+Tuning
+======
+
+Make Maestro Core perform.
+
+Knobs
+-----
+
+Maestro features a range of environment variables, the full list and doc follows.
+Maestro is relying on OFI for network operations, therefore the usual OFI knobs
+can also be played with.
+
+.. doxygengroup:: MSTRO_ENV
+
+Telemetry
+---------
+
+``MSTRO_LOG_LEVEL`` environment controls the verbosity of the Maestro logs, and
+can take the following values: error, warning, info, debug, noise. By default
+Maestro Core outputs to ``stderr``, one may also choose ``stdout`` or
+``syslog`` via  ``MSTRO_LOG_DST=syslog``
+
+Log lines look like 
+
+.. code-block:: bash
+
+    [I:pm] Simple_Pool_Manager:0 1 CQ-H-0-0 (nid00001 777) 22222479341864000: mstro_pm__handle_join_phase2(pool_manager.c:2540) JOIN message received. Caller Client:2 is now known as app #2
+
+Which reads as 
+
+.. code-block:: bash
+
+    [<log level>:<log module>] <component_name>:<rank_id> <app_id> <thread_id> (<hostname> <pid>) <timestamp>: <function>(<file>:<lineno>) <message>
+
+
+Profiling
+---------
+ 
+A couple of utilities shipped with Maestro core may complement well existing profiling tools reports to analyse Maestro-enabled workflows:
+
+* ``$(MAESTRO_PATH)/examples/core_bench`` runs a benchmark that shows some basic numbers
+* ``$(MAESTRO_PATH)/visualise/vis.py`` proposes an in-browser interactive visualisation of a Maestro-enabled workflow
+* ``$(MAESTRO_PATH)/examples/transport_bars.py`` plots timings of Maestro operations relative on transport, based on a Maestro logs input
+
+
+Scheduling
+----------
+
+TODO