We'd probably want a special "MPI details" INI section to run Fluent,On Tue, Apr/14/2009 09:27:14PM, Mike Dubman wrote:
> On Tue, Apr 14, 2009 at 5:04 PM, Jeff Squyres <email@example.com> wrote:
> On Apr 13, 2009, at 2:08 PM, Mike Dubman wrote:
> Hello Ethan,
> Sorry for joining the discussion late... I was on travel last week and
> that always makes me waaay behind on my INBOX. *:-(
> On Mon, Apr 13, 2009 at 5:44 PM, Ethan Mallove <firstname.lastname@example.org>
> Will this translate to something like
> lib/MTT/Reporter/GoogleDatabase.pm? *If we are to move away from the
> current MTT Postgres database, we want to be able to submit results to
> both the current MTT database and the new Google database during the
> transition period. Having a GoogleDatabase.pm would make this easier.
> I think we should keep both storage options: current postgress and
> datastore. The mtt changes will be minor to support datastore.
> Due that fact that google appengine API (as well as datastore API) can
> be python or java only, we will create external scripts to manipulate
> datastore objects:
> Ah, good point (python/java not perl). *But I think that
> lib/MTT/Reporter/GoogleDataStore.pm could still be a good thing -- we
> have invested a lot of time/effort into getting our particular mtt
> clients setup just the way we want them, setting up INI files,
> submitting to batch schedulers, etc.
> A GoogleDataStore.pm reporter could well fork/exec a python/java
> executable to do the actual communication/storing of the data, right...?
> *More below.
> completely agree, once we have external python/java/cobol scripts to
> manipulate GDS objects, we should wrap it by perl and call from MTT in
> same way like it works today for submitting to the postgress.
> The mtt will dump test results in xml format. Then, we provide two
> python (or java?) scripts:
> mtt-results-submit-to-datastore.py - script will be called at the end
> of mtt run and will read xml files, create objects and save to
> Could be pretty easy to have a Reporter/GDS.pm (I keep making that
> filename shorter, don't I? :-) ) that simply invokes the
> mtt-result-submit-to-datastore.pt script on the xml that it dumped for
> that particular test.
> Specifically: I do like having partial results submitted while my MTT
> tests are running. *Cisco's testing cycle is about 24 hours, but groups
> of tests are finishing all the time, so it's good to see those results
> without having to wait the full 24 hours before anything shows up. *I
> guess that's my only comment on the idea of having a script that
> traverses the MTT scratch to find / submit everything -- I'd prefer if
> we kept the same Reporter idea and used an underlying .py script to
> submit results as they become ready.
> Is this do-able?
> sounds good, we should introduce some guid (like pid) for mtt session,
> where all mtt results generated by this session will be referring to this
> guid.* Later we use this guid to submit partial results as they become
> ready and connect it to the appropriate mtt session object (see models.py)
> mtt-results-query.py - sample script to query datastore and generate
> some simple visual/tabular reports. It will serve as tutorial for
> howto access mtt data from scripts for reporting.
> Later, we add another script to replace php web frontend. It will be
> hosted on google appengine machines and will provide web viewer for
> mtt results. (same way like index.php does today)
> Sounds good.
> > * * *b. mtt_save_to_db.py - script which will go over mtt scratch
> dir, find
> > * * *all xml files generated for every mtt phase, parse it and save
> > * * *datastore, preserving test results relations,i.e. all test
> results will
> > * * *be grouped by mtt general info: mpi version, name, date, ....
> > * * *c. same script can scan, parse and save from xml files
> generated by
> > * * *wrapper scripts for non mtt based executions (fluent, ..)
> I'm confused here. *Can't MTT be outfitted to report results of a
> Fluent run?
> I think we can enhance mtt to be not only mpi testing platform, but
> also to serve as mpi benchmarking platform. We can use datastore to
> keep mpi-based benchmarking results in the same manner like mtt does
> for testing results. (no changes to mtt required for that, it is just
> a side effect of using datastore to keep data of any type)
> I think that Ethan was asking was: can't MTT run Fluent and then use the
> normal Reporter mechanism to report the results into whatever back-end
> data store we have? *(postgres or GDS)
> ahhh, okie, i see.
> Correct me if Im wrong, the current mtt implementation allows following
> way of executing mpi test:
> /path/to/mpirun <mpirun options> <test>
> Many mpi based applications have embedded MPI libraries and non-standard
> way to start it, one should set env variable to point to desired mpi
> installation or pass it as cmd line argument, for example:
> for fluent:
> export OPENMPI_ROOT=/path/to/openmpi
> fluent <cmd line args>
[MPI Details: Fluent]
exec = fluent @fluent_args@
...Ditto for pamcrash.
> for pamcrash:
> pamworld -np 2 -mpidir=/path/to/openmpi/dir ....
MTT supports the following INI parameters:
> Im not sure if it is possible to express that execution semantic in mtt
> ini file. Please suggest.
> So far, it seems that such executions can be handled externally from mtt
> but using same object model.
> I can see the value of both sides -- a) using the MTT client as the
> gateway to *all* data storage, or b) making MTT but one (possibly of
> many) tools that can write into the GDS. *a) certainly is more
> attractive towards having a common data format back in GDS such that a
> single web tool is capable of reporting from the data and being able to
> make conherent sense out of the data (vs. 3rd party tools that put data
> back in GDS that may not be in exactly the same format / layout and
> therefore our web reporter may not be able to make sense out of the data
> and report on it).
> I think that having a Reporter/GDS.pm that system()'s the back-end
> python script gives the best of both worlds -- the MTT client can
> [continue to] submit results in the normal way, but there's also a
> standalone script that can submit results from external tool runs (e.g.,
> manually running Fluent, parsing the results, and submitting to our
> GDS). *And hopefully the back-end python script will enforce a specific
> structure to the data that is submitted so that all tools -- MTT and any
> 3rd party tools -- adhere to the same format and the reporter can
> therefore report on it coherently.
> agree. (a) is a preferred form. (b) can be used for tools that cannot be
> called from mtt.
> For the attachment...
> I can "sorta read" python, but I'm not familiar with its intricacies and
> its internal APIs.
> - models.py: looks good. *I don't know if *all* the fields we have are
> listed here; it looks fairly short to me. *Did you attempt to include
> all of the fields we submit through the various phases in Reporter are
> there, or did you intentionally leave some out? *(I honestly haven't
> checked; it just "feels short" to me compared to our SQL schema).
> I listed only some of the fields in every object representing specific
> test result source (called phase in mtt language). This is because every
> test result source object is derived from python provided db.Expando
> class. This gives us great flexibility, like adding dynamic attributes for
> every objects, for example:
> obj = new MttBuildPhaseResult()
> obj.my_favorite_dynamic_key = "hello"
> obj.my_another_dynamic_key = 7
> So, we can have all phase attributes in the phase object without defining
> it in the *sql schema way*. Also we can query object model by these
> dynamic keys.
It looks like model.py doesn't have the daisy chain of inheritance
that the SQL schema requires.
Shouldn't RunTestPhase back-reference the MPIInstallPhase,
TestBuildPhase, and TestSession phase? E.g., we might need to look at
the configure arguments that are keyed to a given test run.
you are right, will add it to the model. Every phase object will have a reference to other relevant phase objects, i.e.
> --> meta question: is it in the zen of GDS to not have too many index
> fields like you would in SQL? *I.e., if you want to do an operation on
> GDS that you
> as far as it seems now, gds creates indexes automatically and also
> provides API to define indexes manually.
> would typically use an SQL index field for, is the idea that you would
> do a map/reduce to select the data instead of an index field?
> yep. seems correct.
> - start_datastore.sh: hmm. *This script seems to imply that the
> datastore is *local*! *Don't we have to HTTP submit the results to
> Google? *More specifically: what is dev_appserver.py? *Is that,
> perchance, just a local proxy agent that will end up submitting our data
> to $datastore_path, which actually resides at Google? *Do we have to use
> a specific google username/URL to submit (and query) results?
> You need to download google`s sdk (dev_appserver.py is a part of it). In
> order to develop for gds you* run your code inside sdk locally, and when
> feel comfortable with it - you upload it to the google cluster. In order
> to run attached example, you need to download sdk, put it in the following
> dir hierarchy:
> and run start_datastore.sh, which will run local instance of GDS on your
> machine.Then in another shell you need to run vbech-dev.py, which
> simulates mtt client accessing GDS, storing some objects in according to
> proposed models and then running some sql-like quires to fetch and
> manipulate results.
> - there's no comments in vbench-dev.py -- can you explain what's going
> on in there? *Can you explain how we would use these scripts?
> This is a mtt simulator, it implements google appengine API to receive
> HTTP requests and call appropriate callbacks. (there is a map of specific
> urls to callbacks).
> The main callback (which intercepts http GET requests to specific URL)
> runs the test code which creates objects defined in models.py, groups many
> test results into MTTSession and they run some queries to access
> previously created objects.
> The real mtt client will use URL pointing to MTT python code running at
> google`s cluster, and use near same code to create/query/manipulate
> objects defined in models.py.
> - it *looks* like these scripts are for storing data out in the GDS.
> *Have you looked at the querying side? *Do we know that storing data in
> the form you listed in models.py are easily retrievable in the ways that
> we want? *E.g., can you mock up queries that resemble the queries we
> currently have in our web-based query system today, just to show that
> storing the data in this way will actually allow us to do the kinds of
> queries that we want to do?
> I think vbench-dev.py shows some querying capabilities for stored objects,
> there are many ways to query objects by object CLASS and Attributes.
> see many examples here:
> for more querying examples we can use.
> In short: I think I'm missing much of the back-story / rationale of how
> the scripts in your tarball work / are to be used.
> BTW -- if it's useful to have a teleconference about this kind of stuff,
> I can host a WebEx meeting. *WebEx has local dialins around the world,
> including Israel...
> sure, what about next week?
> Jeff Squyres
> Cisco Systems
> Visible links
> . mailto:email@example.com
> . mailto:firstname.lastname@example.org
> . http://submit-to-datastore.pt/
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