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    SubjectRe: [patch 0/8] mm: memcg fixlets for 3.3
    On Thu, Nov 24, 2011 at 11:39:39AM +0530, Balbir Singh wrote:
    > On Wed, Nov 23, 2011 at 9:12 PM, Johannes Weiner <hannes@cmpxchg.org> wrote:
    > >
    > > Here are some minor memcg-related cleanups and optimizations, nothing
    > > too exciting.  The bulk of the diffstat comes from renaming the
    > > remaining variables to describe a (struct mem_cgroup *) to "memcg".
    > > The rest cuts down on the (un)charge fastpaths, as people start to get
    > > annoyed by those functions showing up in the profiles of their their
    > > non-memcg workloads.  More is to come, but I wanted to get the more
    > > obvious bits out of the way.
    >
    > Hi, Johannes
    >
    > The renaming was a separate patch sent from Raghavendra as well, not
    > sure if you've seen it.

    I did and they are already in -mm, but unless I miss something, those
    were only for memcontrol.[ch]. My patch is for the rest of mm.

    > What tests are you using to test these patches?

    I usually run concurrent kernbench jobs in separate memcgs as a smoke
    test with these tools:

    http://git.cmpxchg.org/?p=statutils.git;a=summary

    "runtest" takes a job-spec file that looks a bit like RPM spec to
    define works with preparation and cleanup phases, and data collectors.
    The memcg kernbench job I use is in the examples directory. You just
    need to put separate kernel source directories into place (linux-`seq
    -w 04`) and then launch it like this:

    runtest -s memcg-kernbench.load `uname -r`

    which will run the test and collect memory.stat of the parent memcg
    every second, which you can then further evaluate with the other
    tools:

    readdict < `uname -r`-memory.stat.data | columns 14 15 | plot
    for example, where readdict translates the "key value" lines of
    memory.stat into tables where each value is on its own row. Columns
    14 and 15 are total_cache and total_rss (find out with cat -n -- yeah,
    still a bit rough). You need python-matplotlib for plot to work.

    Multiple runs can be collected into the same logfiles and then fold
    ever-increasing counters with the "events" tool. For example, to find
    the average fault count, you would do something like this (19 =
    total_pgfault, 20 = total_pgmajfault):

    for x in `seq 10`; do runtest -s foo.load foo`; done
    readdict < foo-memory.stat.data | columns 19 20 | events | mean -s
    Oh, and workload runtime is always recorded in NAME.time, so

    events < `uname -r`.time

    gives you the timings of each run, which you can then further process
    with "mean" or "median" again.
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