mm: add /proc/pid/smaps_rollup

/proc/pid/smaps_rollup is a new proc file that improves the performance
of user programs that determine aggregate memory statistics (e.g., total
PSS) of a process.

Android regularly "samples" the memory usage of various processes in
order to balance its memory pool sizes.  This sampling process involves
opening /proc/pid/smaps and summing certain fields.  For very large
processes, sampling memory use this way can take several hundred
milliseconds, due mostly to the overhead of the seq_printf calls in
task_mmu.c.

smaps_rollup improves the situation.  It contains most of the fields of
/proc/pid/smaps, but instead of a set of fields for each VMA,
smaps_rollup instead contains one synthetic smaps-format entry
representing the whole process.  In the single smaps_rollup synthetic
entry, each field is the summation of the corresponding field in all of
the real-smaps VMAs.  Using a common format for smaps_rollup and smaps
allows userspace parsers to repurpose parsers meant for use with
non-rollup smaps for smaps_rollup, and it allows userspace to switch
between smaps_rollup and smaps at runtime (say, based on the
availability of smaps_rollup in a given kernel) with minimal fuss.

By using smaps_rollup instead of smaps, a caller can avoid the
significant overhead of formatting, reading, and parsing each of a large
process's potentially very numerous memory mappings.  For sampling
system_server's PSS in Android, we measured a 12x speedup, representing
a savings of several hundred milliseconds.

One alternative to a new per-process proc file would have been including
PSS information in /proc/pid/status.  We considered this option but
thought that PSS would be too expensive (by a few orders of magnitude)
to collect relative to what's already emitted as part of
/proc/pid/status, and slowing every user of /proc/pid/status for the
sake of readers that happen to want PSS feels wrong.

The code itself works by reusing the existing VMA-walking framework we
use for regular smaps generation and keeping the mem_size_stats
structure around between VMA walks instead of using a fresh one for each
VMA.  In this way, summation happens automatically.  We let seq_file
walk over the VMAs just as it does for regular smaps and just emit
nothing to the seq_file until we hit the last VMA.

Benchmarks:

    using smaps:
    iterations:1000 pid:1163 pss:220023808
    0m29.46s real 0m08.28s user 0m20.98s system

    using smaps_rollup:
    iterations:1000 pid:1163 pss:220702720
    0m04.39s real 0m00.03s user 0m04.31s system

We're using the PSS samples we collect asynchronously for
system-management tasks like fine-tuning oom_adj_score, memory use
tracking for debugging, application-level memory-use attribution, and
deciding whether we want to kill large processes during system idle
maintenance windows.  Android has been using PSS for these purposes for
a long time; as the average process VMA count has increased and and
devices become more efficiency-conscious, PSS-collection inefficiency
has started to matter more.  IMHO, it'd be a lot safer to optimize the
existing PSS-collection model, which has been fine-tuned over the years,
instead of changing the memory tracking approach entirely to work around
smaps-generation inefficiency.

Tim said:

: There are two main reasons why Android gathers PSS information:
:
: 1. Android devices can show the user the amount of memory used per
:    application via the settings app.  This is a less important use case.
:
: 2. We log PSS to help identify leaks in applications.  We have found
:    an enormous number of bugs (in the Android platform, in Google's own
:    apps, and in third-party applications) using this data.
:
: To do this, system_server (the main process in Android userspace) will
: sample the PSS of a process three seconds after it changes state (for
: example, app is launched and becomes the foreground application) and about
: every ten minutes after that.  The net result is that PSS collection is
: regularly running on at least one process in the system (usually a few
: times a minute while the screen is on, less when screen is off due to
: suspend).  PSS of a process is an incredibly useful stat to track, and we
: aren't going to get rid of it.  We've looked at some very hacky approaches
: using RSS ("take the RSS of the target process, subtract the RSS of the
: zygote process that is the parent of all Android apps") to reduce the
: accounting time, but it regularly overestimated the memory used by 20+
: percent.  Accordingly, I don't think that there's a good alternative to
: using PSS.
:
: We started looking into PSS collection performance after we noticed random
: frequency spikes while a phone's screen was off; occasionally, one of the
: CPU clusters would ramp to a high frequency because there was 200-300ms of
: constant CPU work from a single thread in the main Android userspace
: process.  The work causing the spike (which is reasonable governor
: behavior given the amount of CPU time needed) was always PSS collection.
: As a result, Android is burning more power than we should be on PSS
: collection.
:
: The other issue (and why I'm less sure about improving smaps as a
: long-term solution) is that the number of VMAs per process has increased
: significantly from release to release.  After trying to figure out why we
: were seeing these 200-300ms PSS collection times on Android O but had not
: noticed it in previous versions, we found that the number of VMAs in the
: main system process increased by 50% from Android N to Android O (from
: ~1800 to ~2700) and varying increases in every userspace process.  Android
: M to N also had an increase in the number of VMAs, although not as much.
: I'm not sure why this is increasing so much over time, but thinking about
: ASLR and ways to make ASLR better, I expect that this will continue to
: increase going forward.  I would not be surprised if we hit 5000 VMAs on
: the main Android process (system_server) by 2020.
:
: If we assume that the number of VMAs is going to increase over time, then
: doing anything we can do to reduce the overhead of each VMA during PSS
: collection seems like the right way to go, and that means outputting an
: aggregate statistic (to avoid whatever overhead there is per line in
: writing smaps and in reading each line from userspace).

Link: http://lkml.kernel.org/r/20170812022148.178293-1-dancol@google.com
Signed-off-by: Daniel Colascione <dancol@google.com>
Cc: Tim Murray <timmurray@google.com>
Cc: Joel Fernandes <joelaf@google.com>
Cc: Al Viro <viro@zeniv.linux.org.uk>
Cc: Randy Dunlap <rdunlap@infradead.org>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Michal Hocko <mhocko@kernel.org>
Cc: Sonny Rao <sonnyrao@chromium.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>

1 files changed, 31 insertions, 0 deletions

diff --git a/Documentation/ABI/testing/procfs-smaps_rollup b/Documentation/ABI/testing/procfs-smaps_rollup
new file mode 100644
index 000000000000..0a54ed0d63c9
--- /dev/null
+++ b/Documentation/ABI/testing/procfs-smaps_rollup
@@ -0,0 +1,31 @@
+What:		/proc/pid/smaps_rollup
+Date:		August 2017
+Contact:	Daniel Colascione <dancol@google.com>
+Description:
+		This file provides pre-summed memory information for a
+		process.  The format is identical to /proc/pid/smaps,
+		except instead of an entry for each VMA in a process,
+		smaps_rollup has a single entry (tagged "[rollup]")
+		for which each field is the sum of the corresponding
+		fields from all the maps in /proc/pid/smaps.
+		For more details, see the procfs man page.
+
+		Typical output looks like this:
+
+		00100000-ff709000 ---p 00000000 00:00 0		 [rollup]
+		Rss:		     884 kB
+		Pss:		     385 kB
+		Shared_Clean:	     696 kB
+		Shared_Dirty:	       0 kB
+		Private_Clean:	     120 kB
+		Private_Dirty:	      68 kB
+		Referenced:	     884 kB
+		Anonymous:	      68 kB
+		LazyFree:	       0 kB
+		AnonHugePages:	       0 kB
+		ShmemPmdMapped:	       0 kB
+		Shared_Hugetlb:	       0 kB
+		Private_Hugetlb:       0 kB
+		Swap:		       0 kB
+		SwapPss:	       0 kB
+		Locked:		     385 kB