mm, hugetlb: improve page-fault scalability

The kernel can currently only handle a single hugetlb page fault at a
time.  This is due to a single mutex that serializes the entire path.
This lock protects from spurious OOM errors under conditions of low
availability of free hugepages.  This problem is specific to hugepages,
because it is normal to want to use every single hugepage in the system
- with normal pages we simply assume there will always be a few spare
pages which can be used temporarily until the race is resolved.

Address this problem by using a table of mutexes, allowing a better
chance of parallelization, where each hugepage is individually
serialized.  The hash key is selected depending on the mapping type.
For shared ones it consists of the address space and file offset being
faulted; while for private ones the mm and virtual address are used.
The size of the table is selected based on a compromise of collisions
and memory footprint of a series of database workloads.

Large database workloads that make heavy use of hugepages can be
particularly exposed to this issue, causing start-up times to be
painfully slow.  This patch reduces the startup time of a 10 Gb Oracle
DB (with ~5000 faults) from 37.5 secs to 25.7 secs.  Larger workloads
will naturally benefit even more.

NOTE:
The only downside to this patch, detected by Joonsoo Kim, is that a
small race is possible in private mappings: A child process (with its
own mm, after cow) can instantiate a page that is already being handled
by the parent in a cow fault.  When low on pages, can trigger spurious
OOMs.  I have not been able to think of a efficient way of handling
this...  but do we really care about such a tiny window? We already
maintain another theoretical race with normal pages.  If not, one
possible way to is to maintain the single hash for private mappings --
any workloads that *really* suffer from this scaling problem should
already use shared mappings.
mm: per-thread vma caching

This patch is a continuation of efforts trying to optimize find_vma(),
avoiding potentially expensive rbtree walks to locate a vma upon faults.
The original approach (https://lkml.org/lkml/2013/11/1/410), where the
largest vma was also cached, ended up being too specific and random,
thus further comparison with other approaches were needed.  There are
two things to consider when dealing with this, the cache hit rate and
the latency of find_vma().  Improving the hit-rate does not necessarily
translate in finding the vma any faster, as the overhead of any fancy
caching schemes can be too high to consider.

We currently cache the last used vma for the whole address space, which
provides a nice optimization, reducing the total cycles in find_vma() by
up to 250%, for workloads with good locality.  On the other hand, this
simple scheme is pretty much useless for workloads with poor locality.
Analyzing ebizzy runs shows that, no matter how many threads are
running, the mmap_cache hit rate is less than 2%, and in many situations
below 1%.

The proposed approach is to replace this scheme with a small per-thread
cache, maximizing hit rates at a very low maintenance cost.
Invalidations are performed by simply bumping up a 32-bit sequence
number.  The only expensive operation is in the rare case of a seq
number overflow, where all caches that share the same address space are
flushed.  Upon a miss, the proposed replacement policy is based on the
page number that contains the virtual address in question.  Concretely,
the following results are seen on an 80 core, 8 socket x86-64 box:

1) System bootup: Most programs are single threaded, so the per-thread
   scheme does improve ~50% hit rate by just adding a few more slots to
   the cache.

+----------------+----------+------------------+
| caching scheme | hit-rate | cycles (billion) |
+----------------+----------+------------------+
| baseline       | 50.61%   | 19.90            |
| patched        | 73.45%   | 13.58            |
+----------------+----------+------------------+

2) Kernel build: This one is already pretty good with the current
   approach as we're dealing with good locality.

+----------------+----------+------------------+
| caching scheme | hit-rate | cycles (billion) |
+----------------+----------+------------------+
| baseline       | 75.28%   | 11.03            |
| patched        | 88.09%   | 9.31             |
+----------------+----------+------------------+

3) Oracle 11g Data Mining (4k pages): Similar to the kernel build workload.

+----------------+----------+------------------+
| caching scheme | hit-rate | cycles (billion) |
+----------------+----------+------------------+
| baseline       | 70.66%   | 17.14            |
| patched        | 91.15%   | 12.57            |
+----------------+----------+------------------+

4) Ebizzy: There's a fair amount of variation from run to run, but this
   approach always shows nearly perfect hit rates, while baseline is just
   about non-existent.  The amounts of cycles can fluctuate between
   anywhere from ~60 to ~116 for the baseline scheme, but this approach
   reduces it considerably.  For instance, with 80 threads:

+----------------+----------+------------------+
| caching scheme | hit-rate | cycles (billion) |
+----------------+----------+------------------+
| baseline       | 1.06%    | 91.54            |
| patched        | 99.97%   | 14.18            |
+----------------+----------+------------------+
m4_defconfig: re-enable oc and this phone is dual core (lol)