[patch 00/10] SLUB: SMP regression tests on Dual Xeon E5345 (8p) and new performance patches

Christoph Lameter
2007-10-28T03:34:06Z

Recent reports from Intel indicated that there were regression on
SMP benchmarks vs. SLAB. This is a discussion of performance results
and some patches are attached to fix various issues.
The patches are also available via git pull from

git://git.kernel.org/pub/scm/linux/kernel/git/christoph/slab.git performance

SLAB and SLUB are fundamentally different architectures. SLAB batches multiple
objects on queues. The movement between queues is protected by a single lock
(at least in the SMP configuration). SLAB can move an arbitrary amount of
objects by taking the list_lock. Integration of objects into the slabs
is deferred as much as possible while objects circle on various slab
queues.

SLUB's design is to directly integrate or extract the objects from the
slabs without going through intermediate queues. Thus the overhead is
eliminated. SLUB has a lock in each slab allowing fine grained locking.
Centralized locks are rarely taken. SLUB cannot batch objects to
optimize lock use. Instead a whole slab is assigned to a processor.
Allocations and frees can then occur from the CPU slab without taking
the slab lock. However, that is limited to the number of objects that
fit into a slab in contrast to SLAB which can extract objects from
multiple slabs and put them on a per CPU queue.

If SLUB is freeing an objects then the per CPU slab can only be used if the
object is part of the CPU slab. This is usually the case for short lived
allocations. Long lived allocations and objects allocated on other CPUs
will need to use the slow path where the slab_lock must be taken to
synchronize the free. This makes the slab_free() path particularly
problematic in SMP contexts.

Optimization in SLUB is therefore mainly optimization of locking
and of the execution code paths. The following patches optimize
locking further by using a cmpxchg_local in the fast path and
by avoiding stores to page struct fields etc to address regressions
that we see under SMP.

Another fundamental distinction between SLAB and SLUB is that SLAB
was designed with SMP in mind. NUMA was a later add-on that added
a significant complexity. SLUB was written for NUMA. NUMA support is
native. The same slab_free() path that is problematic under SMP is
effectively dealing with the alien cache problem that SLAB has under NUMA
and is increasing performance of remote free operations significantly.
The cpu_slab concept makes the determination of NUMA locality of objects
simpler since we can match on the page that an object belongs to and move
the whole page of objects in a NUMA aware fashion instead of the individual
objects in the queues of SLAB.

The fine grained locking is also important for SMP system with a large number
of processors. SLAB can put lots of objects on its queues. However, current
processors can take a large number of objects off the queues in a short
time period. As a result we see significant lock contention using SLAB during
parallel operations on the 8p SMP machine that is investigated here. SLAB has
less problems scaling on NUMA with a more limited number of processors per node
because SLAB will then use node based locks instead of global locks.

Tests were run with 4 different kernels:

SLAB = 2.6.24-rc1 configured to run SLAB
SLUB = 2.6.24-rc1 configured to run SLUB
SLUB+ = 2.6.24-rc1 patched with the following patches.
SLUB-o = SLUB+ booted with slub_max_order=3 slub_min_objects=20

The SLAB tests result in the baseline to work against. SLUB is the
current state of 2.6.24. SLUB+ is an version of SLUB that was optimized
to run on the 8p SMP box after observing some of the performance issues.
SLUB-o is useful to see what effect the use of higher order pages has
on performance.

All tests are done by running 10000 operations on each processor. The time
needed is measured using TSC times tamps.

All measurements are in cycle counts. The higher the cycle count the more
time the allocator needs to perform an operation. The lower the count the
better the performance of the allocator.

Test A: Single threaded kmalloc
===============================

A single cpu is running and is allocating 10000 objects of various
sizes.

SLAB SLUB SLUB+ SLUB-o
8 96 86 45 44 2 *
16 84 92 49 48 ++++
32 84 106 61 59 +++
64 102 129 82 88 ++
128 147 226 188 181 --
256 200 248 207 285 -
512 300 301 260 209 ++
1024 416 440 398 264 ++
2048 720 542 530 390 +++
4096 1254 342 342 336 3 *

SLUB passes 4k allocations directly through to the page allocator which
is more efficient at handling page sized allocations than SLABs handling
of them. 4k (or page sized) allocations will be special throughout these
tests.

We see a performance degradation vs. SLAB in the middle range that
is reduced by the patch set.

The cmpxchg_local operation used in SLUB+ effectively cuts the cycles
spend on the fast path in half. However, SLUB has to use its slow path
more frequently than SLAB. So the advantage gradually disappears at 128
bytes. The frequency of slow path use increases for SLAB when we go
to higher sizes since SLAB reduces the size of the objects queues for
larger sizes. SLUB's slow path is more effective and so there is a slight
win starting at 512 bytes size.

Allowing a larger allocation order in SLUB-o only has a beneficial effect
above 512 bytes but there it gives SLUB a significant advantage.

Test B: Single threaded kfree
=============================

A single cpu is freeing the objects allocated during test A.

SLAB SLUB SLUB+ SLUB-o
8 129 170 128 127 =
16 127 173 132 131 =
32 127 177 135 136 -
64 121 182 138 144 -
128 134 195 154 156 --
256 167 268 233 197 ---
512 329 408 375 273 =
1024 432 518 448 343 -
2048 622 596 525 395 ++
4096 896 342 333 332 2 *

For smaller and larger sizes the performance is equal or better but
in the mid range from 32 bytes to 256 bytes we have regressions
that are only partially addressed by the code path optimizations
or the higher order allocs.

The problem for SLUB here is that the slab_free() fast path cannot be used.
10000 objects are way beyond what fits into a single page and thus we
always operate on the slow path. Adrian and I have tinkered around with
adding some queueing for freeing to SLUB but that would add SLAB concepts
to SLUB making it more complex. Maybe we can avoid that.

Test C: Short lived object: Alloc and immediately free
======================================================

On a single cpu an object is allocated and then immediately freed.
This is mainly useful to show the fastest alloc/free sequence possible.
It shows how fast the fast path can get.

SLAB SLUB SLUB+ SLUB-o
137-146 151 68-72 68-74 2 *

The cycle counts vary only slightly for different sizes, so there is no
use in displaying the whole table. The numbers show that the SLUB fast path
is a tad slower than SLAB. However, the cmpxchg_local optimizations
cut the cycle count in half and at that point SLUB becomes twice as fast
as SLAB. So for relatively short lived objects that can be freed to the
cpu_slab SLUB will be twice as fast.

Test D: Concurrent kmalloc on 8 processors
==========================================

This test is running 10000 allocations concurrently on all processors to see
how lock contention influences the allocator speed.

SLAB SLUB SLUB+ SLUB-o
8 1177 101 66 64 > 10 *
16 1038 117 92 85 > 10 *
32 1049 151 116 131 9 *
64 1680 220 211 200 7 *
128 2964 360 365 363 7 *
256 6228 791 786 1024 7 *
512 12914 1100 1103 1122 > 10 *
1024 26309 1535 1509 1430 > 10 *
2048 52237 6372 6455 2349 7 *
4096 64661 11420 11678 11999 6 *

This shows the effect of SLUBs finer grained locking. SLAB list_lock
contention becomes a major factor on an 8p system. SLUB rarely takes
global locks and thus is always more than 6 times faster than SLAB.
One may be able to address this issue by increasing the SLAB queue
sizes for 8p systems. However, these queues are per cpu so the amount
of memory caught in queues grows with the increase in processor numbers.
The interrupt hold offs grow if the queue size is increased and the processing
cost in the cache reaper too (which cause lots of trouble for MPI jobs f.e.).

Test E: Short lived object: Concurrent alloc and free immediately
=================================================================

Basically the same test as test C but with concurrent allocations. This
verifies that the fast paths of the allocators are decoupled.

SLAB SLUB SLUB+ SLUB-o
136-149 151-153 68-72 69-72 2 *

Same results as before. cmpxchg_local doubles the speed of SLUB.

Test F: Remote free of 70000 objects from a single processor
============================================================

This is a test to simulate the problem that Intel saw. Objects are
allocated on 7 processors and then the 8th processor frees them all
All frees are remote and all objects are cache cold.

SLAB SLUB SLUB+ SLUB-o
8 1120 1309 1046 1047 +
16 1118 1414 1157 1157 =
32 1124 1615 1359 1359 -
64 1619 2038 1732 1722 -
128 1892 2451 2247 2251 --
256 2144 2869 2658 2565 --
512 3021 3329 3123 2751 -
1024 3698 3993 3786 2889 ++
2048 5708 4469 4231 3413 ++
4096 9188 5486 5524 5525 ++++

Again some regressions for SLUB in the middle range.
The code path optimizations and the removal of atomic ops in
SLUB+ closes the gap for many sizes and makes SLUB+ in some
sizes superior to SLAB. This is likely effective in dealing
with the performance problem that Intel saw.

The higher order SLUB reduces the regression even more for 512
to 2048 bytes.

Further possible optimizations:
===============================

I would like to with the basic idea of SLUB and avoid adding queues.
I think on average one will find after these patches that the performance
of SLUB is at equal to SLAB even on SMP. SLAB has some issues with lock
contention for higher cpu counts. So SLUB will become better as
we add more CPUs.

There are a couple of additional optimizations that could be done without
having to resort to queueing objects:

1. Get an IA64 style per cpu area working on x86_64 that maps the per cpu area
at the same address for each processor. If the per cpu structure is always
at the same address on all processors then we can simply forget about
disabling preemption in the fast path (the cmpxchg_local operates on whatever
current cpu structure we are on) and can avoid to calculate the
address of the per cpu structure in the fast path. This is likely
to increase performance by another 30% (The method could also be used
to optimize the page allocator BTW).

2. One could locklessly free objects into non cpu slabs using a cmpxchg
thereby avoiding the interrupt disable / enable in the slow slab_free()
path. There are problems with determining when to free a slab and how to
deal with the races in relation to adding partial slabs to the lists.
Got a draft here but I am not sure if its worth continuing to work on it.

3. Higher order allocs would be useful to increase speed in object size ranges
from 512 - 2048. But the performance gains are likely offset to a bit by
the slowness of the page allocator in providing higher order pages. Zone
locks need to be taken and the higher order pages are extracted directly
from the buddy lists. Optimizing the page allocator to serve higher order
pages more effectively may increase SLUB performance.

NUMA tests:
-----------

The following tests may not be interesting. It verifies that the
patch set does not impact the already good NUMA performance of SLUB.

IA64 8p 4 node NUMA comparison
==============================

The test was performed on a NUMA system with 2p per node. So we have 4
nodes and 8p. In that case the density of CPUs per node is just 2. SLAB
manages structures per node. Only having 2 nodes per cpu cuts down on the
overhead of concurrent allocations. There is no global lock anymore like
under SMP. SLAB is now almost competitive with the concurrent allocations.

IA64 has a 16k page size and no fast cmpxchg_local. So we cannot use the
version of the SLUB fast path that avoids disabling interrupts. However, the
large page size means that lots of objects can be handled within a single
cpu slab. The test with higher order pages was omitted since the bas page
size is already large.

Test A: Single thread test kmalloc
==================================

SLAB SLUB SLUB+
8 121 70 84 +++
16 98 91 87 +
32 93 98 98 =
64 94 111 110 -
128 133 123 132 =
256 144 156 156 -
512 180 181 175 +
1024 348 263 263 ++
2048 348 310 306 +
4096 490 322 328 ++
8192 810 387 389 2 *
16384 1463 594 592 3 *

Small regressions between 64 and 256 byte object size. Overall SLUB is
faster and it was faster even without the performance improvements.

Test B: Single threaded kfree
=============================

SLAB SLUB SLUB+
8 173 115 103 +++
16 172 111 94 +++
32 172 116 100 +++
64 172 119 103 +++
128 175 123 106 +++
256 187 178 141 ++
512 241 310 313 --
1024 221 382 374 --
2048 321 405 403 --
4096 398 407 413 -
8192 608 452 452 +++
16384 977 672 674 ++++

The alien cache overhead hits SLAB for many sizes. Regressions
for 512-4096 byte sizes. The optimizations in the slab_free path
have helped somewhat to make SLUB faster.

Test C: Single threaded short lived object: Alloc/free
======================================================
SLAB SLUB SLUB+
114-142 104-115 101-113 +

The patch set has reduced the cycle count by a few cycles.

SLUB's alloc and free path is simply faster since the NUMA handling overhead
is less if the handling is performed on a slab level (SLUB) and not on the
object level (SLAB).

Test D: Concurrent allocations on 8 CPUs
========================================

SLAB SLUB SLUB+
8 156 94 89 ++++
16 123 101 98 +++
32 127 110 109 ++
64 133 129 127 =
128 183 168 160 +
256 229 212 217 +
512 371 332 327 +
1024 530 555 560 -
2048 1059 1005 957 +
4096 3601 870 824 ++++
8192 7123 1131 1084 7 *
16384 12836 1468 1439 9 *

Same picture as before: SLUB is way better for small and large objects.
Medium range is weak.

However, SLAB is scales much better when it has a lock for only 2
processors instead of 8.

Test E: Short lived objects: Alloc/free concurrently
====================================================

SLAB SLUB SLUB+
116-143 106-117 103-114 +

Same result as for single threaded operations.

Test F: Remote free of 70000 objects from a single processor
============================================================
The objects were allocated on 7 other CPUs.
All frees are remote.

SLAB SLUB SLUB+
8 3806 1435 1335 3 *
16 3836 1713 1620 2 *
32 3836 2298 2207 ++++
64 3825 3441 3373 +++
128 5943 5713 5666 ++
256 5912 5676 5636 +
512 6126 5403 5349 ++
1024 6291 5300 5257 ++
2048 6006 5559 5531 +
4096 6863 5703 5684 ++
8192 8935 6031 6013 +++
16384 13208 8012 8013 ++++

The alien cache handling hurts SLAB for remote frees. For remote
frees under NUMA SLUB is much better.

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