False Sharing¶
What is False Sharing¶
False sharing is related with cache mechanism of maintaining the data coherence of one cache line stored in multiple CPU’s caches; then academic definition for it is in [1]. Consider a struct with a refcount and a string:
struct foo {
refcount_t refcount;
...
char name[16];
} ____cacheline_internodealigned_in_smp;
Member ‘refcount’(A) and ‘name’(B) _share_ one cache line like below:
+-----------+ +-----------+
| CPU 0 | | CPU 1 |
+-----------+ +-----------+
/ |
/ |
V V
+----------------------+ +----------------------+
| A B | Cache 0 | A B | Cache 1
+----------------------+ +----------------------+
| |
---------------------------+------------------+-----------------------------
| |
+----------------------+
| |
+----------------------+
Main Memory | A B |
+----------------------+
‘refcount’ is modified frequently, but ‘name’ is set once at object creation time and is never modified. When many CPUs access ‘foo’ at the same time, with ‘refcount’ being only bumped by one CPU frequently and ‘name’ being read by other CPUs, all those reading CPUs have to reload the whole cache line over and over due to the ‘sharing’, even though ‘name’ is never changed.
There are many real-world cases of performance regressions caused by false sharing. One of these is a rw_semaphore ‘mmap_lock’ inside mm_struct struct, whose cache line layout change triggered a regression and Linus analyzed in [2].
There are two key factors for a harmful false sharing:
A global datum accessed (shared) by many CPUs
In the concurrent accesses to the data, there is at least one write operation: write/write or write/read cases.
The sharing could be from totally unrelated kernel components, or different code paths of the same kernel component.
False Sharing Pitfalls¶
Back in time when one platform had only one or a few CPUs, hot data members could be purposely put in the same cache line to make them cache hot and save cacheline/TLB, like a lock and the data protected by it. But for recent large system with hundreds of CPUs, this may not work when the lock is heavily contended, as the lock owner CPU could write to the data, while other CPUs are busy spinning the lock.
Looking at past cases, there are several frequently occurring patterns for false sharing:
lock (spinlock/mutex/semaphore) and data protected by it are purposely put in one cache line.
global data being put together in one cache line. Some kernel subsystems have many global parameters of small size (4 bytes), which can easily be grouped together and put into one cache line.
data members of a big data structure randomly sitting together without being noticed (cache line is usually 64 bytes or more), like ‘mem_cgroup’ struct.
Following ‘mitigation’ section provides real-world examples.
False sharing could easily happen unless they are intentionally checked, and it is valuable to run specific tools for performance critical workloads to detect false sharing affecting performance case and optimize accordingly.
How to detect and analyze False Sharing¶
perf record/report/stat are widely used for performance tuning, and once hotspots are detected, tools like ‘perf-c2c’ and ‘pahole’ can be further used to detect and pinpoint the possible false sharing data structures. ‘addr2line’ is also good at decoding instruction pointer when there are multiple layers of inline functions.
perf-c2c can capture the cache lines with most false sharing hits, decoded functions (line number of file) accessing that cache line, and in-line offset of the data. Simple commands are:
$ perf c2c record -ag sleep 3
$ perf c2c report --call-graph none -k vmlinux
When running above during testing will-it-scale’s tlb_flush1 case, perf reports something like:
Total records : 1658231
Locked Load/Store Operations : 89439
Load Operations : 623219
Load Local HITM : 92117
Load Remote HITM : 139
#----------------------------------------------------------------------
4 0 2374 0 0 0 0xff1100088366d880
#----------------------------------------------------------------------
0.00% 42.29% 0.00% 0.00% 0.00% 0x8 1 1 0xffffffff81373b7b 0 231 129 5312 64 [k] __mod_lruvec_page_state [kernel.vmlinux] memcontrol.h:752 1
0.00% 13.10% 0.00% 0.00% 0.00% 0x8 1 1 0xffffffff81374718 0 226 97 3551 64 [k] folio_lruvec_lock_irqsave [kernel.vmlinux] memcontrol.h:752 1
0.00% 11.20% 0.00% 0.00% 0.00% 0x8 1 1 0xffffffff812c29bf 0 170 136 555 64 [k] lru_add_fn [kernel.vmlinux] mm_inline.h:41 1
0.00% 7.62% 0.00% 0.00% 0.00% 0x8 1 1 0xffffffff812c3ec5 0 175 108 632 64 [k] release_pages [kernel.vmlinux] mm_inline.h:41 1
0.00% 23.29% 0.00% 0.00% 0.00% 0x10 1 1 0xffffffff81372d0a 0 234 279 1051 64 [k] __mod_memcg_lruvec_state [kernel.vmlinux] memcontrol.c:736 1
A nice introduction for perf-c2c is [3].
‘pahole’ decodes data structure layouts delimited in cache line granularity. Users can match the offset in perf-c2c output with pahole’s decoding to locate the exact data members. For global data, users can search the data address in System.map.
Possible Mitigations¶
False sharing does not always need to be mitigated. False sharing mitigations should balance performance gains with complexity and space consumption. Sometimes, lower performance is OK, and it’s unnecessary to hyper-optimize every rarely used data structure or a cold data path.
False sharing hurting performance cases are seen more frequently with core count increasing. Because of these detrimental effects, many patches have been proposed across variety of subsystems (like networking and memory management) and merged. Some common mitigations (with examples) are:
Separate hot global data in its own dedicated cache line, even if it is just a ‘short’ type. The downside is more consumption of memory, cache line and TLB entries.
Commit 91b6d3256356 (“net: cache align tcp_memory_allocated, tcp_sockets_allocated”)
Reorganize the data structure, separate the interfering members to different cache lines. One downside is it may introduce new false sharing of other members.
Commit 802f1d522d5f (“mm: page_counter: re-layout structure to reduce false sharing”)
Replace ‘write’ with ‘read’ when possible, especially in loops. Like for some global variable, use compare(read)-then-write instead of unconditional write. For example, use:
if (!test_bit(XXX)) set_bit(XXX);instead of directly “set_bit(XXX);”, similarly for atomic_t data:
if (atomic_read(XXX) == AAA) atomic_set(XXX, BBB);Commit 7b1002f7cfe5 (“bcache: fixup bcache_dev_sectors_dirty_add() multithreaded CPU false sharing”)
Commit 292648ac5cf1 (“mm: gup: allow FOLL_PIN to scale in SMP”)
Turn hot global data to ‘per-cpu data + global data’ when possible, or reasonably increase the threshold for syncing per-cpu data to global data, to reduce or postpone the ‘write’ to that global data.
Commit 520f897a3554 (“ext4: use percpu_counters for extent_status cache hits/misses”)
Commit 56f3547bfa4d (“mm: adjust vm_committed_as_batch according to vm overcommit policy”)
Surely, all mitigations should be carefully verified to not cause side effects. To avoid introducing false sharing when coding, it’s better to:
Be aware of cache line boundaries
Group mostly read-only fields together
Group things that are written at the same time together
Separate frequently read and frequently written fields on different cache lines.
and better add a comment stating the false sharing consideration.
One note is, sometimes even after a severe false sharing is detected and solved, the performance may still have no obvious improvement as the hotspot switches to a new place.
Miscellaneous¶
One open issue is that kernel has an optional data structure randomization mechanism, which also randomizes the situation of cache line sharing of data members.