207 lines
9.2 KiB
ReStructuredText
207 lines
9.2 KiB
ReStructuredText
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.. SPDX-License-Identifier: GPL-2.0
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=============
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False Sharing
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=============
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What is False Sharing
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=====================
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False sharing is related with cache mechanism of maintaining the data
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coherence of one cache line stored in multiple CPU's caches; then
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academic definition for it is in [1]_. Consider a struct with a
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refcount and a string::
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struct foo {
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refcount_t refcount;
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...
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char name[16];
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} ____cacheline_internodealigned_in_smp;
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Member 'refcount'(A) and 'name'(B) _share_ one cache line like below::
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+-----------+ +-----------+
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| CPU 0 | | CPU 1 |
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+-----------+ +-----------+
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/ |
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/ |
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V V
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+----------------------+ +----------------------+
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| A B | Cache 0 | A B | Cache 1
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+----------------------+ +----------------------+
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---------------------------+------------------+-----------------------------
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+----------------------+
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+----------------------+
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Main Memory | A B |
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+----------------------+
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'refcount' is modified frequently, but 'name' is set once at object
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creation time and is never modified. When many CPUs access 'foo' at
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the same time, with 'refcount' being only bumped by one CPU frequently
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and 'name' being read by other CPUs, all those reading CPUs have to
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reload the whole cache line over and over due to the 'sharing', even
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though 'name' is never changed.
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There are many real-world cases of performance regressions caused by
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false sharing. One of these is a rw_semaphore 'mmap_lock' inside
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mm_struct struct, whose cache line layout change triggered a
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regression and Linus analyzed in [2]_.
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There are two key factors for a harmful false sharing:
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* A global datum accessed (shared) by many CPUs
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* In the concurrent accesses to the data, there is at least one write
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operation: write/write or write/read cases.
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The sharing could be from totally unrelated kernel components, or
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different code paths of the same kernel component.
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False Sharing Pitfalls
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======================
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Back in time when one platform had only one or a few CPUs, hot data
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members could be purposely put in the same cache line to make them
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cache hot and save cacheline/TLB, like a lock and the data protected
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by it. But for recent large system with hundreds of CPUs, this may
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not work when the lock is heavily contended, as the lock owner CPU
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could write to the data, while other CPUs are busy spinning the lock.
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Looking at past cases, there are several frequently occurring patterns
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for false sharing:
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* lock (spinlock/mutex/semaphore) and data protected by it are
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purposely put in one cache line.
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* global data being put together in one cache line. Some kernel
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subsystems have many global parameters of small size (4 bytes),
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which can easily be grouped together and put into one cache line.
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* data members of a big data structure randomly sitting together
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without being noticed (cache line is usually 64 bytes or more),
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like 'mem_cgroup' struct.
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Following 'mitigation' section provides real-world examples.
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False sharing could easily happen unless they are intentionally
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checked, and it is valuable to run specific tools for performance
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critical workloads to detect false sharing affecting performance case
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and optimize accordingly.
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How to detect and analyze False Sharing
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========================================
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perf record/report/stat are widely used for performance tuning, and
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once hotspots are detected, tools like 'perf-c2c' and 'pahole' can
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be further used to detect and pinpoint the possible false sharing
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data structures. 'addr2line' is also good at decoding instruction
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pointer when there are multiple layers of inline functions.
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perf-c2c can capture the cache lines with most false sharing hits,
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decoded functions (line number of file) accessing that cache line,
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and in-line offset of the data. Simple commands are::
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$ perf c2c record -ag sleep 3
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$ perf c2c report --call-graph none -k vmlinux
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When running above during testing will-it-scale's tlb_flush1 case,
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perf reports something like::
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Total records : 1658231
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Locked Load/Store Operations : 89439
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Load Operations : 623219
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Load Local HITM : 92117
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Load Remote HITM : 139
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#----------------------------------------------------------------------
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4 0 2374 0 0 0 0xff1100088366d880
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#----------------------------------------------------------------------
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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
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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
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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
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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
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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
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A nice introduction for perf-c2c is [3]_.
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'pahole' decodes data structure layouts delimited in cache line
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granularity. Users can match the offset in perf-c2c output with
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pahole's decoding to locate the exact data members. For global
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data, users can search the data address in System.map.
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Possible Mitigations
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====================
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False sharing does not always need to be mitigated. False sharing
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mitigations should balance performance gains with complexity and
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space consumption. Sometimes, lower performance is OK, and it's
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unnecessary to hyper-optimize every rarely used data structure or
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a cold data path.
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False sharing hurting performance cases are seen more frequently with
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core count increasing. Because of these detrimental effects, many
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patches have been proposed across variety of subsystems (like
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networking and memory management) and merged. Some common mitigations
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(with examples) are:
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* Separate hot global data in its own dedicated cache line, even if it
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is just a 'short' type. The downside is more consumption of memory,
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cache line and TLB entries.
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- Commit 91b6d3256356 ("net: cache align tcp_memory_allocated, tcp_sockets_allocated")
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* Reorganize the data structure, separate the interfering members to
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different cache lines. One downside is it may introduce new false
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sharing of other members.
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- Commit 802f1d522d5f ("mm: page_counter: re-layout structure to reduce false sharing")
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* Replace 'write' with 'read' when possible, especially in loops.
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Like for some global variable, use compare(read)-then-write instead
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of unconditional write. For example, use::
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if (!test_bit(XXX))
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set_bit(XXX);
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instead of directly "set_bit(XXX);", similarly for atomic_t data::
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if (atomic_read(XXX) == AAA)
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atomic_set(XXX, BBB);
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- Commit 7b1002f7cfe5 ("bcache: fixup bcache_dev_sectors_dirty_add() multithreaded CPU false sharing")
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- Commit 292648ac5cf1 ("mm: gup: allow FOLL_PIN to scale in SMP")
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* Turn hot global data to 'per-cpu data + global data' when possible,
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or reasonably increase the threshold for syncing per-cpu data to
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global data, to reduce or postpone the 'write' to that global data.
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- Commit 520f897a3554 ("ext4: use percpu_counters for extent_status cache hits/misses")
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- Commit 56f3547bfa4d ("mm: adjust vm_committed_as_batch according to vm overcommit policy")
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Surely, all mitigations should be carefully verified to not cause side
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effects. To avoid introducing false sharing when coding, it's better
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to:
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* Be aware of cache line boundaries
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* Group mostly read-only fields together
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* Group things that are written at the same time together
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* Separate frequently read and frequently written fields on
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different cache lines.
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and better add a comment stating the false sharing consideration.
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One note is, sometimes even after a severe false sharing is detected
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and solved, the performance may still have no obvious improvement as
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the hotspot switches to a new place.
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Miscellaneous
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=============
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One open issue is that kernel has an optional data structure
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randomization mechanism, which also randomizes the situation of cache
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line sharing of data members.
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.. [1] https://en.wikipedia.org/wiki/False_sharing
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.. [2] https://lore.kernel.org/lkml/CAHk-=whoqV=cX5VC80mmR9rr+Z+yQ6fiQZm36Fb-izsanHg23w@mail.gmail.com/
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.. [3] https://joemario.github.io/blog/2016/09/01/c2c-blog/
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