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folly/ThreadCachedInt.hHigh-performance atomic increment using thread caching.
folly/ThreadCachedInt.h introduces a integer class
designed for high performance increments from multiple threads
simultaneously without loss of precision. It has two read modes,
readFast gives a potentially stale value with one load, and
readFull gives the exact value, but is much slower, as
discussed below.
Increment performance is up to 10x greater than
std::atomic_fetch_add in high contention environments. See
folly/test/ThreadCachedIntTest.h for more comprehensive
benchmarks.
readFast is as fast as a single load.
readFull, on the other hand, requires acquiring a mutex
and iterating through a list to accumulate the values of all the thread
local counters, so is significantly slower than
readFast.
Create an instance and increment it with increment or
the operator overloads. Read the value with readFast for
quick, potentially stale data, or readFull for a more
expensive but precise result. There are additional convenience functions
as well, such as set.
ThreadCachedInt<int64_t> val;
EXPECT_EQ(0, val.readFast());
++val; // increment in thread local counter only
EXPECT_EQ(0, val.readFast()); // increment has not been flushed
EXPECT_EQ(1, val.readFull()); // accumulates all thread local counters
val.set(2);
EXPECT_EQ(2, val.readFast());
EXPECT_EQ(2, val.readFull());folly::ThreadCachedInt uses
folly::ThreadLocal to store thread specific objects that
each have a local counter. When incrementing, the thread local instance
is incremented. If the local counter passes the cache size, the value is
flushed to the global counter with an atomic increment. It is this
global counter that is read with readFast via a simple
load, but will not count any of the updates that haven’t been
flushed.
In order to read the exact value, ThreadCachedInt uses
the extended readAllThreads() API of
folly::ThreadLocal to iterate through all the references to
all the associated thread local object instances. This currently
requires acquiring a global mutex and iterating through the references,
accumulating the counters along with the global counter. This also means
that the first use of the object from a new thread will acquire the
mutex in order to insert the thread local reference into the list. By
default, there is one global mutex per integer type used in
ThreadCachedInt. If you plan on using a lot of
ThreadCachedInts in your application, considering breaking
up the global mutex by introducing additional Tag template
parameters.
set simply sets the global counter value, and marks all
the thread local instances as needing to be reset. When iterating with
readFull, thread local counters that have been marked as
reset are skipped. When incrementing, thread local counters marked for
reset are set to zero and unmarked for reset.
Upon destruction, thread local counters are flushed to the parent so that counts are not lost after increments in temporary threads. This requires grabbing the global mutex to make sure the parent itself wasn’t destroyed in another thread already.
There are of course many ways to skin a cat, and you may notice there
is a partial alternate implementation in
folly/test/ThreadCachedIntTest.cpp that provides similar
performance. ShardedAtomicInt simply uses an array of
std::atomic<int64_t>’s and hashes threads across them
to do low-contention atomic increments, and readFull just
sums up all the ints.
This sounds great, but in order to get the contention low enough to
get similar performance as ThreadCachedInt with 24 threads,
ShardedAtomicInt needs about 2000 ints to hash across. This
uses about 20x more memory, and the lock-free readFull has
to sum up all 2048 ints, which ends up being a about 50x slower than
ThreadCachedInt in low contention situations, which is
hopefully the common case since it’s designed for high-write, low read
access patterns. Performance of readFull is about the same
speed as ThreadCachedInt in high contention
environments.
Depending on the operating conditions, it may make more sense to use
one implementation over the other. For example, a lower contention
environment will probably be able to use a ShardedAtomicInt
with a much smaller array without hurting performance, while improving
memory consumption and perf of readFull.