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TODO: Multi-threading experiments for better performance on smaller GEMM sizes
Platforms: all, but special focus should be put on mobile OSes (Android...)
where thread scheduling seems to be unfavorable to throughput.
Coding time: Unknown
Experimentation time: XL
Skill required: XL
Relevant file:
internal/multi_thread_gemm.h
The problem, and what we have done about it so far
==================================================
It's easy to get a multi-threaded GEMM implementation to perform well
for large enough GEMM sizes, because then the parallel workloads are large
enough compared to the synchronization overhead. In gemmlowp however,
we are specifically interested in "medium" GEMM sizes, of the order of 100,
which are small enough to make synchronization overhead dominant in many
situations.
We have already implemented some changes that were very effective at getting
good multi-threading benefits for smaller GEMM sizes:
https://github.com/google/gemmlowp/commit/210ac891d6d2d0749f7856103c928d9be70ded94
Let us paste the commit message:
1. Use only N-1 worker threads while the master plays the role
of the Nth worker, where N is the number of cores.
This 1:1 mapping of threads to cores gives much better perf
esp. for not-very-large GEMMs and esp. on Android.
2. Implement waiting by actually busy-waiting for a little while
before eventually falling back to passive waiting. That
ensures that we wake up quickly from short naps, which helps
with not-very-large GEMMs esp. on Android.
These changes revolved around the idea that when the GEMM size is too small to
be efficiently supported by the OS's theading primitives, we can instead
present the OS with a very simple workload: exactly as many threads as there
are CPU cores, and these threads being always busy, never waiting. This makes
it easy for the OS to decide to bring all CPU cores online and give each of our
threads its own CPU core, and occupy it nearly 100% of the time, thus avoiding
to have to wait to get scheduled.
The cost of waiting (or in particular, of locking) is not just the time it
takes; especially on mobile platforms, it is also the side effects of getting
our threads de-scheduled by the OS, of getting CPUs spun down, etc. With that
in mind, anything that can help us avoid waiting/locking in a OS-visible way,
is worth experimenting with.
Other things that would be worth experimenting with
===================================================
Busy-waiting in mutex-locking too
---------------------------------
While we have replaced most of the pthread_cond_wait waiting by busy-waiting
in WaitForVariableChange, on the other hand we are still calling
pthread_mutex_lock in a couple of places outside of WaitForVariableChange.
It might be interesting to avoid that too, by having a mutex-locking
implementation that first spends some time busy-waiting before actually
resorting to calling pthread_mutex_lock.
Minimizing locking
------------------
The inherent synchronization points of the GEMM, which we essentially can't
avoid, are already implemented using WaitForVariableChange, so they are
already using busy-waiting over short periods of time, which is the
best that we can do. On the other hand, we are also using mutex locking
in a couple of places: around updates to the State of worker threads, and
around updates to the counter value in BlockingCounter. The locking done
there is unnecessary: it could be replaced by atomic operations, and in
fact, because our thread structure is so simple and rigid, even atomic
operations might not be needed at all, as long as we ensure basic
memory ordering. A precise understanding of the CPU's memory model
is needed here, and the outcome could depend on the CPU architecture.
Restructuring the GEMM to remove synchronization points
-------------------------------------------------------
Compared to the above ideas, this one is a much bigger departure from
what we are currently doing.
The current structure of our multi-threaded GEMM is:
for_each(slice_of_RHS) {
pack(slice_of_RHS);
for_each(slice_of_LHS) {
do_gemm_on_some_thread(slice_of_LHS, packed_slice_of_RHS)
}
wait_for_all_threads(); // synchronization point
}
Thus we have a synchronization point at the end of each slice of RHS.
The motivation for this design is to have all threads work on a single
large slice of RHS, occupying top-level (shared among cores) CPU cache.
Thus the current approach is optimized for cache-friendliness at the
expense of parallelization. Maybe we should consider amending it
to strike a better balance of cache-friendliness vs. parallelization.
For instance, we could have a "pipeline" where at a given time we have
*two* slices of RHS packed into top-level CPU cache. We would normally
schedule thread tasks to work with the first of these two RHS slices;
whenever a thread task is done, we would immediately give the thread
a new task, and if we are already done with the first RHS slice, we
could then immediately start a task against the second RHS slice.
There could still be some necessary waiting, if one thread is lagging
behind another by more than one full RHS slice; but that should be a
lot better than the current situation, where we wait at the end of
each slice.
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