[Haskell-cafe] Best ways to achieve throughput, for large M:N ratio of STM threads, with hot TVar updates?

Ryan Yates fryguybob at gmail.com
Fri Jul 24 18:02:17 UTC 2020

To be clear, I was trying to refer to Linux `perf` [^1].  Sampling based
profiling can do a good job with concurrent and parallel programs where
other methods are problematic.  For instance,
 changing the size of heap objects can drastically change cache performance
and completely different behavior can show up.

[^1]: https://en.wikipedia.org/wiki/Perf_(Linux)

The spinning in `readTVar` should always be very short and it typically
shows up as intensive CPU use, though it may not be high energy use with
`pause` in the loop on x86 (looks like we don't have it [^2], I thought we
did, but maybe that was only in some of my code... )

[^2]: https://github.com/ghc/ghc/blob/master/rts/STM.c#L1275

All that to say, I doubt that you are spending much time spinning (but it
would certainly be interesting to know if you are!  You would see `perf`
attribute a large amount of time to `read_current_value`).  The amount of
code to execute for commit (the time when locks are held) is always much
shorter than it takes to execute the transaction body.  As you add more
conflicting threads this gets worse of course as commits sequence.

The code you have will count commits of executions of `retry`.  Note that
`retry` is a user level idea, that is, you are counting user level
*explicit* retries.  This is different from a transaction failing to commit
and starting again.  These are invisible to the user.  Also using your
trace will convert `retry` from the efficient wake on write implementation,
to an active retry that will always attempt again.  We don't have cheap
logging of transaction aborts in GHC, but I have built such logging in my
work.  You can observe these aborts with a debugger by looking for
execution of this line:



On Fri, Jul 24, 2020 at 12:35 PM Compl Yue <compl.yue at icloud.com> wrote:

> I'm not familiar with profiling GHC yet, may need more time to get myself
> proficient with it.
> And a bit more details of my test workload for diagnostic: the db clients
> are Python processes from a cluster of worker nodes, consulting the db
> server to register some path for data files, under a data dir within a
> shared filesystem, then mmap those data files and fill in actual array
> data. So the db server don't have much computation to perform, but puts the
> data file path into a global index, which at the same validates its
> uniqueness. As there are many client processes trying to insert one meta
> data record concurrently, with my naive implementation, the global index's
> TVar will almost always in locked state by one client after another, from a
> queue never fall empty.
> So if `readTVar` should spinning waiting, I doubt the threads should
> actually make high CPU utilization, because at any instant of time, all
> threads except the committing one will be doing that one thing.
> And I have something in my code to track STM retry like this:
> ```
> -- blocking wait not expected, track stm retries explicitly
> trackSTM :: Int -> IO (Either () a)
> trackSTM !rtc = do
> when -- todo increase the threshold of reporting?
> (rtc > 0) $ do
> -- trace out the retries so the end users can be aware of them
> tid <- myThreadId
> trace
> ( "πŸ”™\n"
> <> show callCtx
> <> "πŸŒ€ "
> <> show tid
> <> " stm retry #"
> <> show rtc
> )
> $ return ()
> atomically ((Just <$> stmJob) `orElse` return Nothing) >>= \case
> Nothing -> -- stm failed, do a tracked retry
> trackSTM (rtc + 1)
> Just ... -> ...
> ```
> No such trace msg fires during my test, neither in single thread run, nor
> in runs with pressure. I'm sure this tracing mechanism works, as I can see
> such traces fire, in case e.g. posting a TMVar to a TQueue for some other
> thread to fill it, then read the result out, if these 2 ops are composed
> into a single tx, then of course it's infinite retry loop, and a sequence
> of such msgs are logged with ever increasing rtc #.
> So I believe no retry has ever been triggered.
> What can going on there?
> On 2020/7/24 δΈ‹εˆ11:46, Ryan Yates wrote:
> > Then to explain the low CPU utilization (~10%), am I right to understand
> it as that upon reading a TVar locked by another committing tx, a
> lightweight thread will put itself into `waiting STM` and descheduled
> state, so the CPUs can only stay idle as not so many threads are willing to
> proceed?
> Since the commit happens in finite steps, the expectation is that the lock
> will be released very soon.  Given this when the body of a transaction
> executes `readTVar` it spins (active CPU!) until the `TVar` is observed
> unlocked.  If a lock is observed while commiting, it immediately starts the
> transaction again from the beginning.  To get the behavior of suspending a
> transaction you have to successfully commit a transaction that executed
> `retry`.  Then the transaction is put on the wakeup lists of its read set
> and subsequent commits will wake it up if its write set overlaps.
> I don't think any of these things would explain low CPU utilization.  You
> could try running with `perf` and see if lots of time is spent in some
> recognizable part of the RTS.
> Ryan
> On Fri, Jul 24, 2020 at 11:22 AM Compl Yue <compl.yue at icloud.com> wrote:
>> Thanks very much for the insightful information Ryan! I'm glad my suspect
>> was wrong about the Haskell scheduler:
>> > The Haskell capability that is committing a transaction will not yield
>> to another Haskell thread while it is doing the commit.  The OS thread may
>> be preempted, but once commit starts the haskell scheduler is not invoked
>> until after locks are released.
>> So best effort had already been made in GHC and I just need to cooperate
>> better with its design. Then to explain the low CPU utilization (~10%), am
>> I right to understand it as that upon reading a TVar locked by another
>> committing tx, a lightweight thread will put itself into `waiting STM` and
>> descheduled state, so the CPUs can only stay idle as not so many threads
>> are willing to proceed?
>> Anyway, I see light with better data structures to improve my situation,
>> let me try them and report back. Actually I later changed `TVar (HaskMap k
>> v)` to be `TVar (HashMap k Int)` where the `Int` being array index into
>> `TVar (Vector (TVar (Maybe v)))`, in pursuing insertion order preservation
>> semantic of dict entries (like that in Python 3.7+), then it's very hopeful
>> to incorporate stm-containers' Map or ttrie to approach free of contention.
>> Thanks with regards,
>> Compl
>> On 2020/7/24 δΈ‹εˆ10:03, Ryan Yates wrote:
>> Hi Compl,
>> Having a pool of transaction processing threads can be helpful in a
>> certain way.  If the body of the transaction takes more time to execute
>> then the Haskell thread is allowed and it yields, the suspended thread
>> won't get in the way of other thread, but when it is rescheduled, will have
>> a low probability of success.  Even worse, it will probably not discover
>> that it is doomed to failure until commit time.  If transactions are more
>> likely to reach commit without yielding, they are more likely to succeed.
>> If the transactions are not conflicting, it doesn't make much difference
>> other than cache churn.
>> The Haskell capability that is committing a transaction will not yield to
>> another Haskell thread while it is doing the commit.  The OS thread may be
>> preempted, but once commit starts the haskell scheduler is not invoked
>> until after locks are released.
>> To get good performance from STM you must pay attention to what TVars are
>> involved in a commit.  All STM systems are working under the assumption of
>> low contention, so you want to minimize "false" conflicts (conflicts that
>> are not essential to the computation).    Something like `TVar (HashMap k
>> v)` will work pretty well for a low thread count, but every transaction
>> that writes to that structure will be in conflict with every other
>> transaction that accesses it.  Pushing the `TVar` into the nodes of the
>> structure reduces the possibilities for conflict, while increasing the
>> amount of bookkeeping STM has to do.  I would like to reduce the cost of
>> that bookkeeping using better structures, but we need to do so without
>> harming performance in the low TVar count case.  Right now it is optimized
>> for good cache performance with a handful of TVars.
>> There is another way to play with performance by moving work into and out
>> of the transaction body.  A transaction body that executes quickly will
>> reach commit faster.  But it may be delaying work that moves into another
>> transaction.  Forcing values at the right time can make a big difference.
>> Ryan
>> On Fri, Jul 24, 2020 at 2:14 AM Compl Yue via Haskell-Cafe <
>> haskell-cafe at haskell.org> wrote:
>>> Thanks Chris, I confess I didn't pay enough attention to STM specialized
>>> container libraries by far, I skimmed through the description of
>>> stm-containers and ttrie, and feel they would definitely improve my code's
>>> performance in case I limit the server's parallelism within hardware
>>> capabilities. That may because I'm still prototyping the api and
>>> infrastructure for correctness, so even `TVar (HashMap k v)` performs okay
>>> for me at the moment, only if at low contention (surely there're plenty of
>>> CPU cycles to be optimized out in next steps). I model my data after graph
>>> model, so most data, even most indices are localized to nodes and edges,
>>> those can be manipulated without conflict, that's why I assumed I have a
>>> low contention use case since the very beginning - until I found there are
>>> still (though minor) needs for global indices to guarantee global
>>> uniqueness, I feel faithful with stm-containers/ttrie to implement a more
>>> scalable global index data structure, thanks for hinting me.
>>> So an evident solution comes into my mind now, is to run the server with
>>> a pool of tx processing threads, matching number of CPU cores, client RPC
>>> requests then get queued to be executed in some thread from the pool. But
>>> I'm really fond of the mechanism of M:N scheduler which solves
>>> massive/dynamic concurrency so elegantly. I had some good result with Go in
>>> this regard, and see GHC at par in doing this, I don't want to give up this
>>> enjoyable machinery.
>>> But looked at the stm implementation in GHC, it seems written TVars are
>>> exclusively locked during commit of a tx, I suspect this is the culprit
>>> when there're large M lightweight threads scheduled upon a small N hardware
>>> capabilities, that is when a lightweight thread yield control during an stm
>>> transaction commit, the TVars it locked will stay so until it's scheduled
>>> again (and again) till it can finish the commit. This way, descheduled
>>> threads could hold live threads from progressing. I haven't gone into more
>>> details there, but wonder if there can be some improvement for GHC RTS to
>>> keep an stm committing thread from descheduled, but seemingly that may
>>> impose more starvation potential; or stm can be improved to have its TVar
>>> locks preemptable when the owner trec/thread is in descheduled state?
>>> Neither should be easy but I'd really love massive lightweight threads
>>> doing STM practically well.
>>> Best regards,
>>> Compl
>>> On 2020/7/24 上午12:57, Christopher Allen wrote:
>>> It seems like you know how to run practical tests for tuning thread
>>> count and contention for throughput. Part of the reason you haven't gotten
>>> a super clear answer is "it depends." You give up fairness when you use STM
>>> instead of MVars or equivalent structures. That means a long running
>>> transaction might get stampeded by many small ones invalidating it over and
>>> over. The long-running transaction might never clear if the small
>>> transactions keep moving the cheese. I mention this because transaction
>>> runtime and size and count all affect throughput and latency. What might be
>>> ideal for one pattern of work might not be ideal for another. Optimizing
>>> for overall throughput might make the contention and fairness problems
>>> worse too. I've done practical tests to optimize this in the past, both for
>>> STM in Haskell and for RDBMS workloads.
>>> The next step is sometimes figuring out whether you really need a data
>>> structure within a single STM container or if perhaps you can break up your
>>> STM container boundaries into zones or regions that roughly map onto update
>>> boundaries. That should make the transactions churn less. On the outside
>>> chance you do need to touch more than one container in a transaction, well,
>>> they compose.
>>> e.g. https://hackage.haskell.org/package/stm-containers
>>> https://hackage.haskell.org/package/ttrie
>>> It also sounds a bit like your question bumps into Amdahl's Law a bit.
>>> All else fails, stop using STM and find something more tuned to your
>>> problem space.
>>> Hope this helps,
>>> Chris Allen
>>> On Thu, Jul 23, 2020 at 9:53 AM YueCompl via Haskell-Cafe <
>>> haskell-cafe at haskell.org> wrote:
>>>> Hello Cafe,
>>>> I'm working on an in-memory database, in Client/Server mode I just let
>>>> each connected client submit remote procedure call running in its dedicated
>>>> lightweight thread, modifying TVars in RAM per its business needs, then in
>>>> case many clients connected concurrently and trying to insert new data, if
>>>> they are triggering global index (some TVar) update, the throughput would
>>>> drop drastically. I reduced the shared state to a simple int counter by
>>>> TVar, got same symptom. While the parallelism feels okay when there's no
>>>> hot TVar conflicting, or M is not much greater than N.
>>>> As an empirical test workload, I have a `+RTS -N10` server process, it
>>>> handles 10 concurrent clients okay, got ~5x of single thread throughput;
>>>> but in handling 20 concurrent clients, each of the 10 CPUs can only be
>>>> driven to ~10% utilization, the throughput seems even worse than single
>>>> thread. More clients can even drive it thrashing without much  progressing.
>>>>  I can understand that pure STM doesn't scale well after reading [1],
>>>> and I see it suggested [7] attractive and planned future work toward that
>>>> direction.
>>>> But I can't find certain libraries or frameworks addressing large M
>>>> over small N scenarios, [1] experimented with designated N parallelism, and
>>>> [7] is rather theoretical to my empirical needs.
>>>> Can you direct me to some available library implementing the
>>>> methodology proposed in [7] or other ways tackling this problem?
>>>> I think the most difficult one is that a transaction should commit with
>>>> global indices (with possibly unique constraints) atomically updated, and
>>>> rollback with any violation of constraints, i.e. transactions have to cover
>>>> global states like indices. Other problems seem more trivial than this.
>>>> Specifically, [7] states:
>>>> > It must be emphasized that all of the mechanisms we deploy originate,
>>>> in one form or another, in the database literature from the 70s and 80s.
>>>> Our contribution is to adapt these techniques to software transactional
>>>> memory, providing more effective solutions to important STM problems than
>>>> prior proposals.
>>>> I wonder any STM based library has simplified those techniques to be
>>>> composed right away? I don't really want to implement those mechanisms by
>>>> myself, rebuilding many wheels from scratch.
>>>> Best regards,
>>>> Compl
>>>> [1] Comparing the performance of concurrent linked-list implementations
>>>> in Haskell
>>>> https://simonmar.github.io/bib/papers/concurrent-data.pdf
>>>> [7] M. Herlihy and E. Koskinen. Transactional boosting: a methodology
>>>> for highly-concurrent transactional objects. In Proc. of PPoPP ’08, pages
>>>> 207–216. ACM Press, 2008.
>>>> https://www.cs.stevens.edu/~ejk/papers/boosting-ppopp08.pdf
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>>> --
>>> Chris Allen
>>> Currently working on http://haskellbook.com
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