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

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 mailto:haskell-cafe@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