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Comparison of different concurrency models: Actors, CSP, Disruptor and Threads (java-is-the-new-c.blogspot.com)
67 points by r4um on Apr 19, 2014 | hide | past | web | favorite | 18 comments

Disagree with the characterization of CSP. CSP as I understand it has channels as the main building block for interprocess communication. Channels can be synchronous or asynchronous, and bounded or unbounded. The important point is that a channel is a value (you can pass it to a method, return it from a method) and usually has a type.

Actors are like a simplified CSP where each (lightweight) thread has a single input channel. In the case of Akka this mean you lose type information because control messages are mingled with data messages and you can't assign any useful type of them.

Disruptor is mainly a pattern and implementation for high efficiency -- big queues, minimum number of threads, and some tricks using CAS operations and the like. I wouldn't call it a model of concurrency -- it's basically a particular implementation of CSP.

CSP as Tony Hoare invented it specified a static set of processes where communication was also synchronization, and first-class channels didn't appear until years later -- so it's understandable where that claim came from. (AFAIK Concurrent ML was the first practical language with that feature; Wikipedia says Hoare coauthored a paper some years earlier).

As long as we're in a well-actually: in the Actors model each channel has one receiving thread. If it were that each thread has one input channel, that wouldn't keep different threads from consuming from the same channel, which is different. Also, the original papers talked about an implementation spawning threads to handle each message -- even two threads to a message, one to compute the messages sent in response and one to compute the next state to handle the next message with, etc.

More comparison of the two: http://en.wikipedia.org/wiki/Communicating_sequential_proces...

Yes, authors cited their sources, so we can understand their perspective better.

facepalm That should be "authors should..." Which is BTW an absurdly trivial observation to post in retrospect, on top of it.

Can anyone elaborate on what happens with Akka after 5 cores? In all of the timings, Akka has an equivalent exponential drop to all of the other systems - until it hits 5 cores. At that point, it levels off or goes up. Is there anything inherent in the Akka implementation that would cause this?

There should be a rethink of how our multicore computers are architected. Here is what I have to do in order to write a multicore parallel soft real-time application.

1) I have to scrutinize the way I use memory, such that no two threads are going to stomp on the same cache line too often. (False sharing)

2) The above includes the way I use locking primitives and higher level constructs that are built on them! (Lock convoying)

3) If I am using a sophisticated container like a PersistentMap, which is supposed to make it easy to think about concurrency, I still have to think about 1 & 2 above at the level of the container's API/contract, as well as think about how they might interleave contended data within their implementations. (Yes, Concurrency is not Parallelism. Here we see why.)

4) Garbage Collection -- Now I have to think about if the GC is in a separate worker thread and think about how that can result in cache line contention.

5) Even if you do all of the above correctly, the OS can still come along and do something counterproductive, like trying to schedule all your threads on as few cores/sockets as possible. (This is even nicknamed "Stupid Scheduling" in Linux by people who have to contend with it.) This entails yet more work.

6) Profiling all of the above is, as far as I can tell, still a hazy art. Nothing is a smoking gun for one of the pitfalls of multicore parallelism. One is only left with educated guesses, which means that you have to increase data gathering. Is there something like a debugger like QEMU which can simulate multicore machines and provide statistics on cache misses? Apparently, there are ways to get this information from hardware as well.

It would seem that Erlang has an advantage with regards to multicore parallelism, because its model is "distributed by default," so contention is severely limited, which is great for parallelism. However, coordination is severely limited as well! (I need to look at Supervisors and see what they can and cannot do.)

It would also seem like there's room for languages that combine these recently popular advanced concurrency tools with enough low-level power to navigate the above pitfalls, combined with a memory management abstraction that increases productivity without requiring the complications for parallelism entailed by GC. Rust, C++, and Objective-C are the only languages that somewhat fit this bill. (If only Rust were not quite so new!) Go, with its emphasis on pass by value semantics might also work for certain applications, despite its reliance on GC.

What do you mean by coordination being limited in Erlang, exactly?

Supervisors are essentially in charge of one thing, and that's process lifecycle management; starting up processes, restarting them when they die, and bailing out if something goes wrong.

What do you mean by coordination being limited in Erlang, exactly?

I have an algorithm for an absolute occupancy grid that can handle multicore parallelism. Basically, the grid is subdivided into smaller parts that just do their thing independently, but if they detect that a move takes a player out of their boundaries, they queue it up locally for the global grid. Once all of the subgrid threads are done, they wait while the global grid does its thing. (Aggregating all the local subgrid queues, then processing those moves requiring global coordination.)

I don't see how I can do that in Erlang. The closest thing I'm aware of (and I know almost nothing about Erlang) is that moves are are done optimistically, then collisions can be detected after the fact and rolled back. Maybe that's what I'll need to do: port to Erlang and use rollbacks.

Supervisors are essentially in charge of one thing, and that's process lifecycle management

I thought they could do more than just that, maybe.

That should be reasonably possible, surely? Your global grid is one process, that sends messages containing the smaller parts to the processing processes, then waits for them to reply with another message, and then aggregates the replies. It's all just message buses, you can push whatever stuff you want around however you want.

Imagine how you'd implement your system if each part of the system had to communicate with the others over a network socket, and you're most of the way to implementing it in Erlang.

It's probably not optimal to do things involving lots of calculations in Erlang, though, as the VM is fairly slow. Akka implements a similar system, but for the JVM (Scala).

waits for them to reply with another message, and then aggregates the replies

Basically, you are proposing that the world actor aggregate the entire world's data? That wouldn't scale. Or, maybe the world actor just becomes responsible for adjudicating moves between subgrids. Maybe.

Isn't the fundamental point of concurrency the players?

Couldn't you have X players moving on a global grid? I have built systems with literally tens of millions of players (Erlang processes) moving on a grid (and doing way more, localized threat detection, decision making, moving away or towards, and coordination with other units in X range).

Can you give a high level description of how you implement the global grid? It's really only absolute coordination of a checkerboard-like grid that I'm wrestling with. I've verified that my algorithm works, and is nicely stable. I've also scrutinized it with VisualVM, and on Linux and OS X, the threads are either in wait or running, in the pattern I'd expect. (The subgrid worker-group threads have to wait for the global grid to do its coordinating.) I'm also seeing expected use of the thread pools. However, for some reason I can't seem to scale beyond 250 users.

One complication is that my grid is for a procedurally generated world with 2^120 locations in it. This is why I generate subgrids. A degenerate case is one subgrid per user. However, these subgrids are organized in load-balanced groups, each of which has their own thread pool, caches, and locks.

Also, rollbacks are problematic, though the real problems are arguably corner cases.

Erlang might be a win because each garbage collector only has to deal with its own local memory.

EDIT: It turns out my algorithm is somewhat similar to Pikko:


One big difference, is that my algorithm doesn't move or reconfigure masts, instead it dynamically creates subgrids, which are then grouped into "workgroups" each of which is supposed to be processed by a different CPU socket. Instead of there being an API, it's more that the subgrids stop what they are doing and their information is briefly managed by the global grid code. (The procedurally generated map is rigged, so that there are many opportunities for crossing from one subgrid to another.)

Going to do my best to put a quick summary (from memory) on something that took us a long time to get right and was a bit convoluted.

Our units actually would report to intermediaries their maximum interaction boundries, which would then be passed to processes to create something somewhat like a mast, someone like a subgrid -- a dynamic interaction zone. All our units had hard constraints (max speed, etc) and worked in global ticks that represented real time. Then, we would talk to global to stretch all the interaction zones to fill empty space and report back boundaries. Then, our units would work in little worlds until they crossed a threshold, we caused a rezoning among them and their neighbors. So initially, the everything would have to be parsed out into interaction zones, but then they could ignore each other for periods of times until a unit strayed across an edge, and then rezoning took place.

Not sure how well it would work with amped up movement (making it have to go all the way up to global more) and not certain how it would work at the scale of 1 undecillion 329 decillion 227 nonillion 995 octillion 784 septillion 915 sextillion 872 quintillion 903 quadrillion 807 trillion 60 billion 280 million 344 thousand 576 points!

I woke up with the realization that my problem is probably GC pressure. The system is currently written in idiomatic Clojure so doing everything generates garbage. What's more, the garbage is created in one tick, then released in a subsequent tick, so I'm losing the benefit of generational GC.

I think I can make my subgrids algorithm work, but it will have to be using mutable data structures.

Disruptor is an application of the pre-emptive threading model. It certainly it interesting but to put it in the same pedestal as CSP and Actor model is wrong. (Also the stability of Disruptor approach in face of competing applications on the same machine was an issue last I checked.)

It is interesting since in Erlang data structures are functional (immutable) actor mailboxes could as well be implemented to share data instead of copying it. Large binaries are handled that way. They live in a binary memory area and are referenced via pointers. The rest of the messages are not. At some point it was deemed it was better to actually make the copy.

Even if everything is immutable, sharing data between threads adds memory management overhead that isn't worth it for most objects. For GC, you have to walk the environment of every thread to free anything in the shared memory. I dunno how big of a difference that makes for fancy concurrent GCs, but for regular ones, you'd have to stop every thread during collection. For reference counting, it's a bit simpler; so long as each thread keeps its own reference count, you can decouple the reference counting from freeing and have a hybrid scheme where you "GC" the shared object heap by scanning for objects with reference counts of 0 in all threads. Still not free, though.

> For maximum performance one would create one large job for each Core of the CPU used.

Dmitry Vyukov has suggested otherwise in a similar scenario using Go:

> If you split the image into say 8 equal parts, and then one of the goroutines/threads/cores accidentally took 2 times more time to complete, then whole processing is slowed down 2x. The slowdown can be due to OS scheduling, other processes/interrupts, unfortunate NUMA memory layout, different amount of processing per part (e.g. ray tracing) and other reasons. [...] size of a work item must never be dependent on input data size (in an ideal world), it must be dependent on overheads of parallelization technology. Currently a reference number is ~100us-1ms per work item. So you can split the image into blocks of fixed size (say 64x64) and then distribute them among threads/goroutines. This has advantages of both locality and good load balancing.


Or to put it this way: imagine that there was zero concurrency overhead. Then splitting out jobs to their minimal size would be the ideal option, as that would allow for the most smoothed out division of labour where every processor does work all the time and they are all doing work until the entire task is completed.

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