I'm looking at several examples from PETSc and petsc4py and looking at the PDF user manual of PETSc. The manual states:
For those not familiar with MPI, acommunicatoris a way of indicating a collection of processes that will be involved together in a calculation or communication. Communicators have the variable type MPI_Comm. In most cases users can employ the communicator PETSC_COMM_WORLD to indicate all processes in a given run and PETSC_COMM_SELF to indicate a single process.
I believe I understand that statement, but I'm unsure of the real consequences of actually using these communicators are. I'm unsure of what really happens when you do TSCreate(PETSC_COMM_WORLD,...) vs TSCreate(PETSC_COMM_SELF,...) or likewise for a distributed array. If you created a DMDA with PETSC_COMM_SELF, does this maybe mean that the DM object won't really be distributed across multiple processes? Or if you create a TS with PETSC_COMM_SELF and a DM with PETSC_COMM_WORLD, does this mean the solver can't actually access ghost nodes? Does it effect the results of DMCreateLocalVector and DMCreateGlobalVector?
The communicator for a solver decides which processes participate in the solver operations. For example, a TS with PETSC_COMM_SELF would run independently on each process, whereas one with PETSC_COMM_WORLD would evolve a single system across all processes. If you are using a DM with the solver, the communicators must be congruent.
Related
I am creating a webserver using tokio. Whenever a client connection comes in, a green thread is created via tokio::spawn.
The main function of my web server is proxy. Target server information for proxy is stored as a global variable, and for proxy, all tasks must access the data. Since there are multiple target servers, they must be selected by round robin. So the global variable (struct) must have information of the recently selected server(by index).
Concurrency problems occur because shared information can be read/written by multiple tasks at the same time.
According to the docs, there seems to be a way to use Mutex and Arc or a way to use channel to solve this.
I'm curious which one you usually prefer, or if there is another way to solve the problem.
If it's shared data, you generally do want Arc, or you can leak a box to get a 'static reference (assuming that the data is going to exist until the program exits), or you can use a global variable (though global variables tends to impede testability and should generally be considered an anti-pattern).
As far as what goes in the Arc/Box/global, that depends on what your data's access pattern will be. If you will often read but rarely write, then Tokio's RwLock is probably what you want; if you're going to be updating the data every time you read it, then use Tokio's Mutex instead.
Channels make the most sense when you have separate parts of the program with separate responsibilities. It doesn't work as well to update multiple workers with the same changes to data, because then you get into message ordering problems that can result in each worker's state disagreeing about something. (You get many of the problems of a distributed system without any of the benefits.)
Channels can work if there is a single entity responsible for maintaining the data, but at that point there isn't much benefit over using some kind of mutual exclusion mechanism; it winds up being the same thing with extra steps.
Greetings SO denizens!
I'm trying to architect an overhaul of an existing NodeJS application that has outgrown its original design. The solutions I'm working towards are well beyond my experience.
The system has ~50 unique async tasks defined as various finite state machines which it knows how to perform. Each task has a required set of parameters to begin execution which may be supplied by interactive prompts, a database or from the results of a previously completed async task.
I have a UI where the user may define a directed graph ("the flow"), specifying which tasks they want to run and the order they want to execute them in with additional properties associated with both the vertices and edges such as extra conditionals to evaluate before calling a child task(s). This information is stored in a third normal form PostgreSQL database as a "parent + child + property value" configuration which seems to work fairly well.
Because of the sheer number of permutations, conditionals and absurd number of possible points of failure I'm leaning towards expressing "the flow" as a state machine. I merely have just enough knowledge of graph theory and state machines to implement them but practically zero background.
I think what I'm trying to accomplish is at the flow run time after user input for the root services have been received, is somehow compile the database representation of the graph + properties into a state machine of some variety.
To further complicate the matter in the near future I would like to be able to "pause" a flow, save its state to memory, load it on another worker some time in the future and resume execution.
I think I'm close to a viable solution but if one of you kind souls would take mercy on a blind fool and point me in the right direction I'd be forever in your debt.
I solved similar problem few years ago as my bachelor and diploma thesis. I designed a Cascade, an executable structure which forms growing acyclic oriented graph. You can read about it in my paper "Self-generating Programs – Cascade of the Blocks".
The basic idea is, that each block has inputs and outputs. Initially some blocks are inserted into the cascade and inputs are connected to outputs of other blocks to form an acyclic graph. When a block is executed, it reads its inputs (cascade will pass values from connected outputs) and then the block sets its outputs. It can also insert additional blocks into the cascade and connect its inputs to outputs of already present blocks. This should be equal to your task starting another task and passing some parameters to it. Alternative to setting output to an value is forwarding a value from another output (in your case waiting for a result of some other task, so it is possible to launch helper sub-tasks).
I have a CLI application of which I can run multiple instances simultaneously. I need to associate a unique, sequential and re-usable identifier to each instance. It should also be contextual/independent for each process type.
Example:
The first, second and third instances get ids 0, 1 and 2, respectively.
Now if the second instance dies and another instance comes up, that new instance should be given id 1 since it was "freed" by the dying instance.
If I run a different process type, I should be given id 0.
The obvious choice would be to use the processes' PID but that would give me too many different and too sparse identifiers.
Is there something built-in in Unix/Linux or some service that gives me that?
I would prefer a system native or Node.js solution.
Background:
I'm using Graphite to generate stats of an application and I don't want to potentially create thousands of buckets of the same stats using the processes' PIDs. If there's an alternative solution to this problem, I would also be interested in knowing that.
Thank you!
Since I didn't find any system that meets my requirements, I have created an app myself and hosted it at GitHub: https://github.com/muzzley/process-id-dealer
It's a Node.js app that deals sequential and reusable process ids through an HTTP endpoint. Thus, it can be used by any other program.
I'm designing a large-scale project, and I think I see a way I could drastically improve performance by taking advantage of multiple cores. However, I have zero experience with multiprocessing, and I'm a little concerned that my ideas might not be good ones.
Idea
The program is a video game that procedurally generates massive amounts of content. Since there's far too much to generate all at once, the program instead tries to generate what it needs as or slightly before it needs it, and expends a large amount of effort trying to predict what it will need in the near future and how near that future is. The entire program, therefore, is built around a task scheduler, which gets passed function objects with bits of metadata attached to help determine what order they should be processed in and calls them in that order.
Motivation
It seems to be like it ought to be easy to make these functions execute concurrently in their own processes. But looking at the documentation for the multiprocessing modules makes me reconsider- there doesn't seem to be any simple way to share large data structures between threads. I can't help but imagine this is intentional.
Questions
So I suppose the fundamental questions I need to know the answers to are thus:
Is there any practical way to allow multiple threads to access the same list/dict/etc... for both reading and writing at the same time? Can I just launch multiple instances of my star generator, give it access to the dict that holds all the stars, and have new objects appear to just pop into existence in the dict from the perspective of other threads (that is, I wouldn't have to explicitly grab the star from the process that made it; I'd just pull it out of the dict as if the main thread had put it there itself).
If not, is there any practical way to allow multiple threads to read the same data structure at the same time, but feed their resultant data back to a main thread to be rolled into that same data structure safely?
Would this design work even if I ensured that no two concurrent functions tried to access the same data structure at the same time, either for reading or for writing?
Can data structures be inherently shared between processes at all, or do I always explicitly have to send data from one process to another as I would with processes communicating over a TCP stream? I know there are objects that abstract away that sort of thing, but I'm asking if it can be done away with entirely; have the object each thread is looking at actually be the same block of memory.
How flexible are the objects that the modules provide to abstract away the communication between processes? Can I use them as a drop-in replacement for data structures used in existing code and not notice any differences? If I do such a thing, would it cause an unmanageable amount of overhead?
Sorry for my naivete, but I don't have a formal computer science education (at least, not yet) and I've never worked with concurrent systems before. Is the idea I'm trying to implement here even remotely practical, or would any solution that allows me to transparently execute arbitrary functions concurrently cause so much overhead that I'd be better off doing everything in one thread?
Example
For maximum clarity, here's an example of how I imagine the system would work:
The UI module has been instructed by the player to move the view over to a certain area of space. It informs the content management module of this, and asks it to make sure that all of the stars the player can currently click on are fully generated and ready to be clicked on.
The content management module checks and sees that a couple of the stars the UI is saying the player could potentially try to interact with have not, in fact, had the details that would show upon click generated yet. It produces a number of Task objects containing the methods of those stars that, when called, will generate the necessary data. It also adds some metadata to these task objects, assuming (possibly based on further information collected from the UI module) that it will be 0.1 seconds before the player tries to click anything, and that stars whose icons are closest to the cursor have the greatest chance of being clicked on and should therefore be requested for a time slightly sooner than the stars further from the cursor. It then adds these objects to the scheduler queue.
The scheduler quickly sorts its queue by how soon each task needs to be done, then pops the first task object off the queue, makes a new process from the function it contains, and then thinks no more about that process, instead just popping another task off the queue and stuffing it into a process too, then the next one, then the next one...
Meanwhile, the new process executes, stores the data it generates on the star object it is a method of, and terminates when it gets to the return statement.
The UI then registers that the player has indeed clicked on a star now, and looks up the data it needs to display on the star object whose representative sprite has been clicked. If the data is there, it displays it; if it isn't, the UI displays a message asking the player to wait and continues repeatedly trying to access the necessary attributes of the star object until it succeeds.
Even though your problem seems very complicated, there is a very easy solution. You can hide away all the complicated stuff of sharing you objects across processes using a proxy.
The basic idea is that you create some manager that manages all your objects that should be shared across processes. This manager then creates its own process where it waits that some other process instructs it to change the object. But enough said. It looks like this:
import multiprocessing as m
manager = m.Manager()
starsdict = manager.dict()
process = Process(target=yourfunction, args=(starsdict,))
process.run()
The object stored in starsdict is not the real dict. instead it sends all changes and requests, you do with it, to its manager. This is called a "proxy", it has almost exactly the same API as the object it mimics. These proxies are pickleable, so you can pass as arguments to functions in new processes (like shown above) or send them through queues.
You can read more about this in the documentation.
I don't know how proxies react if two processes are accessing them simultaneously. Since they're made for parallelism I guess they should be safe, even though I heard they're not. It would be best if you test this yourself or look for it in the documentation.
I've been learning some lua for game development. I heard about coroutines in other languages but really came up on them in lua. I just don't really understand how useful they are, I heard a lot of talk how it can be a way to do multi-threaded things but aren't they run in order? So what benefit would there be from normal functions that also run in order? I'm just not getting how different they are from functions except that they can pause and let another run for a second. Seems like the use case scenarios wouldn't be that huge to me.
Anyone care to shed some light as to why someone would benefit from them?
Especially insight from a game programming perspective would be nice^^
OK, think in terms of game development.
Let's say you're doing a cutscene or perhaps a tutorial. Either way, what you have are an ordered sequence of commands sent to some number of entities. An entity moves to a location, talks to a guy, then walks elsewhere. And so forth. Some commands cannot start until others have finished.
Now look back at how your game works. Every frame, it must process AI, collision tests, animation, rendering, and sound, among possibly other things. You can only think every frame. So how do you put this kind of code in, where you have to wait for some action to complete before doing the next one?
If you built a system in C++, what you would have is something that ran before the AI. It would have a sequence of commands to process. Some of those commands would be instantaneous, like "tell entity X to go here" or "spawn entity Y here." Others would have to wait, such as "tell entity Z to go here and don't process anymore commands until it has gone here." The command processor would have to be called every frame, and it would have to understand complex conditions like "entity is at location" and so forth.
In Lua, it would look like this:
local entityX = game:GetEntity("entityX");
entityX:GoToLocation(locX);
local entityY = game:SpawnEntity("entityY", locY);
local entityZ = game:GetEntity("entityZ");
entityZ:GoToLocation(locZ);
do
coroutine.yield();
until (entityZ:isAtLocation(locZ));
return;
On the C++ size, you would resume this script once per frame until it is done. Once it returns, you know that the cutscene is over, so you can return control to the user.
Look at how simple that Lua logic is. It does exactly what it says it does. It's clear, obvious, and therefore very difficult to get wrong.
The power of coroutines is in being able to partially accomplish some task, wait for a condition to become true, then move on to the next task.
Coroutines in a game:
Easy to use, Easy to screw up when used in many places.
Just be careful and not use it in many places.
Don't make your Entire AI code dependent on Coroutines.
Coroutines are good for making a quick fix when a state is introduced which did not exist before.
This is exactly what java does. Sleep() and Wait()
Both functions are the best ways to make it impossible to debug your game.
If I were you I would completely avoid any code which has to use a Wait() function like a Coroutine does.
OpenGL API is something you should take note of. It never uses a wait() function but instead uses a clean state machine which knows exactly what state what object is at.
If you use coroutines you end with up so many stateless pieces of code that it most surely will be overwhelming to debug.
Coroutines are good when you are making an application like Text Editor ..bank application .. server ..database etc (not a game).
Bad when you are making a game where anything can happen at any point of time, you need to have states.
So, in my view coroutines are a bad way of programming and a excuse to write small stateless code.
But that's just me.
It's more like a religion. Some people believe in coroutines, some don't. The usecase, the implementation and the environment all together will result into a benefit or not.
Don't trust benchmarks which try to proof that coroutines on a multicore cpu are faster than a loop in a single thread: it would be a shame if it were slower!
If this runs later on some hardware where all cores are always under load, it will turn out to be slower - ups...
So there is no benefit per se.
Sometimes it's convenient to use. But if you end up with tons of coroutines yielding and states that went out of scope you'll curse coroutines. But at least it isn't the coroutines framework, it's still you.
We use them on a project I am working on. The main benefit for us is that sometimes with asynchronous code, there are points where it is important that certain parts are run in order because of some dependencies. If you use coroutines, you can force one process to wait for another process to complete. They aren't the only way to do this, but they can be a lot simpler than some other methods.
I'm just not getting how different they are from functions except that
they can pause and let another run for a second.
That's a pretty important property. I worked on a game engine which used them for timing. For example, we had an engine that ran at 10 ticks a second, and you could WaitTicks(x) to wait x number of ticks, and in the user layer, you could run WaitFrames(x) to wait x frames.
Even professional native concurrency libraries use the same kind of yielding behaviour.
Lots of good examples for game developers. I'll give another in the application extension space. Consider the scenario where the application has an engine that can run a users routines in Lua while doing the core functionality in C. If the user needs to wait for the engine to get to a specific state (e.g. waiting for data to be received), you either have to:
multi-thread the C program to run Lua in a separate thread and add in locking and synchronization methods,
abend the Lua routine and retry from the beginning with a state passed to the function to skip anything, least you rerun some code that should only be run once, or
yield the Lua routine and resume it once the state has been reached in C
The third option is the easiest for me to implement, avoiding the need to handle multi-threading on multiple platforms. It also allows the user's code to run unmodified, appearing as if the function they called took a long time.