What is the use of the yield keyword in Julia and what does it do? I have seen it used a few times in Python and then in Julia but it seems to be used in very different contexts in Julia.
Unlike in Python, where the yield keyword function similar to the return keyword but for generators, the yield keyword function in Julia is part of the tasks interface. From the docs:
yield()
Switch to the scheduler to allow another scheduled task to run. A task that calls this function is still runnable, and will be restarted immediately if there are no other runnable tasks.
Related
I am using foreach + %dopar% to achieve parallelism over multiple cores. I know some of the tasks will face exceptions. When an exception occurs:
Will the remaining tasks that were already parallelly started still complete?
Will the tasks that were not scheduled (I don't know if that's the correct term) be scheduled and eventually complete? If so, will it still be able to utilize all the cores?
I tried finding resources on this, but couldn't find any. Looks like I'm using the wrong keywords. If you have any resources, please direct me to them.
There is a parameter in foreach called .errorhandling, it could have the values of stop (default), remove or pass. their behaviour is like this:
stop: The function will be stopped.
remove: The result of this specific task will not be returned.
pass: The error object will be included with the results.
So addressing your specific question, if you have many task running in parallel and one of the task in one worker raised an exception, then it stop the process and will pass to the next task "scheduled" (that is because of the default value stop). The other task will continue as normal in parallel.
Please see this answer that explains better how are handled the errors in foreach and %dopar%.
I hope this will clarify a little your problem.
Are coroutines just syntactic sugar around completion blocks and completion blocks will be created under the hood? Or is the concept of coroutines much more complex and broad then just compiler trick aka syntactic sugar
It's not just syntactic sugar, not at all. Coroutines do not block threads, they just suspend execution, thus they encourage non-blocking concurrent programming.
Coroutines do not rely on features of the operating system or the JVM (e.g. they are not mapped to native Threads). Instead, coroutines and suspend functions particularly are transformed by the compiler producing a state machine capable of handling suspensions in general and passing around suspending coroutines keeping their state. This is enabled by Continuations, which are added as a parameter to each and every suspending function by the compiler; this technique is called “Continuation-passing style”.
For details please have a look at https://github.com/Kotlin/kotlin-coroutines/blob/master/kotlin-coroutines-informal.md
No, coroutines are not syntactic sugar. You can think of coroutines as of functions that can interact with the caller. When you call a normal function, say foo you pass control to foo and have to wait until foo either completes or throws exception. Coroutines are functions which can pass control back to caller, and caller can decide whether coroutine should continue and when and how coroutine should continue. This gives opportunity to implement things which are special language constructs in other languages:
Generators (aka yield keyword) like in C# and JavaScript. Caller continues execution of coroutine when a user wants new value from iterator. Coroutine passes back to caller by calling yield() function, which also passes some value to caller.
Async/await like in C# and JavaScript. Caller continues execution when Future (similar to Task or Promise) gets resolved. Coroutine passes back to caller by calling await() function. Caller passes value to coroutine when Future gets resolved and coroutine observes this value as a result of await() call.
Goroutines/channels in Go.
Unlike C#, JavaScript or Go, Kotlin does not implement any of these features in special syntax. Instead, Kotlin provides only suspend fun syntax, and then you can implement these features yourself (or get existing one from corresponding library called kotlinx.coroutines).
Corutines are syntactic sugar around asyncronous procedures, or, better say, Actors, which are repeatable asynchronous procedures.
Here is an interesting article mentioning implementing async/await in their system: http://joeduffyblog.com/2015/11/19/asynchronous-everything/
Along with this, we added the await and async keywords. A method could be marked async:
async int Foo() { ... }
All this meant was that it was allowed to await inside of it:
async int Bar() {
int x = await Foo();
...
return x * x;
}
Originally this was merely syntactic sugar for all the callback goop above, like it is in C#. Eventually, however, we went way beyond this, in the name of performance, and added lightweight coroutines and linked stacks.
I'm learning about async and Torando and struggling. First off is it possible to use executor class in Tornado?
The below example i'm creating a websocket and when receiving a message I want to run check() as another process in the background. This is a contrived example just for my learning sake. Neither the INSIDE or AFTER gets printed. Why do we need async specific packages like Motor if we have this executor class?
Also in all the example i've seen of Torando the #gen.coroutine are always done on classes that extend the tornado.web.RequestHandler in my example I'm using a tornado.websocket.WebSocketHandler can #gen.coroutine be used inside this class also?
Finally can anyone recommend a book or in-depth tutorial on this subject? I bought "Introduction to tornado" however it's a little outdated because it uses the tornado.gen.engine.
def check(msg):
time.sleep(10)
return msg
class SessionHandler(tornado.websocket.WebSocketHandler):
def open(self):
pass
def on_close(self):
pass
# not sure if i needed this decorator or not?
#tornado.web.asynchronous
def on_message(self,message):
print("INSIDE")
with concurrent.futures.ProcessPoolExecutor() as executor:
f=executor.submit(check,"a")
result = yield f
print("AFTER")
In order to use yield, you must use #tornado.gen.coroutine (or #gen.engine). #tornado.web.asynchronous is unrelated to the use of yield, and is generally only used with callback-based handlers (#asynchronous only works in regular handlers, not websockets). Change the decorator and you should see your print statements run.
Why would someone write an asynchronous library like Motor instead of using an executor like this? For performance. A thread or process pool is much more expensive (mainly in terms of memory) than doing the same thing asynchronously. There's nothing wrong with using an executor when you need a library that has no asynchronous counterpart, but it's better to use an asynchronous version if it's available (and if the performance matters enough, to write an asynchronous version when you need one).
Also note that ProcessPoolExecutor can be tricky: all arguments submitted to the executor must be picklable, and this can be expensive for large objects. I would recommend a ThreadPoolExecutor in most cases where you just want to wrap a synchronous library.
As this question is huge, I will give my view on this question so that you can simply tell me whether I am right or not. If not, where to correct. If my view is superficial, please present an overview of F# async usage. In mu understanding, to write async program, you need to put async code into "async" block like async{expression}, and use "let!" or "use!" to bind names to primitives, then you need to use method to run this async expression like "Async.Run". In addition, you can use exception handling to deal with exception, and cancellation to cancel when necessary. I also know there are several primitives that defined in F# core libraries, and F# extension of I/O operation. I just need to make sure the relation between these things. If you think my view on async workflows is superficial, please give an overview usage like what I have mentioned above. Thank you very much!
This question is huge, so at best, I can highlight some ideas and point you to learning resources and examples.
The description in the question isn't wrong (though there is no Async.Run function). But the main point about Asyncs is how they execute and why the way they execute is useful.
An async block defines a piece of code that becomes an Async<'T> object, which can be seen as a computation that can be executed at a later time. The Async returns an object of type 'T when its execution has completed -- if it has neither failed nor been cancelled.
let!, do! and use! are used inside of an async block to run another Async and, in the cases of let! and use!, bind its result to a name inside the current async. Unlike for example normal let, which simply binds any value to a name, the versions with an exclamation mark explicitly "import" the result of another async.
When an Async depends on another and waits for its result, such as with a let! binding, it normally does not block a thread. Asyncs utilize the .NET thread pool for easy parallel execution, and after an Async completes that another Async depends on, a continuation runs the remainder of the dependent Async.
The Async functions offer many ready-made ways to run Asyncs, such as Async.Start, which is a simple dispatch of an Async with no result, Async.RunSynchronously, which runs the Async and returns its result as if it were a normal function, Async.Parallel, which combines a sequence of Asyncs into one that executes them in parallel, or Async.StartAsTask, which runs an Async as an independent task. Further methods allow composition of Asyncs in terms of cancellation, or explicit control over continuation after an exception or cancellation.
Asyncs are very useful where waiting times are included: otherwise blocking calls can use Asyncs to not block execution, for example in I/O bound functions.
The best introductions to F# Asyncs I know are written, or co-written, by Don Syme, the lead designer of F#:
The chapter Reactive, Asynchronous, and Parallel Programming in the book Expert F#
A blog post with examples for asyncronous agents
The blog post introducing Asyncs in late 2007
I could not find detailed documentation about the #async macro. From the docs about parallelism I understand that there is only one system thread used inside a Julia process and there is explicit task switching going on by the help of the yieldto function - correct me if I am wrong about this.
For me it is difficult to understand when exactly these task switches happen just by looking at the code, and knowing when it happens seems crucial.
As I understand a yieldto somewhere in the code (or in some function called by the code) needs to be there to ensure that the system is not stuck with only one task.
For example when there is a read operation, inside the read there probably is a wait call and in the implementation of wait there probably is a yieldto call. I thought that without the yieldto call the code would stuck in one task; however running the following example seems to prove this hypothesis wrong.
#async begin # Task A
while true
println("A")
end
end
while true # Task B
println("B")
end
This code produces the following output
BA
BA
BA
...
It is very unclear to me where the task switching happens inside the task created by the #async macro in the code above.
How can I tell about looking at some code the points where task switching happens?
The task switch happens inside the call to println("A"), which at some point calls write(STDOUT, "A".data). Because isa(STDOUT, Base.AsyncStream) and there is no method that is more specialized, this resolves to:
write{T}(s::AsyncStream,a::Array{T}) at stream.jl:782
If you look at this method, you will notice that it calls stream_wait(ct) on the current task ct, which in turn calls wait().
(Also note that println is not atomic, because there is a potential wait between writing the arguments and the newline.)
You could of course determine when stuff like that happens by looking at all the code involved. But I don't see why you would need to know this exactly, because, when working with parallelism, you should not depend on processes not switching context anyway. If you depend on a certain execution order, synchronize explicitly.
(You already kind of noted this in your question, but let me restate it here: As a rule of thumb, when using green threads, you can expect potential context switches when doing IO, because blocking for IO is a textbook example of why green threads are useful in the first place.)