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Suppose I'm writing a program in node.js (or perhaps another typical back-end scripting language). Suppose further I have a C function f (or a python function, or what have you) that does some pure data transformation.
If I want to use f in my node program, there are two approaches:
Bind f via something like node-gyp that makes it callable from JavaScript land.
Make f into a binary (or, in the case of a language like python, a single f.py interface) that sits on the file system, and then call it from node as if were any other system command (so that one can then take the output from the system call as a string, convert it into node.js data, and then use it).
Question: What are the performance implications of choosing (2) over (1)?
This is important because if you are using a language like C to make some aspect of your application run significantly faster, then using (2) would seem pointless if it slowed things down past some threshold.
The cost of 1 is the cost of loading the native code, transfering arguments (ffi), calling the native code, and transfering arguments back. With loading being done only once.
The cost of 2 is always going to be the cost to startup the process, running the process, converting the results back from strings.
If the cost of f is high, you may never see a difference between 1 and 2. If the cost of f is low, then 2 will take longer because the process startup overhead will dominate.
However, depending on the complexity of f (it might be a very large data-processing application in C), it's almost always faster to create a native binding like 1. Avoiding process startup overhead is important, it also reduces the total amount of memory needed to run your application.
Alternatively you could do option:
Have the C code talk over a local network socket. Accepting requests and responding with answers when the computation is done.
This has the benefit of scaling out to multiple nodes if you need it.
Benchmarking both for your use case is the only way to be sure but method 1 is
likely to be faster.
The startup cost of calling a binary and starting an interpreter for python/perl/blah would likely kill any performance gain you might get using their Foreign Function Interface (FFI). Startup cost is one of the reasons why Apache has mod_python, mod_perl and why FastCGI exists.
Another thing to consider is that you're adding another language to the mix and this might kill performance of the team ie now everyone needs to know two languages and two FFI methods etc. If your app is in Node, keep it in Node and use node to call native methods.
Simply put, I want to manipulate two motors in parallel, then when both are ready, continue with a 3rd thread.
Below is image of what I have now. In two top threads, it sets motors B and C to "unlimited", then waits until both trigger the switches, then sets a separate boolean variable for both.
Then in 3rd thread, I poll these two variables with 1 second interval, until AND operation gives true to the loop termination condition.
This is embedded system and all, so it may be ok here, but in "PC programming", this kind of polling loop would be rather horrible thing to do.
Question: Can I do either of both of
wait for variable without this kind of polling loop?
wait for a thread to finish without using a variable at all?
Your question is a bit vague on what you actually want to achieve and using which language. As I understood you want to be able to implement a similar multithreaded motor control mechanism in Labview?
If so, then the answer to both of your questions is yes, you can implement the wait without an explicitly defined variable (other than the error cluster, which you probably would be passing around anyway). The easiest method is to pass an error cluster to both your loops and then use Merge errors to combine the generated errors once the loops are finished. Merge errors will wait until both inputs have data, merges the errors, and passes the merged error cluster on. By wiring the merged error cluster to your teardown function you effectively achieve the thread synchronization you described. If you require thread synchronization for the two control loops, you would however still have to use semaphores, rendezvous', notifiers, and other built-in synch methods.
In the image there's an init function that opens two serial devices (purple wire) and passes them to the control loops, which both runs until an error (yellow-black wire) occurs. The errors from both are merged and passed to the teardown function that releases the serial devices. Notice that in this particular example the synchronization would occur at the end of program as long as there's at least one wire coming from each loop to the teardown function.
Similar functionality in a text based programming language would necessitate the use of more elaborate mechanisms, though some specialised language for parallel programming might help here.
Wanting to be sure we're using the correct synchronization (and no more than necessary) when writing threadsafe code in JRuby; specifically, in a Puma instantiated Rails app.
UPDATE: Extensively re-edited this question, to be very clear and use latest code we are implementing. This code uses the atomic gem written by #headius (Charles Nutter) for JRuby, but not sure it is totally necessary, or in which ways it's necessary, for what we're trying to do here.
Here's what we've got, is this overkill (meaning, are we over/uber-engineering this), or perhaps incorrect?
ourgem.rb:
require 'atomic' # gem from #headius
SUPPORTED_SERVICES = %w(serviceABC anotherSvc andSoOnSvc).freeze
module Foo
def self.included(cls)
cls.extend(ClassMethods)
cls.send :__setup
end
module ClassMethods
def get(service_name, method_name, *args)
__cached_client(service_name).send(method_name.to_sym, *args)
# we also capture exceptions here, but leaving those out for brevity
end
private
def __client(service_name)
# obtain and return a client handle for the given service_name
# we definitely want to cache the value returned from this method
# **AND**
# it is a requirement that this method ONLY be called *once PER service_name*.
end
def __cached_client(service_name)
##_clients.value[service_name]
end
def __setup
##_clients = Atomic.new({})
##_clients.update do |current_service|
SUPPORTED_SERVICES.inject(Atomic.new({}).value) do |memo, service_name|
if current_services[service_name]
current_services[service_name]
else
memo.merge({service_name => __client(service_name)})
end
end
end
end
end
end
client.rb:
require 'ourgem'
class GetStuffFromServiceABC
include Foo
def self.get_some_stuff
result = get('serviceABC', 'method_bar', 'arg1', 'arg2', 'arg3')
puts result
end
end
Summary of the above: we have ##_clients (a mutable class variable holding a Hash of clients) which we only want to populate ONCE for all available services, which are keyed on service_name.
Since the hash is in a class variable (and hence threadsafe?), are we guaranteed that the call to __client will not get run more than once per service name (even if Puma is instantiating multiple threads with this class to service all the requests from different users)? If the class variable is threadsafe (in that way), then perhaps the Atomic.new({}) is unnecessary?
Also, should we be using an Atomic.new(ThreadSafe::Hash) instead? Or again, is that not necessary?
If not (meaning: you think we do need the Atomic.news at least, and perhaps also the ThreadSafe::Hash), then why couldn't a second (or third, etc.) thread interrupt between the Atomic.new(nil) and the ##_clients.update do ... meaning the Atomic.news from EACH thread will EACH create two (separate) objects?
Thanks for any thread-safety advice, we don't see any questions on SO that directly address this issue.
Just a friendly piece of advice, before I attempt to tackle the issues you raise here:
This question, and the accompanying code, strongly suggests that you don't (yet) have a solid grasp of the issues involved in writing multi-threaded code. I encourage you to think twice before deciding to write a multi-threaded app for production use. Why do you actually want to use Puma? Is it for performance? Will your app handle many long-running, I/O-bound requests (like uploading/downloading large files) at the same time? Or (like many apps) will it primarily handle short, CPU-bound requests?
If the answer is "short/CPU-bound", then you have little to gain from using Puma. Multiple single-threaded server processes would be better. Memory consumption will be higher, but you will keep your sanity. Writing correct multi-threaded code is devilishly hard, and even experts make mistakes. If your business success, job security, etc. depends on that multi-threaded code working and working right, you are going to cause yourself a lot of unnecessary pain and mental anguish.
That aside, let me try to unravel some of the issues raised in your question. There is so much to say that it's hard to know where to start. You may want to pour yourself a cold or hot beverage of your choice before sitting down to read this treatise:
When you talk about writing "thread-safe" code, you need to be clear about what you mean. In most cases, "thread-safe" code means code which doesn't concurrently modify mutable data in a way which could cause data corruption. (What a mouthful!) That could mean that the code doesn't allow concurrent modification of mutable data at all (using locks), or that it does allow concurrent modification, but makes sure that it doesn't corrupt data (probably using atomic operations and a touch of black magic).
Note that when your threads are only reading data, not modifying it, or when working with shared stateless objects, there is no question of "thread safety".
Another definition of "thread-safe", which probably applies better to your situation, has to do with operations which affect the outside world (basically I/O). You may want some operations to only happen once, or to happen in a specific order. If the code which performs those operations runs on multiple threads, they could happen more times than desired, or in a different order than desired, unless you do something to prevent that.
It appears that your __setup method is only called when ourgem.rb is first loaded. As far as I know, even if multiple threads require the same file at the same time, MRI will only ever let a single thread load the file. I don't know whether JRuby is the same. But in any case, if your source files are being loaded more than once, that is symptomatic of a deeper problem. They should only be loaded once, on a single thread. If your app handles requests on multiple threads, those threads should be started up after the application has loaded, not before. This is the only sane way to do things.
Assuming that everything is sane, ourgem.rb will be loaded using a single thread. That means __setup will only ever be called by a single thread. In that case, there is no question of thread safety at all to worry about (as far as initialization of your "client cache" goes).
Even if __setup was to be called concurrently by multiple threads, your atomic code won't do what you think it does. First of all, you use Atomic.new({}).value. This wraps a Hash in an atomic reference, then unwraps it so you just get back the Hash. It's a no-op. You could just write {} instead.
Second, your Atomic#update call will not prevent the initialization code from running more than once. To understand this, you need to know what Atomic actually does.
Let me pull out the old, tired "increment a shared counter" example. Imagine the following code is running on 2 threads:
i += 1
We all know what can go wrong here. You may end up with the following sequence of events:
Thread A reads i and increments it.
Thread B reads i and increments it.
Thread A writes its incremented value back to i.
Thread B writes its incremented value back to i.
So we lose an update, right? But what if we store the counter value in an atomic reference, and use Atomic#update? Then it would be like this:
Thread A reads i and increments it.
Thread B reads i and increments it.
Thread A tries to write its incremented value back to i, and succeeds.
Thread B tries to write its incremented value back to i, and fails, because the value has already changed.
Thread B reads i again and increments it.
Thread B tries to write its incremented value back to i again, and succeeds this time.
Do you get the idea? Atomic never stops 2 threads from running the same code at the same time. What it does do, is force some threads to retry the #update block when necessary, to avoid lost updates.
If your goal is to ensure that your initialization code will only ever run once, using Atomic is a very inappropriate choice. If anything, it could make it run more times, rather than less (due to retries).
So, that is that. But if you're still with me here, I am actually more concerned about whether your "client" objects are themselves thread-safe. Do they have any mutable state? Since you are caching them, it seems that initializing them must be slow. Be that as it may, if you use locks to make them thread-safe, you may not be gaining anything from caching and sharing them between threads. Your "multi-threaded" server may be reduced to what is effectively an unnecessarily complicated, single-threaded server.
If the client objects have no mutable state, good for you. You can be "free and easy" and share them between threads with no problems. If they do have mutable state, but initializing them is slow, then I would recommend caching one object per thread, so they are never shared. Thread[] is your friend there.
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.
I've read a lot recently about how writing multi-threaded apps is a huge pain in the neck, and have learned enough about the topic to understand, at least at some level, why it is so.
I've read that using functional programming techniques can help alleviate some of this pain, but I've never seen a simple example of functional code that is concurrent. So, what are some alternatives to using threads? At least, what are some ways to abstract them away so you needn't think about things like locking and whether a particular library's objects are thread-safe.
I know Google's MapReduce is supposed to help with the problem, but I haven't seen a succinct explanation of it.
Although I'm giving a specific example below, I'm more curious of general techniques than solving this specific problem (using the example to help illustrate other techniques would be helpful though).
I came to the question when I wrote a simple web crawler as a learning exercise. It works pretty well, but it is slow. Most of the bottleneck comes from downloading pages. It is currently single threaded, and thus only downloads a single page at a time. Thus, if the pages can be downloaded concurrently, it would speed things up dramatically, even if the crawler ran on a single processor machine. I looked into using threads to solve the issue, but they scare me. Any suggestions on how to add concurrency to this type of problem without unleashing a terrible threading nightmare?
The reason functional programming helps with concurrency is not because it avoids using threads.
Instead, functional programming preaches immutability, and the absence of side effects.
This means that an operation could be scaled out to N amount of threads or processes, without having to worry about messing with shared state.
Actually, threads are pretty easy to handle until you need to synchronize them. Usually, you use threadpool to add task and wait till they are finished.
It is when threads need to communicate and access shared data structures that multi threading becomes really complicated. As soon as you have two locks, you can get deadlocks, and this is where multithreading gets really hard. Sometimes, your locking code could be wrong by just a few instructions. In that case, you could only see bugs in production, on multi-core machines (if you developed on single core, happened to me) or they could be triggered by some other hardware or software. Unit testing doesn't help much here, testing finds bugs, but you can never be as sure as in "normal" apps.
I'll add an example of how functional code can be used to safely make code concurrent.
Here is some code you might want to do in parallel, so you don't have wait for one file to finish to start downloading the next:
void DownloadHTMLFiles(List<string> urls)
{
foreach(string url in urls)
{
DownlaodOneFile(url); //download html and save it to a file with a name based on the url - perhaps used for caching.
}
}
If you have a number of files the user might spend a minute or more waiting for them all. We can re-write this code functionally like this, and it basically does the exact same thing:
urls.ForEach(DownloadOneFile);
Note that this still runs sequentially. However, not only is it shorter, we've gained an important advantage here. Since each call to the DownloadOneFile function is completely isolated from the others (for our purposes, available bandwidth isn't an issue) you could very easily swap out the ForEach function for another very similar function: one that kicks off each call to DownlaodOneFile on a separate thread from a threadpool.
It turns out .Net has just such a function availabe using Parallel Extensions. So, by using functional programming you can change one line of code and suddenly have something run in parallel that used to run sequentially. That's pretty powerful.
There are a couple of brief mentions of asynchronous models but no one has really explained it so I thought I'd chime in. The most common method I've seen used as an alternative for multi-threading is asynchronous architectures. All that really means is that instead of executing code sequentially in a single thread, you use a polling method to initiate some functions and then come back and check periodically until there's data available.
This really only works in models like your aforementioned crawler, where the real bottleneck is I/O rather than CPU. In broad strokes, the asynchronous approach would initiate the downloads on several sockets, and a polling loop periodically checks to see if they're finished downloading and when that's done, we can move on to the next step. This allows you to run several downloads that are waiting on the network, by context switching within the same thread, as it were.
The multi-threaded model would work much the same, except using a separate thread rather than a polling loop checking multiple sockets in the same thread. In an I/O bound application, asynchronous polling works almost as well as threading for many use cases, since the real problem is simply waiting for the I/O to complete and not so much the waiting for the CPU to process the data.
Another real world example is for a system that needed to execute a number of other executables and wait for results. This can be done in threads, but it's also considerably simpler and almost as effective to simply fire off several external applications as Process objects, then check back periodically until they're all finished executing. This puts the CPU-intensive parts (the running code in the external executables) in their own processes, but the data processing is all handled asynchronously.
The Python ftp server lib I work on, pyftpdlib uses the Python asyncore library to handle serving FTP clients with only a single thread, and asynchronous socket communication for file transfers and command/response.
See for further reading the Python Twisted library's page on Asynchronous Programming - while somewhat specific to using Twisted, it also introduces async programming from a beginner perspective.
Concurrency is quite a complicated subject in computer science, which demands good understanding of hardware architecture as well as operating system behavior.
Multi-threading has many implementations based on your hardware and your hosting OS, and as tough as it is already, the pitfalls are numerous. It should be noted that in order to achieve "true" concurrency, threads are the only way to go. Basically, threads are the only way for you as a programmer to share resources between different parts of your software while allowing them to run in parallel. By parallel you should consider that a standard CPU (dual/multi-cores aside) can only do one thing at a time. Concepts like context switching now come into play, and they have their own set of rules and limitations.
I think you should seek more generic background on the subject, like you are saying, before you go about implementing concurrency in your program.
I guess the best place to start is the wikipedia article on concurrency, and go on from there.
What typically makes multi-threaded programming such a nightmare is when threads share resources and/or need to communicate with each other. In the case of downloading web pages, your threads would be working independently, so you may not have much trouble.
One thing you may want to consider is spawning multiple processes rather than multiple threads. In the case you mention--downloading web pages concurrently--you could split the workload up into multiple chunks and hand each chunk off to a separate instance of a tool (like cURL) to do the work.
If your goal is to achieve concurrency it will be hard to get away from using multiple threads or processes. The trick is not to avoid it but rather to manage it in a way that is reliable and non-error prone. Deadlocks and race conditions in particular are two aspects of concurrent programming that are easy to get wrong. One general approach to manage this is to use a producer/consumer queue... threads write work items to the queue and workers pull items from it. You must make sure you properly synchronize access to the queue and you're set.
Also, depending on your problem, you may also be able to create a domain specific language which does away with concurrency issues, at least from the perspective of the person using your language... of course the engine which processes the language still needs to handle concurrency, but if this will be leveraged across many users it could be of value.
There are some good libraries out there.
java.util.concurrent.ExecutorCompletionService will take a collection of Futures (i.e. tasks which return values), process them in background threads, then bung them in a Queue for you to process further as they complete. Of course, this is Java 5 and later, so isn't available everywhere.
In other words, all your code is single threaded - but where you can identify stuff safe to run in parallel, you can farm it off to a suitable library.
Point is, if you can make the tasks independent, then thread safety isn't impossible to achieve with a little thought - though it is strongly recommended you leave the complicated bit (like implementing the ExecutorCompletionService) to an expert...
One simple way to avoid threading in your simple scenario, Is to download from different processes. The main process will invoke other processes with parameters that will download the files to local directory, And then the main process can do the real job.
I don't think that there are any simple solution to those problems. Its not a threading problem. Its the concurrency that brake the human mind.
You might watch the MSDN video on the F# language: PDC 2008: An introduction to F#
This includes the two things you are looking for. (Functional + Asynchronous)
For python, this looks like an interesting approach: http://members.verizon.net/olsongt/stackless/why_stackless.html#introduction
Use Twisted. "Twisted is an event-driven networking engine written in Python" http://twistedmatrix.com/trac/. With it, I could make 100 asynchronous http requests at a time without using threads.
Your specific example is seldom solved with multi-threading. As many have said, this class of problems is IO-bound, meaning the processor has very little work to do, and spends most of it's time waiting for some data to arrive over the wire and to process that, and similarly it has to wait for disk buffers to flush so that it can put more of the recently downloaded data on disk.
The method to performance is through the select() facility, or an equivalent system call. The basic process is to open a number of sockets (for the web crawler downloads) and file handles (for storing them to disk). Next you set all of the different sockets and fh to non-blocking mode, meaning that instead of making your program wait until data is available to read after issuing a request, it returns right away with a special code (usually EAGAIN) to indicate that no data is ready. If you looped through all of the sockets in this way you would be polling, which works well, but is still a waste of cpu resources because your reads and writes will almost always return with EAGAIN.
To get around this, all of the sockets and fp's will be collected into a 'fd_set', which is passed to the select system call, then your program will block, waiting on ANY of the sockets, and will awaken your program when there's some data on any of the streams to process.
The other common case, compute bound work, is without a doubt best addressed with some sort of true parallelism (as apposed to the asynchronous concurrency presented above) to access the resources of multiple cpu's. In the case that your cpu bound task is running on a single threaded archetecture, definately avoid any concurrency, as the overhead will actually slow your task down.
Threads are not to be avoided nor are they "difficult". Functional programming is not necessarily the answer either. The .NET framework makes threading fairly simple. With a little thought you can make reasonable multithreaded programs.
Here's a sample of your webcrawler (in VB.NET)
Imports System.Threading
Imports System.Net
Module modCrawler
Class URLtoDest
Public strURL As String
Public strDest As String
Public Sub New(ByVal _strURL As String, ByVal _strDest As String)
strURL = _strURL
strDest = _strDest
End Sub
End Class
Class URLDownloader
Public id As Integer
Public url As URLtoDest
Public Sub New(ByVal _url As URLtoDest)
url = _url
End Sub
Public Sub Download()
Using wc As New WebClient()
wc.DownloadFile(url.strURL, url.strDest)
Console.WriteLine("Thread Finished - " & id)
End Using
End Sub
End Class
Public Sub Download(ByVal ud As URLtoDest)
Dim dldr As New URLDownloader(ud)
Dim thrd As New Thread(AddressOf dldr.Download)
dldr.id = thrd.ManagedThreadId
thrd.SetApartmentState(ApartmentState.STA)
thrd.IsBackground = False
Console.WriteLine("Starting Thread - " & thrd.ManagedThreadId)
thrd.Start()
End Sub
Sub Main()
Dim lstUD As New List(Of URLtoDest)
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file0.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file1.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file2.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file3.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file4.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file5.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file6.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file7.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file8.txt"))
lstUD.Add(New URLtoDest("http://stackoverflow.com/questions/382478/how-can-threads-be-avoided", "c:\file9.txt"))
For Each ud As URLtoDest In lstUD
Download(ud)
Next
' you will see this message in the middle of the text
' pressing a key before all files are done downloading aborts the threads that aren't finished
Console.WriteLine("Press any key to exit...")
Console.ReadKey()
End Sub
End Module