I noticed in a story on Hacker News that many people do not understand that differences in threading implementations between different programming languages. In the single processor days, understanding the threading model that you were working with was not that important. With more than one core, it is a good thing to know. This is an overview.
The Beginning (C and Native Threads)The first threading model we will look at is the standard OS level thread. Every modern OS has support for this, though the APIs change from OS to OS. Basically, a thread is a process that can run on its own processor, is scheduled by the OS scheduler, and can block. It acts just like its own process except that it shares resources with every other thread in the process. This mainly means that memory and file descriptors are shared between all threads in a process. This is what people mean by “native threading”. From C on linux, you can use these threads by linking with the pthread library. BSDs generally support pthreads as well, and Windows does its own thing that is very similar.
Java and Green ThreadsWhen Java came out, it introduced a different type of threading model to the world called green threads. Green threads are essentially simulated threads. The Java virtual machine would take care of switching between different green threads, but the virtual machine itself would only run in one OS thread. This generally has some advantages. OS threads have almost as much overhead as a process on most POSIX systems. It is also usually slower to switch between native threads than it is between green threads.
This can mean that in some situations, green threads are much preferable to native threads. A system can usually support a much higher number of green threads than OS threads. For instance, it would be practical to spawn a new green thread for every new connection on a web server, but it is not generally practical to spawn a new native thread for every incoming HTTP connection.
There are disadvantages, however. The biggest is that you cannot have two threads running at the same time. There is only one native thread, so it is the only thread that gets scheduled. Even if there are multiple CPUs and multiple green threads, only one CPU will be running any given green thread at any given time. This is because it all looks like one thread to the OS scheduler.
Java has supported native threading since version 1.2, and it has been the default for some time now.
PythonPython is one of my favorite scripting languages, and was one of the first scripting languages to offer threading. Python exposes a threading module that manipulates native threads. This means that Python can benefit from all the advantages of true native threading, except for one catch.
Python has a global interpreter lock (GIL). This lock is necessary to keep Python threads from corrupting the global state of the interpreter. This means that no two Python instructions can be running simultaneously. The GIL gets released every 100 Python instructions or so and another Python thread is free to acquire the lock and begin executing.
On the face of it, this seems like a major flaw. However, in practice it is not that big of a deal. Any thread that blocks will generally release the GIL. C extensions can also release the GIL whenever they are not interacting with the Python/C api, so CPU intensive operations can be carried out in C without blocking the executing Python threads. The only situation in which the GIL proves problematic is when you have more than one CPU bound thread written in Python on a multi-core machine.
Stackless python is an implementation of Python that brings “tasklets” (essentially green threads) to Python. The greenlet module is derived from their work and is compatible with the standard cPython implementation.
RubyRuby’s threading model is and always has been in a state of flux. Ruby’s original implementation only supported cooperative green threads. These work fine in many situations, but they do not take advantage of multiple processors.
JRuby mapped Ruby’s threads straight to Java’s threads, which are generally OS native threads. This doesn’t work. Since Ruby’s threads are cooperative, there is no need to synchronize between the threads. Every thread can be assured that no other thread is accessing a resource while it is accessing it. This breaks down in JRuby, since native threads are generally preemptive, meaning any thread could be accessing any shared data at any time.
Because of the mismatches and the desire for native threading from the C Ruby folks, it was decided that Ruby would move to a native threading in Ruby 2.0. In Ruby 1.9, a different interpreter was swapped into the standard Ruby distribution. 1.9 adds a threading model it calls Fibers, which as far as I know are a more efficient implementation of green threads.
In short, Ruby’s threading model is a poorly documented mess.
PerlPerl has an interesting threading model, one which Mozilla borrowed for SpiderMonkey if I’m not mistaken. Instead of having a global interpreter lock like Python, Perl makes all global state thread local and spawns off a new interpreter with each new thread. This allows for true native threading. There are two catches though.
First, you must explicitly make variables available to threads outside your own. This is the nature of everything being thread local. The values must then be kept up to date across threads.
The second catch is that every new thread is very expensive to create. The interpreter is not small, and duplicating it with every thread makes for a lot of overhead.
C# uses native threads.
Well, I hope that makes the whole threading picture a little bit clearer. Please let me know if anything is confusing or if I messed anything up. I don’t know everything, after all.