| Ruby Implementation | Concurrency | Parallelism |
|---|---|---|
| MRI 1.8 | ✔ | |
| MRI 1.9 | ✔ | |
| Rubinius 1 | ✔ | |
| Rubinius 2 | ✔ | ✔ |
| JRuby | ✔ | ✔ |
| MacRuby | ✔ | ✔ |
| Maglev | ✔ | |
| IronRuby | ✔ | ✔ |
A big topic in the world of Ruby this year has been how to get more out of Ruby, specifically, how to get more done in parallel. The topic of concurrency, though, is one fraught with misunderstanding. This is largely due to the complexities of not only thinking about multiple things at once, but the limitations of Ruby implementations and operating systems.
In this article, I’ll lay the groundwork for understanding the difference between concurrency and parallelism. Then, I’ll look at how a programmer experiences them.
Concurrency vs. Parallelism
This has been discussed many times, but I sometimes still have difficulty with it. Let’s first break down the definitions of these two words:
- Concurrent: existing, happening, or done at the same time
- Parallel: occurring or existing at the same time or in a simple way
Hmm, ok. Well, that hasn’t improved our thinking about these two topics. We need to dig deeper into how the world of computing applies to these words. Rather than looking at the abstract, let’s instead consider some real world examples.
A “Real World” Example
Let’s say you’ve sat down for the evening to complete tomorrow’s homework. This evening you’ve got both Math and History worksheets to fill out. Tonight for some reason, you decide to do one problem in Math, then one problem in History, then back to Math, etc until all the problems are done.
In the parlance of computing, you’re now doing your Math and History worksheets concurrently. This is because your Current task list includes 2 items: Math worksheet and History worksheet.
Now, clearly you the reader can see a problem here. By switching back and forth, completing your homework will probably take longer than if you did the complete Math worksheet then did the History worksheet. In other words, if you did the worksheets in serial.
So, if concurrent means “having multiple outstanding tasks at once”, then what is parallel? Parallel is the ability to make progress on multiple tasks simultaneously.
Let’s say you’ve been asked to read the book One O’Clock Jump by Lise McClendon. You also need to drive down to San Diego for Comic-Con. Thankfully you find that One O’Clock Jump is available on audiobook!
You can now listen to the book while driving. You’re simultaneously making progress on two separate tasks. This is the equivalent of parallelism in computing.
I hope that these real world examples help illustrate the difference between concurrency and parallelism. Now let’s apply this newfound knowledge to Ruby.
Back to Ruby
One reason this problem can be difficult to understand is because Ruby only provides a single mechanism for concurrency. But, whether or not these Threads are parallel depends on a number of factors.
MRI 1.8
Let’s look at MRI 1.8 (and MRI forks such as REE) to begin with, because it has the simplest model. MRI 1.8 uses a technique known as “green threads” to implement Threads. This means that every once in a while (around 100 milliseconds), the program says “oh, I should let another thread run now!” This saves the current info into the current thread and restores another thread. This is exactly like our homework example above. We can have as many things as we’d like in our task list, but we can only make progress on one of them at a time.
There is a wrinkle in the concurrency/parallelism game that I haven’t mentioned before now. This wrinkle is IO, namely how Threads interact when waiting for some external event. MRI 1.8.7 is quite smart, and knows that when a Thread is waiting for some external event (such as a browser to send an HTTP request), the Thread can be put to sleep and be woken up when data is detected. This simple consolation improves the usage of Threads so much that for a very long time the MRI 1.8.7 model was good enough for all Ruby programs.
MRI 1.9
Switching back to Ruby implementations, let’s look at MRI 1.9. As has been previously reported, MRI 1.9 removes the “green threads” we had in MRI 1.8 and uses native threads to implement the Thread class. Now, what are these “native threads”? These are are units of concurrency that the underlying operating system is aware of. A big reason to switch to use native threads is that it vastly simplifies the implementation of Threading. The operating system handles the low level parts of saving and restoring Thread information in a completely transparent way. Additionally, letting the OS know what parts of a program should be concurrent allows it to use the full resources of the computer to make that happen. In this modern world, that means using multiple cores.
Up until now, all we’ve talked about with Ruby’s Threading model was about concurrency, the ability to have multiple outstanding tasks at once. Now when we add in the idea of multiple cores, we can finally talk about parallelism. When a computer includes multiple cores (which is pretty much every computer now), those cores can run different code simultaneously, providing true parallelism. When a computer only has one core, there is no true parallelism, instead there is just simple concurrency, even at the OS level. The OS manages all the processes and threads in the system the same way you handled your Math and History worksheets, doing one for a little while, then grabbing another one.
Back to multiple cores though. Now that there is the opportunity to run things truly in parallel, we have to look at if Ruby can take advantage of that. Since MRI 1.9 uses OS threads, it can actually spread out your Ruby Threads to multiple cores!
Unfortunately, MRI 1.9 prevents the Ruby code itself from running in parallel by requiring that any thread running Ruby code hold a lock. This lock is commonly knows as the GIL (Global Interpreter Lock) or GVL (Global VM Lock).
There are a few reasons the GIL to exists, but for this discussion we will say that it’s because the non-Ruby parts of MRI 1.9 are not thread-safe. This means if data were manipulated by multiple threads at the same time, the data could become corrupt. The important thing for this post is how it applies to parallelism: the GIL inhibits parallelism within Ruby code.
MRI 1.9 uses the same technique as MRI 1.8 to improve the situation, namely the GIL is released if a Thread is waiting on an external event (normally IO) which improves responsiveness. MRI 1.9 also includes an experimental API that C extensions can use to run some C code without the GIL locked to utilize parallelism. This API is very restrictive though because no Ruby object may be accessed in any way while the GIL is not held by the current thread.
That about sums up the situation with MRI 1.8 and 1.9 with regards to concurrency and parallelism. Both provide concurrency of Ruby code, but neither provide parallelism of Ruby code.
Rubinius
Let’s take a quick look at other Ruby implementations where things are a bit different than MRI. I’ll start with Rubinius, since it’s the one I’m most familiar with. Rubinius 1.x also had a GIL and worked pretty much the same as MRI 1.9. With the upcoming 2.0 release though, the GIL will be removed, allowing Ruby code to run fully concurrent and fully parallel. We think this opens up a lot of uses for Ruby (parallel algorithms, etc) that Rubinius couldn’t handle well previously.
JRuby
JRuby layers the Thread class on top of Java’s thread class, so the threading model is whatever the JVM supports. That being said, OpenJDK is the primary JVM; it puts a Java thread directly onto an OS thread with no GIL. Thusly, JRuby almost always has full concurrency and parallelism available to it.
MacRuby
MacRuby also uses Cocoa’s NSThread as its abstraction, which runs without a GIL. So, this is another fully parallel implementation.
Maglev
Maglev runs directly on top of a Smalltalk VM and thusly layers the Thread class on top of a concept called Smalltalk Processes. In this case, the GemStone VM implements Processes in the same way as MRI 1.8, namely via “green threads” that don’t expose concurrency to the OS, and therefore, have no parallelism.
IronRuby
Lastly, IronRuby layers Thread directly on top of CLR’s threads without a GIL.
Conclusion
I hope that this helps to clear up what concurrency and parallelism are and how the different Ruby implementations address them. Having this understanding is critical for discussing and understanding topics such and thread-safety of libraries and performance of applications.
In future posts, we’ll look to build on this knowledge to help you make the best use of Ruby!