Introduction

In the TruffleRuby status report for 2016, we indicated that we planned to address our startup time problem in the coming year. We’re well aware that one of the biggest challenges facing alternative Ruby implementations is being competitive with MRI on startup time and memory consumption. While we have established a peak performance advantage over other implementations in many benchmarks, we haven’t had a very impressive story to tell for short-lived applications such as one-off Ruby scripts and test suites.

I’m extremely pleased to say that we’re making good on that promise. Starting with GraalVM 0.20, we’re now shipping a new virtual machine and ahead-of-time compiler that can produce static binaries of Truffle-based languages.

Glossary

There’s a lot of terminology involved in discussing TruffleRuby. If you’ve been following our project status you might already be familiar with them, but I assume many people are not. In order to make the remainder of this post easier to understand, I’ve provided a brief glossary below:

Graal An optimizing compiler for the JVM written in Java with hooks available to interact with it from Java. Truffle A self-optimizing AST interpreter framework for Java. When paired with Graal it is able to perform partial evaluation on the interpreter and user program to produce tight machine code for the running application. TruffleRuby An implementation of Ruby based on Truffle. Since it uses Truffle, the runtime is authored in Java, but much of the core library is written in Ruby. GraalVM A distribution containing a Graal-enabled JVM and runtimes for Truffle-based languages (currently TruffleRuby, Graal.js, and FastR) from Oracle Labs.

Performance

Isolating startup time from program execution can be a bit tricky. Rather than getting mired in the details, I’ve taken the measurement1 of an extremely simple program: ruby -e 'p "Hello, world"' . If you want to follow along, simply install GraalVM and build the TruffleRuby binary.

Real Time (s) Max RSS (MB) TruffleRuby SVM 0.20 0.24 128.6 TruffleRuby JVM 0.20 3.43 439.6 JRuby 9.1.7.0 1.55 200.4 Rubinius 3.69 0.27 71.1 MRI 2.4.0 0.05 8.8

Running TruffleRuby on the Substrate VM is 13 times faster than running on the JVM while only using 30% as much memory. We still have a ways to go before we catch up to MRI’s startup time, but TruffleRuby on the Substrate VM starts up faster than all other alternative Ruby implementations. It’s unlikely we’ll ever approach MRI’s memory consumption because we must retain runtime metadata for the Graal compiler, but we should be able to whittle it down further and run well on memory-constrained cloud servers.

Turning our attention to a more real world application, I ran the set of language specs from the Ruby Spec Suite. These specs look and run very similarly to a typical application’s test suite.

Real Time (s) Max RSS (MB) TruffleRuby SVM 0.20 9.00 1,364.1 TruffleRuby JVM2 0.20 68.38 560.8 JRuby3 9.1.7.0 37.57 380.6 Rubinius3 3.69 7.18 112.6 MRI 2.4.0 1.01 13.7

Test suites like this are generally hard on optimizing runtimes. They always start in a cold state, they run for a short period of time, and in the case of JVM-based languages they incur a high degree of overhead when spawning new processes. For this test suite, which has 2,121 specs and 3,824 assertions, TruffleRuby on the SVM is 6.6 times faster than TruffleRuby on the JVM, shaving almost a full minute off the test suite — a fairly substantial savings. However, this is a relative performance reduction from what we saw with the simple startup test, suggesting we have additional opportunities to reduce total run time.

At first blush, the increased max RSS value is concerning. The big difference here is that the SVM has a new generational garbage collector (GC) that’s different from the JVM’s. The SVM ahead-of-time (AOT) compiler uses a 1 GB young generation size and a 3 GB old generation size by default. That means the GC won’t even start collecting until TruffleRuby has allocated 1 GB of memory. Over the course of those specs we generate a lot of garbage. Fortunately, this isn’t an inherent limitation of either TruffleRuby or the SVM; the SVM can be used for applications other than TruffleRuby and simply defaults to a large heap as a conservative measure. For future releases we’ll look for better defaults for our needs.

The numbers show we haven’t quite caught up to MRI yet but we’re quickly closing the gap. As both the SVM and TruffleRuby continue to mature, we expect we’ll be able to approach MRI’s level of responsiveness. I think these initial results suggest the approach is viable and that our goal is realistic. We’re currently on a monthly release schedule and will continue to track these metrics.

If you’ve held off using TruffleRuby due to concerns about startup performance, now is a great time to experiment with it. Just keep in mind that this is an early release and there are certainly bugs. We do have an open issue tracker and love receiving reports from real workloads.

More about the Substrate VM

The Substrate VM is a project under the GraalVM umbrella at Oracle Labs, headed by Christian Wimmer. The basic idea behind the Substrate VM is to provide a new distribution mechanism for languages authored with Truffle. The Truffle framework is Java-based, which means languages wishing to make use of Truffle must also be written in Java. Authoring a language, such as Ruby, in a high level language like Java brings many advantages such as excellent IDEs, refactoring capabilities, performance & analysis tools, and a codebase that’s easy to maintain. However, it also brings with it disadvantages such as slower startup time, increased memory usage, and distribution difficulties.

The Substrate VM addresses those deficiencies by producing a static binary of a Truffle language runtime4. It performs an extensive static analysis of the runtime, noting which classes and methods are used and stripping away the ones that are not. The AOT compiler then performs some up-front optimizations such as trivial method inlining and constructs the metadata necessary for Graal to perform its runtime optimizations. The final output is a version of the Truffle language interpreter fully compiled to native machine code; i.e., there is no Java bytecode anywhere. As an added benefit, the binary size is considerably smaller than the JVM’s because all the unused classes and methods are excluded.

This is a powerful new addition to the Truffle toolchain. In addition to an optimizing JIT, GC, profiler, debugger, and built-in polyglot support, by implementing your language on top of Truffle you now get an ahead-of-time compiler for your interpreter.

As with all technology, there are trade-offs to targeting the Substrate VM. In order to perform its static analysis, all code to be included in the binary must be statically reachable. Consequently, reflection and dynamic class loading can’t be used. Additionally, arbitrary native code cannot be called. The Substrate VM does ship with built-in support for JNR POSIX to make native POSIX calls easier. And, as the Substrate VM is still a young project, certain APIs from the JDK might not yet be supported.

For code you have control over, these restrictions might be inconvenient, but are generally manageable. However, pulling in arbitrary 3rd party code can create headaches since it’s not quite as easy to avoid paths that don’t comply with the SVM’s restrictions. The SVM does include a mechanism to replace a method implementation at runtime, much like aspect-oriented programming, but this should be used a last resort.

Conclusion

The Substrate VM provides Truffle-based languages, such as TruffleRuby, with an incredible new way to deliver a language runtime. If you’ve been following the TruffleRuby project, you likely know we’ve been talking about the SVM for the past couple years as our solution to solving our startup time problem. I’m excited to say it wasn’t vaporware! The results for TruffleRuby on the SVM thus far are extremely promising and I think the release of the SVM marks a new evolutionary phase of the TruffleRuby project.

To me, one of the most amazing parts of all this is that the same TruffleRuby codebase is used to target the JVM, the GraalVM, and now the SVM. You no longer have to choose between fast startup and peak peformance — just use the best VM for your given context.

Additional Resources

If you’re interested in learning more about the history of TruffleRuby or get a deeper look at its internals, please take a look at the collection of resources Chris Seaton has assembled. Truffle and Graal are active research projects with a rich set of publications (Truffle, Graal) . If you’d like to learn more about implementing a language with Truffle, I suggest checking out the SimpleLanguage implementation and watching Christian Wimmer’s walkthrough of the code.

1 All measurements were taken on an i7-4930K running at 4.1 GHz and 48 GB RAM. The operating system was Ubuntu 16.04.1 with a 4.4 Linux kernel.

2 Due to a bug in GraalVM 0.20, the Ruby Spec Suite language specs do not run with runtime compilation enabled. For this evaluation I ran with a stock JVM, while the startup tests report the JVM with Graal.

3 Neither JRuby nor Rubinius pass 100% of the language specs from the Ruby Spec Suite. As a result, they error out on some specs that TruffleRuby and MRI pass. Since they’re not executing the same code the recorded time and memory values shouldn’t be taken as definitive.

4 There is no technical reason SVM can’t be used with arbitrary Java applications. However, its primary use case and the one driving development is Truffle-based language runtimes.

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