Tag Archives: Graal

SOMns 0.2 Release with CSP, STM, Threads, and Fork/Join

Since SOMns is a pure research project, we aren’t usually doing releases for SOMns yet. However, we added many different concurrency abstractions since December and have plans for bigger changes. So, it seems like a good time to wrap up another step, and get it into a somewhat stable shape.

The result is SOMns v0.2, a release that adds support for communicating sequential processes, shared-memory multithreading, fork/join, and a toy STM. We also improved a variety of things under the hood.

Note, SOMns is still not meant for ‘users’. It is however a stable platform for concurrency research and student projects. If you’re interested to work with it, drop us a line, or check out the getting started guide.

0.2.0 – 2017-03-07 Extended Concurrency Support

Concurrency Support

  • Added basic support for shared-memory multithreading and fork/join
    programming (PR #52)

    • object model uses now a global safepoint to synchronize layout changes
    • array strategies are not safe yet
  • Added Lee and Vacation benchmarks (PR #78)

  • Configuration flag for actor tracing, -atcfg=
    example: -atcfg=mt:mp:pc turns off message timestamps, message parameters and promises

  • Added Validation benchmarks and a new Harness.

  • Added basic Communicating Sequential Processes support.
    See PR #84.

  • Added CSP version of PingPong benchmark.

  • Added simple STM implementation. See s.i.t.Transactions and PR #81 for details.

  • Added breakpoints for channel operations in PR #99.

  • Fixed isolation issue for actors. The test that an actor is only created
    from a value was broken (issue #101, PR #102)

  • Optimize processing of common single messages by avoiding allocation and
    use of object buffer (issue #90)

Interpreter Improvements

  • Turn writes to method arguments into errors. Before it was leading to
    confusing setter sends and ‘message not understood’ errors.

  • Simplified AST inlining and use objects to represent variable info to improve
    details displayed in debugger (PR #80).

  • Make instrumentation more robust by defining number of arguments of an
    operation explicitly.

  • Add parse-time specialization of primitives. This enables very early
    knowledge about the program, which might be unreliable, but should be good
    enough for tooling. (See Issue #75 and PR #88)

  • Added option to show methods after parsing in IGV with
    -im/--igv-parsed-methods (issue #110)

Language Research with Truffle at the SPLASH’16 Conference

Next weekend starts one of the major conferences of the programming languages research community. The conference hosts many events including our Meta’16 workshop on Metaprogramming, SPLASH-I with research and industry talks, the Dynamic Languages Symposium, and the OOPSLA research track.

This year, the overall program includes 9 talks on Truffle and Graal-related topics. This includes various topics including optimizing high-level metaprogramming, low-level machine code, benchmarking, parallel programming. I posted a full list including abstracts here: Truffle and Graal Presentations @SPLASH’16. Below is an overview and links to the talks:

Sunday, Oct. 30th

AST Specialisation and Partial Evaluation for Easy High-Performance Metaprogramming (PDF)
Chris Seaton, Oracle Labs
Meta’16 workshop 11:30-12:00

Towards Advanced Debugging Support for Actor Languages: Studying Concurrency Bugs in Actor-based Programs (PDF)
Carmen Torres Lopez, Stefan Marr, Hanspeter Moessenboeck, Elisa Gonzalez Boix
Agere’16 workshop 14:10-14:30

Monday, Oct. 31st

Bringing Low-Level Languages to the JVM: Efficient Execution of LLVM IR on Truffle (PDF)
Manuel Rigger, Matthias Grimmer, Christian Wimmer, Thomas Würthinger, Hanspeter Mössenböck
VMIL’16 workshop 15:40-16:05

Tuesday, Nov. 1st

Building Efficient and Highly Run-time Adaptable Virtual Machines (PDF)
Guido Chari, Diego Garbervetsky, Stefan Marr
DLS 13:55-14:20

Optimizing R Language Execution via Aggressive Speculation
Lukas Stadler, Adam Welc, Christian Humer, Mick Jordan
DLS 14:45-15:10

Cross-Language Compiler Benchmarking—Are We Fast Yet? (PDF)
Stefan Marr, Benoit Daloze, Hanspeter Mössenböck
DLS 16:30-16:55

Thursday, Nov. 3rd

GEMs: Shared-memory Parallel Programming for Node.js (DOI)
Daniele Bonetta, Luca Salucci, Stefan Marr, Walter Binder
OOPSLA conference 11:20-11:45

Efficient and Thread-Safe Objects for Dynamically-Typed Languages (PDF)
Benoit Daloze, Stefan Marr, Daniele Bonetta, Hanspeter Mössenböck
OOPSLA conference 13:30-13:55

Truffle and Graal: Fast Programming Languages With Modest Effort
Chris Seaton, Oracle Labs
SPLASH-I 14:20-15:10

Towards Meta-Level Engineering and Tooling for Complex Concurrent Systems

Last December, we got a research project proposal accepted for a collaboration between the Software Languages Lab in Brussels and the Institute for System Software here in Linz. Together, we will be working on tooling for complex concurrent systems. And with that I mean systems that use multiple concurrency models in combination to solve different problems, each with the appropriate abstraction. I have been working on these issues already for a while. Some pointers are available here in an earlier post: Why Is Concurrent Programming Hard? And What Can We Do about It?

End of February, I am going to talk about that a little more at the Arbeitstagung Programmiersprachen in Vienna. Below, you can find an abstract and link to the position paper. There is not a lot of concrete material in yet, but it sketches the problems we will try to address in the years to come.


With the widespread use of multicore processors, software becomes more and more diverse in its use of parallel computing resources. To address all application requirements, each with the appropriate abstraction, developers mix and match various concurrency abstractions made available to them via libraries and frameworks. Unfortunately, today’s tools such as debuggers and profilers do not support the diversity of these abstractions. Instead of enabling developers to reason about the high-level programming concepts, they used to express their programs, the tools work only on the library’s implementation level. While this is a common problem also for other libraries and frameworks, the complexity of concurrency exacerbates the issue further, and reasoning on the higher levels of the concurrency abstractions is essential to manage the associated complexity.

In this position paper, we identify open research issues and propose to build tools based on a common meta-level interface to enable developers to reasons about their programs based on the high-level concepts they used to implement them.

  • Towards Meta-Level Engineering and Tooling for Complex Concurrent Systems; Stefan Marr, Elisa Gonzalez Boix, Hanspeter Mössenböck; in ‘Proceedings of the 9th Arbeitstagung Programmiersprachen’ (ATPS’ 16).
  • Paper: PDF, HTML
  • BibTex: BibSonomy

Add Graal JIT Compilation to Your JVM Language in 5 Easy Steps, Step 5

Step 5: Optimizing the Interpreter for Compilation

In the previous post of this series, we completed the support for executing Mandelbrot and saw that our interpreter reaches with the help of the Graal compiler the same performance as Golo's bytecode-based implementation.

In this post, we first introduce how the Graal compiler works for our interpreter, and then we are going to use IGV, a viewer for Graal's compilation graphs, to identify a performance bottleneck and optimize it.

Introduction to Graal's Compilation Approach

Conceptually, Graal is a pretty classic compiler. It works on a input program, applies all kind of optimizations structured in different compiler phases and based on that generates native code for the execution.

The way we use Graal is however a little unusual. We use it as a meta-compiler. So, instead of applying it to a specific input program, we apply it to an interpreter that executes a specific program. So, Graal is not specific to a language, but can compile any language that is implemented as a Truffle interpreter.

The compilation works very similar to many other just-in-time compilers, i.e., compilers that are executed at runtime based on a program's behavior. Graal will consider the compilation of methods for our Golo program once a certain execution threshold is reached. This usually means, when a Golo method was executed 1000 times, Graal will start compiling and optimizing it.

For our benchmark, it eventually compiles the mandelbrot(size) method that we saw in the previous posts. This method is represented as AST and Graal knows that the relevant entry point is the execute(.) method of the RootNode. So, it takes the AST and the code of the execute(.) method to start compiling. In the first step it partially evaluates the code of this method. In that process it will reach for instance field reads from AST nodes or method calls on these nodes. In these cases it can fill in the blanks based on the concrete AST it got. And since our AST already specialized itself to contain the most specific operations possible, this process works very well. In the end, this partial evaluation constructs the compilation unit. This means it gathers, some say inlines, all code that is reachable from the initial execute(.) method. This is a greedy process, so it inlines as much as possible.11 In some cases this is problematic. For more control, the @TruffleBoundary annotation can be used to stop inlining. The goal is to know as much about the execution of this method as possible. When it finds calls to other Golo functions, it uses a heuristic to decide whether to inline these, too. This means we end up with a large set of code that describes which operations our program performs and on which we can apply classic compiler optimizations. Generally, this process is pretty successful in removing all the overhead of the interpreter and yield native code that runs fast.

However, as we have seen in the previous post, we did still have an overhead of about 5x over Java, which is not really impressive. So, let's see why this is the case.

Setting up Graal and Golo

So far, we have not discussed the more practical issues of how to obtain the Graal compiler, it's tool, and Golo. Let's rectify that now. To investigate our performance issues, we need IGV. It is also known as the Ideal Graph Visualizer and is currently maintained as part of the Graal compiler. And we need of course the Golo implementation. In case you are more interested in the later discussions than following along yourself, feel free to skip to the next section.

First, we want the GraalVM binaries. The GraalVM is a release of the Graal compiler together with Oracle's language implementations for JavaScript, R, and Ruby. It can be downloaded from the Oracle Technology Network.

Unfortunately, IGV is not part of this release, so, we also need the source repository. This is a little more involved. We need the mx tool that is used for obtaining dependencies, building, and executing the development builds of Graal. As many OpenJDK projects, this project also uses Mercurial as version control system.

The following steps should result in a usable setup:

mkdir graal-ws; cd graal-ws             # create and change to a working folder
hg clone https://bitbucket.org/allr/mx  # get mx
./mx/mx sclone http://hg.openjdk.java.net/graal/graal-compiler  # get graal

Note, this can take quite a while. The repositories are rather large.

In the meanwhile, we can already get and compile the Golo branch with support for Mandelbrot. Use git to checkout the truffle/mandelbrot branch in the graal-ws folder. And then, use the provided Gradle script to compile everything:

git clone -b truffle/mandelbrot https://github.com/smarr/golo-lang.git
cd golo-lang
./gradlew instDist  # warning: this will download all dependencies

After we obtained the above mentioned GraalVM binary, the source repository, and Golo, we are all set for investigating the performance of our Golo interpreter.

How to use IGV to Inspect Compilation Results

The next step is to run our benchmark. Assuming that the GraalVM release was put into the graal-ws folder, we should be able to execute the following:

../GraalVM-0.9/bin/java -classpath build/install/golo/lib/golo-3.0.0-incubation-SNAPSHOT.jar:build/install/golo/lib/asm-5.0.4.jar:build/install/golo/lib/jcommander-1.48.jar:build/install/golo/lib/txtmark-0.13.jar:build/install/golo/lib/json-simple-1.1.1.jar fr.insalyon.citi.golo.cli.Main golo --truffle --files samples/mandelbrot.golo

This is the full command to run the Truffle interpreter on the GraalVM. Thus, it includes the classpath with all runtime dependencies, as well as the Golo command with the --truffle option to execute the samples/mandelbrot.golo file.

The output of the resulting execution should show that the Mandelbrot benchmark is executed 100 times.

Run IGV, Run Benchmark with IGV Output

At this point, we should be able to execute the Mandelbrot benchmark. Now, we are looking into understanding what it is doing. For that we use IGV. To start it, we need a new terminal and change to the graal-compiler folder, and start it using mx:

cd graal-compiler
../mx/mx igv

It might take a bit, but eventually a Java desktop application should start up. Once it is running, we can run the benchmark again, but this time asking it to dump all of the compiler information, which is then displayed by IGV:

../GraalVM-0.9/bin/java -Djvmci.option.Dump=Truffle,TruffleTree \
  -classpath build/install/golo/lib/golo-3.0.0-incubation-SNAPSHOT.jar:build/install/golo/lib/asm-5.0.4.jar:build/install/golo/lib/jcommander-1.48.jar:build/install/golo/lib/txtmark-0.13.jar:build/install/golo/lib/json-simple-1.1.1.jar \
  fr.insalyon.citi.golo.cli.Main golo --truffle --files samples/mandelbrot.golo

After completion, IGV should roughly the following:

IGV showing the compiled methods in the outline

In the outline pane on the left, we see six folders, which correspond to three compilation requests Graal received during the execution of the benchmark. At the top of the list, we see two compilations named RepeatNode. These correspond to loop bodies in the mandelbrot(.) method and the folder contain the concrete AST that was compiled. Then we see two entries from TruffleCompilerThreads, that are also about the RepeatNode. These folders contain the compiler graph. We will ignore all four of these folders and focus on the last two ones.

The last two folder correspond to the whole mandelbrot(.) method. The folder name Function@... is contains the AST and the folder from the TruffleCompilerThread contains the compiler graph.

Inspecting the AST

Let's first have a brief look at the AST that was compiled:

IGV showing the AST of the mandelbrot() method.

The screenshot here shows the satellite view (icon of magnifying glass in the middle). When we are in the normal view, we can navigate over the method, and inspect nodes. The right hand side of IGV should show a property window with details to the nodes. For instance the LocalVariable nodes contain the details on the slot object they hold, and thereby the variable names.

In general, each AST node shows it subnodes/child nodes. So, the LocalVariableWriteNodes refer to the value expression that returns the value to be written. At the beginning of the method that corresponds to the literals defined in the program.

When looking for instance at the LessThanNode, we see an IntegersNode_:

LessThanNode in IGV, showing specialization for Integers

This IntegersNode_ is generated by the TruffleDSL for the case that all arguments are int values. Thus, it corresponds to the @Specialization we implemented previously. Note also the UninitializedNode_ below it. In case the arguments should change and for instance lead to a comparison of floating point numbers, the DSL provides us with the support to handle this kind of polymorphism. Exploring the graph further would reveal that the mandelbrot(.) method is very well behaved and all operations only require a single specialization. So, it is ideal for compilation.

In general, a look at the Truffle AST is useful to get a very high-level overview and to confirm initial assumptions about what gets compiled. As we will see in the next step, this high-level view is unfortunately not preserved when looking at the compiler graphs.

Inspecting the Graal Compilation

When we open the last folder named TruffleCompilerThread..., we see two folders and one graph in-between the two. The first folder contains the details of how the partial evaluation of the AST is performed.

In the screenshot below, we see the initial method that was sent to compilation:

Graal's compilation Graph at the first step

To make the graph a little more readable, we select in the filter pane on the right the option of Coloring, Remove State, and Reduce Edges. Since those graphs are typically very complex, these simplifications help to get an overview of where is what.

In the graph, we see now the Start node and then various other ones. The red edges indicate control flow between those nodes, and the blue and black edges correspond to data dependencies. What we can read from this graph is that Truffle actually does argument type profiling for all Truffle methods. This is a very generic and usually beneficial optimization. Since we pass only untyped Object arrays, the additional type information helps the optimizer by providing the missing information.

At the bottom of the screenshot, we further see a red box that corresponds to the invoke of the isValid() method. Such method calls will typically be inlined during this partial evaluation process.

Graph after completing the Partial Evaluation Phase

The screenshot above shows the whole graph after the partial evaluation phase. It ended up being quite complex, and the nodes are way too small to be distinguishable, but with a little practice, one can spot the loops.

So, let's have a look at the second folder. This one contains the output for the various optimization passes applied by Graal. The list looks roughly something like this:

 0: initial state
 1: Canonicalizer
 2: Inlining
 3: DeadCodeElimination
 4: Canonicalizer
17: DominatorConditionalElimination
18: IterativeConditionalElimination
31: LoopFullUnroll
36: After inlining snippet HotSpotMethod<BoxingSnippets.doubleValue(Double)>
37: After lowering -100...|Unbox...
120: After inlining snippet HotSpotMethod<BoxingSnippets.intValueOf(int)>
121: After lowering -100...|Box...
186: After inlining snippet HotSpotMethod<NewObjectSnippets.allocateInstance(...)>
187: After lowering -100...|NewInstance...

The names of the phases give hints of what the change should be with respect to the previous graph. It includes further inlining, dead code elimination, loop unrolling, and many other classic compiler optimizations.

One thing that sticks out here however are almost a hundred passes related to boxing and unboxing of integer and double values. And later, we see another 60 or so passes that relate to object instantiation. This looks very strange. Our benchmark should mostly work on primitive numbers, here we really would not expect boxing or allocation going on. After all, Mandelbrot is a numerical benchmark. So, let's investigate that closer.

I chose to open the phase 28, the result after running the Canonicalizer. This is very early in the process, and the graph should now be in a normalized form. Browsing a little over it, trying to see where all those boxing operations come from, I chose to investigate some Box operation somewhere in the middle. I picked the one that has the number 11510 in my graph. Double clicking on it hides the whole rest of the graph in shows only the remaining siblings. From here I started to explore the context of this boxing operation. As a parent node, we see a subtraction operation, with the constant 1.5 as an input, as well as a division as input that itself got a multiplication with the constant 2.0 as input. The other input to the division is an unbox operation. This all does not look very promising.

Graph showing the context of a boxing operation

With those fragments of information, I'd say this piece of the graph corresponds to the following line of our mandelbrot(.) function:

var cr = (2.0 * doubleValue(x) / size) - 1.5

With that in mind, we can verify that the unbox operation corresponds to the reading of size, which is the function's argument, and thus, the value is stored as an object in an array. And, exploring the graph, we see the corresponding LoadIndexed at index 0. Ok, so, that's expected, and we can't really do something about that.

Now, why is the boxing operation there? A Phi node depends on the result of it, and that itself goes into a value proxy node, which seems to have another dependency of another Phi node, which then again is also the input for the Phi node that has the boxing operation a dependency.

What we can't see here at this stage in the graph is that the operations on the frame object, i.e., reading and writing of local variables has been converted to pure data dependencies. So, that ideally, there are not actual store operations but operations can consume the temporary results directly.

At this point, I would venture the guess that what we see here is the assignment to the cr variable. Considering the following implementation for local variable assignment, the result looks plausible:

abstract class LocalVariableWriteNode extends ExpressionNode {
  protected final FrameSlot slot;
  public LocalVariableWriteNode(FrameSlot slot) { this.slot = slot; }

  public Object writeGeneric(VirtualFrame frame, Object expValue) {
    frame.setObject(slot, expValue);
    return expValue;
// ...

Currently, our local variables only consider objects. Unfortunately, Graal cannot automatically remove the boxing operations for us, so, we need to optimize this manually.

Type-Specialization of Access to Local Variables

To avoid the boxing, we want to store primitive values directly into the frame objects. We'll do this by providing additional specializations for the LocalVariableWriteNode and LocalVariableReadNode classes.

Let's start with writing to variables. We need to add new specializations to the class. Depending on the type of the value that the expr returns, we can use the frame's type-specific setters:

@NodeChild(value = "expr", type = ExpressionNode.class)
abstract class LocalVariableWriteNode extends ExpressionNode {
  protected final FrameSlot slot;
  public LocalVariableWriteNode(FrameSlot slot) { this.slot = slot; }

  @Specialization(guards = "isIntKind()")
  public int writeInt(VirtualFrame frame, int expValue) {
    frame.setInt(slot, expValue);
    return expValue;

  @Specialization(guards = "isDoubleKind()")
  public double writeDouble(VirtualFrame frame, double expValue) {
    frame.setDouble(slot, expValue);
    return expValue;

  @Specialization(contains = {"writeInt", "writeDouble"})
  public Object writeGeneric(VirtualFrame frame, Object expValue) {
    frame.setObject(slot, expValue);
    return expValue;

  protected final boolean isIntKind() {
    if (slot.getKind() == FrameSlotKind.Int) { return true; }
    if (slot.getKind() == FrameSlotKind.Illegal) {
      return true;
    return false;

  protected final boolean isDoubleKind() {
    if (slot.getKind() == FrameSlotKind.Double) { return true; }
    if (slot.getKind() == FrameSlotKind.Illegal) {
      return true;
    return false;

We added two new specializations for writing int and double values to the frame. The specialization are based on the implicit check of the result type of the expValue evaluation, which is implied by the method signatures, as well as the defined guards. Since other AST nodes can cause the type of a variable to change, we need to guard a specialization based on the recorded type in the frame slot. Generally, the idea is that we can use a specialization if the slot has either the right type or is not yet initialized. The slot is uninitialized if its kind is Illegal.

Since we now have three different specializations, we need to think about their relation at runtime. By designing the guard so that it fails if unexpected type is found, we make sure that we go to the most general case writeGeneric. This avoids changing back and forth between specialization, which would prevent compilation. Furthermore, we tell the writeGeneric specialization that it contains the two more specific ones. This allows the DSL to generate the right code, so that it really uses only the writeGeneric version and remove previously used specializations to avoid runtime overhead.

The read node is a little simpler:

abstract class LocalVariableReadNode extends ExpressionNode {
  protected final FrameSlot slot;

  public LocalVariableReadNode(FrameSlot slot) { this.slot = slot; }

  @Specialization(guards = "isUninitialized()")
  public Object doNull() { return null; }

  @Specialization(guards = "isInitialized()",
                  rewriteOn = {FrameSlotTypeException.class})
  public int doInt(VirtualFrame frame) throws FrameSlotTypeException {
    return frame.getInt(slot);

  @Specialization(guards = "isInitialized()",
                  rewriteOn = {FrameSlotTypeException.class})
  public double doDouble(VirtualFrame frame) throws FrameSlotTypeException {
    return frame.getDouble(slot);

  @Specialization(guards = "isInitialized()")
  public Object doObject(final VirtualFrame frame) {
    return frame.getValue(slot);

  protected boolean isInitialized() {
    return slot.getKind() != FrameSlotKind.Illegal;

  protected boolean isUninitialized() {
    return slot.getKind() == FrameSlotKind.Illegal;

The first specialization uses the slot kind to specialize for the case that a read is done on a variable that has not yet been assigned a value, thus, it returns null. The remaining three specializations are for the int, double, or finally the generic Object case. In the interpreter this means, we first try to read from a slot as int, if that fails, we catch the FrameSlotTypeException and go to the next specialization. In this case, this is the doDouble specialization. If that one fails as well, we go to the last specialization that will always succeed by reading a boxed object.

Measuring the Results

With this optimization in place, let's see what it gives. We use the same details for execution as earlier and get the following results:

Final performance of Golo+Graal JIT compilation

This time around, Golo+Graal is about as fast as Java. It is roughly 18% slower than Java, but about 4x faster than Golo with its bytecode-based backend on top of the Hotspot JVM. With a little bit more optimizing, we would probably be able to squeeze a little bit more out of Golo+Graal, but, let's call it fast enough for now.


In this post, we discussed how Graal compiles our interpreter, how to use IGV to get a better understanding of what is going on, and how we can optimize the access to local variables to avoid excessive overhead because of boxing. With the optimization, we reached Java's speed within 20% and are 4x faster than Golo was before. Overall, a pretty nice result.

However, we had to built a new interpreter from scratch, which took quite a bit of time and code. And, the implementation is nowhere near completion. To execute Golo programs, we would still need to implement a large number of things. For instance, how to support its dynamic objects based on the Truffle object model, how to use the @Cached support of the DSL for inline caches, perhaps how to optimize tail-calls to encourage a more functional programming style, etc. Plenty of things left to discuss. But for now, this post completes this series. I hope you found it useful.

If you start with your on project, drop by the Graal mailing list or check the archives. We will try to answer your questions. Truffle looks somewhat simple in the beginning, but my experience is that it takes quite a bit to get used to thinking in the way the interpreter and compilation interact to get great results.

Truffle Concepts

To close off this post, a brief list of the introduced concepts:

  • @TruffleBoundary asks Graal to stop inlining here during the partial evaluation pass. This is necessary for instance for recursive code, when the termination condition is not based on a compile-time constant.
  • Guards encode conditions for when a specialization is applicable.
  • contains denotes specializations that are subsumed by a more generic specialization.
  • @Cached is an annotation to support custom inline caches in specializations.


I'd like to thank Julien Ponge for his comments on drafts of this tutorial and his help with the technical details of Golo. And thanks to Gilles Duboscq for spotting typos.

Creative Commons License
“Add Graal JIT Compilation to Your JVM Language in 5 Easy Steps” by Stefan Marr is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Permissions beyond the scope of this license are available on request.

Add Graal JIT Compilation to Your JVM Language in 5 Easy Steps, Step 4

Step 4: Complete Support for Mandelbrot

In the previous post of this series, we built up all the infrastructure to execute a simple Fibonacci function with a Truffle interpreter for Golo. This included an introduction to the basic aspects of Truffle, its support for specializations, the idiomatic ways for realizing sequences, control flow, basic operators, and literals. Together with the discussion of how function invocation can be implemented, we covered most aspects that are required also for our final goal, i.e., to support the execution of the Mandelbrot program from the very first post in the series.

Below, the main part of the Mandelbrot program implemented in Java:

while (y < size) {
  double ci = (2.0 * y / size) - 1.0;
  int x = 0;

  while (x < size) {
    // ...
    double cr = (2.0 * x / size) - 1.5;

    int z = 0;
    int escape = 1;

    while (z < 50) {
      zr = zrzr - zizi + cr;
      zi = 2.0 * zr * zi + ci;

      zrzr = zr*zr;
      zizi = zi*zi;

      if (zrzr + zizi > 4.0) {
        escape = 0;
      z += 1;

    byte_acc = (byte_acc << 1) | escape;
    bit_num += 1;
// remainder left out for brevity

Since much of what we needed to support Mandelbrot are extensions, or based on the same principles that we discussed before, this post will be much shorter and only sketch the changes to existing elements. However, we discuss in greater detail how to add local variables, loops, and how to call Java functions based on method handles.

Extensions for Longs, Doubles, Strings, and Basic Operations

In a first step, we extend the Types class and the ExpressionNode to support long and double primitives as well as Strings for specialization. The Types class looks now like this:

  long.class,    // added for Mandelbrot
  double.class,  // added for Mandelbrot
  String.class,  // added for Mandelbrot
class Types { }

In the ExpressionNode, we merely add the missing executeLong(.), executeDouble(.), and executeString(.) methods in the same way as the existing ones. This will then allow a node to speculate on the child nodes always evaluating, for instance, to a primitive long value.

As we can see in the Mandelbrot program above, we also need support for literals. Specifically, we are going to add nodes for literal long, double, and string values. Furthermore, we add nodes for the true, false, and null literals. With the IntegerLiteralNode from the last post, we already saw the general idea. For true, false, and null it is even simpler, we merely return the fixed value on execution. So, it looks like this:

class NullLiteralNode extends LiteralNode {

  public Object executeGeneric(VirtualFrame frame) {
    return null;

Since Mandelbrot is mostly about computation, we also need a range of additions and extensions to support multiplication, division, addition, subtraction, less-than, equal as well as not-equal. Furthermore, we need to implement the Truffle versions of the xor, or, and left shift bit operators.

Below find a sketch of the addition as implemented by the PlusNode. The sketch shows the support for doubles and strings. Note that is also has a doStringAndDouble(.,.) method. This is because the child nodes have of course different semantics so that we need to specify the desired behavior for all combinations as well.

abstract class PlusNode extends BinaryNode {

  public String doStrings(String left, String right) {
    return left + right;

  public double doDoubles(double left, double right) {
    return left + right;

  public String doStringAndDouble(String left, double right) {
    return left + right;
  // ... and a few other combinations, incl. int and long

The bit operations are only defined for integers and longs, so the resulting BitXorNode looks like this:

abstract class BitXorNode extends BinaryNode {
  public long doLongs(long left, long right) {
    return left ^ right;

  public int doIntegers(int left, int right) {
    return left ^ right;

Unary Operations: Type Coercions and the not Operator

Next on our list are unary operators for value casting. Specifically, we need to implement intValue(.) and doubleValue(.). You might remember from the last post that we handled the println(.) method specially. It is a method defined on the Golo's Predefined class that takes only one parameter, too. Our solution the last time was to create a PrintlnNode during the lookup and directly replace the UninitializedInvocationNode AST node with this node. During execution, this has the major advantage that there is no overhead for function calls or even argument wrapping. To properly support Mandelbrot in Golo, we need explicit coercions for integer and double values. Of course, these are simple operations that should not have any overhead. Thus, we use the same approach.

The nodes are defined as follows:

@NodeChild(value = "expr", type = ExpressionNode.class)
abstract class UnaryNode extends ExpressionNode { }

abstract class IntValue extends UnaryNode implements PreEvaluated {

  abstract Object executeEvaluated(VirtualFrame frame, Object value);

  public Object doEvaluated(VirtualFrame frame, Object[] args) {
    return executeEvaluated(frame, args[0]);

  public int doDouble(double value) {
    return (int) value;

On the first look, this seems to be more complicated than necessary. The reason that we implement the PreEvaluated interface is because of the way node specialization should work. Normally, a specialize(.) method will replace the old node in the tree and return the new node so that it can be executed directly (see previous post). However, it needs to take the already evaluated arguments. For this purpose, we implement the interface and the corresponding doEvaluated(.) method. The implementation for the abstract executeEvaluated(.) will be generated by the TruffleDSL and we do not have to do that manually. The main functionality of the node is realized with the specialization doDouble(.), which merely casts the double value to an integer value. For the doubleValue(.) operation, the implementation is essentially the same.

The not operator is handled differently in Golo. Instead of being a function invocation, it is a keyword. We will skip over how to add the necessary support to the Golo IR to Truffle visitor. In the end, the executing AST node is simply this:

abstract class NotNode extends UnaryNode {
  public boolean doBoolean(boolean value) {
    return !value;

Local Variables

One issue we have kept putting off so far is to support the notion of local variables. For our Fibonacci function, we merely needed support for accessing arguments (see previous post, sec. 4.2). This was realized by accessing the array that is passed on function invocation and is stored in the frame object given to the execute*(.) methods. Now, we will use these frames to also store local variables.

As a little bit of background, frames are the activation records of functions. Thus, they keep the temporary state that is needed during the execution of a function. This includes access to the actual arguments, local variables, and possibly other execution state.

Truffle distinguishes two types of frames, virtual and materialized frames. The first type is called virtual to indicate that the optimizer will not allocate an object for the frame at runtime, instead, it will use the frame to figure out the data dependencies between operations within a compilation unit, i.e., a function and possibly additionally inlined functions. To make this reliable the usage of virtual frames is restricted. For instance, they are not supposed to be assigned to fields of other objects and cannot be passed to methods of objects where Graal cannot determine the concrete method at compilation time. Generally, virtual frames cannot escape the compilation unit, because this would mean that they need to be represented as a proper object. Materialized frames on the other hand can be used as normal objects. Graal does not impose the same restrictions on them, but instead, they come with a runtime cost. Materialized frames are useful to implement features like closures.

In addition to giving the compiler the means to determine data dependencies, frames are also meant to help it with determining the concrete type information. For both reasons, frames come with a FrameDescriptor. On the one hand, it maintains the structural information about the slots of a frame, and on the other hand, the frame slots can record type information. For now, we will focus on the structural elements, and will ignore the type information.

What does this mean concretely for our interpreter? For our Function objects it means, we need to give them proper FrameDescriptors so that the structure of the frame is known. We do that in our visitFunction(.) method:

public Function visitFunction(GoloFunction function) {
  FrameDescriptor frameDesc = new FrameDescriptor();

  ExpressionNode body = function.getBlock().accept(this);

  return new Function(body, function, frameDesc);

While it is not really necessary to support Mandelbrot, we have a stack of frameDescriptors in the visitor, because Golo supports nested functions. And with this stack, we can model the lexical scoping of them correctly.

To access variables, we need to add support for it to the visitReferenceLookup(.) method, next to the support for reading arguments:

public ExpressionNode visitReferenceLookup(ReferenceLookup referenceLookup) {
  LocalReference reference = referenceLookup.
  if (reference.isArgument()) {
    return new LocalArgumentReadNode(reference.getIndex());
  } else {
    FrameSlot slot = getFrameSlot(reference);
    return LocalVariableReadNodeGen.create(slot);

FrameSlot getFrameSlot(LocalReference reference) {
  return context.frameDescriptors.peek().

When we see that a reference is not an argument, we create a LocalVariableReadNode. This read node gets a FrameSlot object that is created by the frame descriptor based on the name of the variable. Since we do not care about type information for the moment, a frame slot is merely Truffle's handle to represent the variable.

Assignment statements are transformed similarly:

public ExpressionNode visitAssignmentStatement(
    AssignmentStatement assignment) {
  LocalReference reference = assignment.getLocalReference();
  FrameSlot slot = getFrameSlot(reference);
  return LocalVariableWriteNodeGen.create(
      slot, (ExpressionNode) assignment.

For them, we create a LocalVariableWriteNode. But of course, a write node still needs the expression that computes the value it needs to write. So, beside the slot, it also gets the transformed subexpression of the assignment statement.

For the implementation of the read node, we got the following code:

abstract class LocalVariableReadNode extends ExpressionNode {

  protected final FrameSlot slot;

  public LocalVariableReadNode(FrameSlot slot) {
    this.slot = slot;

  public Object doObject(VirtualFrame frame) {
    return frame.getValue(slot);

The node is structured as usual. It takes the slot object as argument and stores it in a final field so that the compiler knows it can rely on it as a constant. The specialization does merely take the slot object to read a value from the frame. There could be more specialization to read different primitive types from the frame, but for the moment this is not necessary.

The write node looks more or less the same:

@NodeChild(value = "expr", type = ExpressionNode.class)
abstract class LocalVariableWriteNode extends ExpressionNode {

  protected final FrameSlot slot;

  public LocalVariableWriteNode(FrameSlot slot) {
    this.slot = slot;

  public Object writeGeneric(VirtualFrame frame, Object exprValue) {
    frame.setObject(slot, exprValue);
    return exprValue;

Here we need to have the expression for the value that is to be written. So, it is declared with an annotation. In the specialization, we get it as the expValue argument. Note, when executing writeGeneric(.), we first ensure that the slot kind has been set to Object. The optimizer is able to remove this, because it only reaches frame slots that have been initialized. Finally, we set the expValue on the frame.

And that's all. With these nodes we support read and write operations for local variables.


The final element we need for the Mandelbrot function is iteration, or loops. Golo got multiple types of loops including for and while loops. Fortunately for us, Golo's IR already takes care of desugaring these constructs to a single loop construct with explicit initialization, condition, body, and post-condition.

The Main Loop Node

We translate this construct to the following node implementation:

class ForLoopNode extends ExpressionNode {

  @Child protected ExpressionNode init;
  @Child protected LoopNode loopNode;

  public ForLoopNode(ExpressionNode init,
      ExpressionNode condition,
      ExpressionNode body,
      ExpressionNode post) {
    this.init = init;
    loopNode = Truffle.getRuntime().createLoopNode(
        new RepeatNode(condition, body, post));

  public Object executeGeneric(VirtualFrame frame) {
    if (init != null) {
    try {
    } catch (BreakLoopException e) { /* just left the loop */ }
    return null;

Our ForLoopNode got two @Child nodes. The first one is the init expression. In a for loop like in Java or C, it typically initializes an induction variable, e.g., i, with an initial value. The second child node is Truffle's LoopNode, which realizes the iteration with support for on-stack-replacement. More on that in a bit.

The executeGeneric(.) first executes the init expression if available, and afterwards the loop. In case the loop contained a break keyword, we would throw a control-flow exception that is caught here.

Using Truffle's LoopNode

The actual iteration happens in the LoopNode. For that, it requires an implementation of the RepeatingNode interface. The interface has a single method which does a loop iteration and indicates whether it should continue. As mentioned before, this abstraction is ment to enable on-stack-replacement. This is a useful feature for long-running methods that contain loops. Normally, the execution would only reach a newly compiled native code version of a method when it is entered again. With on-stack-replacement however, the VM can detect methods with long-running loops and switch to the optimized native code even while executing the loop.

Our implementation of the RepeatingNode interface looks as follows:

class RepeatNode extends Node implements RepeatingNode {

  @Child protected ExpressionNode condition;
  @Child protected ExpressionNode body;
  @Child protected ExpressionNode post;

  public RepeatNode(ExpressionNode condition,
      ExpressionNode body, ExpressionNode post) {
    this.condition = condition;
    this.body = body;
    this.post = post;

  public boolean executeRepeating(VirtualFrame frame) {
    if (shouldExecute(frame)) {
      if (post != null) {
      return true;
    } else {
      return false;

  private boolean shouldExecute(VirtualFrame frame) {
    try {
      return condition.executeBoolean(frame);
    } catch (UnexpectedResultException e) {
      throw new UnsupportedSpecializationException(
          this, new Node[]{condition}, e.getResult());

It got child nodes for the loop condition, the loop body, and a post expression that is evaluated after each iteration. The method shouldExecute evaluates the loop condition. But since Golo is a dynamically typed language, we can't be completely sure that the condition expression really returns a boolean. Here we handle this case simply be throwing an exception that indicates that this case is not yet supported.

The executeRepeating(.) method represents one iteration. Thus, it first checks whether the loop should continue executing by invoking shouldExecute(.), and if that is the case, it executes the loop's body. After executing the body, it also executes the post expression if it is present. The return value of executeRepeating(.) indicates whether the iteration should be continued. Beside the minor complications, implementing a loop construct is thus pretty simple. If on-stack-replacement is not desired for some reason, one could implement the loop also directly using any Java loop construct.

The break Keyword in Loops

As we have seen earlier, Golo also supports the break keyword to exit from loops. The implementation idea is very similar to handling the return keyword. The AST node for the break keyword is going to throw a control-flow exception. And as we have seen in the ForLoopNode, we handle it with a try {...} catch (BreakLoopException e) around the execution of the loop body. Thus, the break keyword is implemented with the following BreakLoopNode:

class BreakLoopNode extends ExpressionNode {

  public Object executeGeneric(VirtualFrame frame) {
    throw new BreakLoopException();

The node will simply raise the BreakLoopException which returns execution to the ForLoopNode. After catching the exception, execution simply continues after the loop.

Invoking Arbitrary Java Methods

Golo is designed to be a dynamic language for the JVM ecosystem. Thus, it embraces Java and its ecosystem and Golo programs use Java libraries throughout. So far, we added dedicated nodes for each functionality. Applying the same approach to call arbitrary Java methods would not work. So, we need a way to call Java methods in a more reflective fashion.

Since Golo's lookup mechanism returns for some cases already Java MethodHandles, we added a MethodHandleInvokeNode:

class MethodHandleInvokeNode extends FunctionInvocationNode {
  private final MethodHandle method;

  public MethodHandleInvokeNode(FunctionInvocationNode uninit,
      MethodHandle method) {
    super(uninit.name, uninit.module, uninit.argumentsNode);
    this.method = method;

  public Object executeEvaluated(VirtualFrame frame,
      Object[] args) {
    try {
      return method.invokeWithArguments(args);
    } catch (Throwable e) {
      throw new NotYetImplemented();

This node becomes part of the function invocation specialization discussed in the previous post. So, if we get a MethodHandle as return, the UninitializedFunctionInvocationNode rewrites itself to this MethodHandleInvokeNode. When it is executed, it simply calls invokeWithArguments(.).

For our benchmark, this is used to call the methods that read the System's time.


In this post, we discussed how the interpreter can be extended to support the Mandelbrot program. For this, we extended the TypeSystem, added arithmetic operations, cast operators, support for local variables, support for Golo's loop constructs, as well as the ability to call arbitrary Java methods.

The main goal for this series of posts was to improve the numerical performance of Golo by using the Graal just-in-time compiler. So, let's look at the results. When we execute Mandelbrot now on top of Graal using our Truffle interpreter, we get the following results:

Initial Performance of Golo+Truffle

On Java, Mandelbrot takes about 108ms to execute. With Golo's bytecode compilation backend, it takes about 512ms. And, as we can see on the plot above, Golo running with the Truffle-based interpreter on top of Truffle takes about 505ms. This means, our Golo+Graal is about the same speed as the bytecode-based version.

In the next post, we'll investigate how the compilation works, and optimize our interpreter. It should not be much slower than Java, which it unfortunately still is. But more on that next time.

Truffle Concepts, Conventions, and Other Important Bits

As at the end of the last post, here also a brief overview of relevant Truffle concepts:

  • VirtualFrame object (sec. 3) cannot escape a Graal compilation unit, they cannot be assigned to fields of objects, or passed to polymorphic functions.
  • MaterializedFrame objects (sec. 3) can be used as normal objects, but have a runtime overhead.
  • FrameDescriptor (sec. 3) objects define the set of local variables in a method activation in terms of FrameSlots, which can track their runtime types.
  • The LoopNode class (sec. 4.2) enables Graal to perform on-stack-replacement to be able to optimize long-running loops during their execution


I'd like to thank Julien Ponge for his comments on drafts of this tutorial and his help with the technical details of Golo.

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“Add Graal JIT Compilation to Your JVM Language in 5 Easy Steps” by Stefan Marr is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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