Improving the Ideal Graph Visualizer for better comprehension of Java's main JIT compiler
Hello, world! This first post describes a tool to visualize the inner workings of Java’s main JIT compiler, and our work to make this tool more useful for current and potential users.
Making sense out of a large, complex JIT compiler
Program comprehension is one of the main challenges in maintaining large and complex software systems. After a few months trying to find my way around C2 (the main JIT compiler in OpenJDK, the reference Java implementation), I can attest to that! Luckily, optimizing compilers like C2 are often designed around a well-specified representation of the code under compilation: an Intermediate Representation, or just “IR”. Understanding the structure and main invariants of the IR is often the gateway to understanding the compiler itself. As Eric Raymond put it (paraphrasing Fred Brooks): “Show me your code and conceal your data structures, and I shall continue to be mystified. Show me your data structures, and I won’t usually need your code; it’ll be obvious”.
Ideal Graph Visualizer is our friend
Luckily for those of us who maintain and improve C2, a tool is available to explore the IR of a program through its different C2 compilation phases: Ideal Graph Visualizer (IGV). IGV is not only “visual”, but also interactive, making it possible to disentangle, with a few mouse clicks, the complex graph that lies at the core of C2’s IR. Created in 2007 by Thomas Wuerthinger as part of his master’s thesis, IGV is used today as much by C2 newcomers (like me) as by the most experienced engineers. Time saved by using IGV means more time can be spent improving C2 itself!
Improvements in JDK 17
Given the value provided by IGV, we have recently set about improving the tool so that it can support even better the needs of both experienced and new C2 engineers. Among other improvements, we have fixed the most frequent crashes, simplified the build process, introduced support for the latest JDK versions, made it easier to search for specific nodes in the IR graph, and introduced more intuitive coloring schemes and default filters to focus on different aspects of the IR.
These improvements have already made it into OpenJDK’s main repository, and will be part of JDK 17. Even thought IGV, as an internal development tool, is typically not distributed with the virtual machine, we have made it very simple to build it if you want to give it a try.
In the future, we would like to consolidate the functionality already supported by IGV as well as extend IGV with new use cases. After talking with a fair number of C2 engineers from different organizations, it is clear that there is interest and no shortage of ideas for improving IGV! For example, a recurring theme is that, while IGV has good support for exploring and tracking the neighborhood of an IR node through the compilation phases (“bottom-up exploration”), it could do a better job at presenting the overall structure and components (e.g. loops) of the program under compilation for top-down exploration.
If you have ever used IGV, it is never too late to tell us about your experience with the tool and what could be improved in general. Even better, if you want to get further involved in the improvement work, there is always something to be done, from reporting issues at the JDK Bug System to actually picking up IGV improvement tasks. Some of these tasks, marked with the label “starter”, are particularly suitable for newcomers who want to learn and contribute to OpenJDK: improving IGV is a more fun and less intimidating path towards learning the internals of a large and complex compiler than hacking the compiler itself.
Acknowledgements: thanks to David Therkelsen for providing feedback on an earlier version of this post.