A highly productive platform accelerates the production of research results. The design of a Virtual Machine (VM) written in the Java TM programming language can be simplified through exploitation of interfaces, type and memory safety, automated memory management (garbage collection), exception handling, and reflection. Moreover, modern Java IDEs offer time-saving features such as refactoring, auto-completion, and code navigation. Finally, Java annotations enable compiler extensions for low-level "systems programming" while retaining IDE compatibility. These techniques collectively make complex system software more "approachable" than has been typical in the past.The Maxine VM, a metacircular Java VM implementation, has aggressively used these features since its inception. A co-designed companion tool, the Maxine Inspector, offers integrated debugging and visualization of all aspects of the VM's runtime state. The Inspector's implementation exploits advanced Java language features, embodies intimate knowledge of the VM's design, and even reuses a significant amount of VM code directly. These characteristics make Maxine a highly approachable VM research platform and a productive basis for research and teaching.

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Method inlining is considered to be one of the most important optimizations in a compiler. However, a poor inlining heuristic can lead to significant degradation of a program's running time. Therefore, it is important that an inliner has an effective heuristic that controls whether a method is inlined or not. An important component of any inlining heuristic are the features that characterize the inlining decision. These features often correspond to the caller method and the callee methods. However, it is not always apparent what the most important features are for this problem or the relative importance of these features. Compiler writers developing inlining heuristics may exclude critical information that can be obtained during each inlining decision.In this paper, we use a machine learning technique, namely neuro-evolution [18], to automatically induce effective inlining heuristics from a set of features deemed to be useful for inlining. Our learning technique is able to induce novel heuristics that significantly out-perform manuallyconstructed inlining heuristics. We evaluate the heuristic constructed by our neuro-evolutionary technique within the highly tuned Java HotSpot server compiler and the Maxine VM C1X compiler, and we are able to obtain speedups of up to 89% and 114%, respectively. In addition, we obtain an average speedup of almost 9% and 11% for the Java HotSpot VM and Maxine VM, respectively. However, the output of neuro-evolution, a neural network, is not human readable. We show how to construct more concise and readable heuristics in the form of decision trees that perform as well as our neuro-evolutionary approach.

The smallest complete Java™ virtual machine implementations in use today are based on the CLDC standard and are deployed in mobile phones and PDAs. These implementations require several tens of kilobytes. Smaller Java-like implementations also exist, but these involve compromises in Java semantics. This paper describes a JVM™ architecture designed for very small devices. It supports all the CLDC Java platform semantics, including exact garbage collection, dynamic class loading, and verification. For portability and ease of debugging, the entire system is written in the Java language, with key components automatically translated into C and compiled for the target device. The resulting system will run on the next generation of smart cards, and has performance comparable to the reference CLDC implementation available from Sun™.

The smallest complete Java™ virtual machine implementations in use today are based on the CLDC standard and are deployed in mobile phones and PDAs. These implementations require several tens of kilobytes. Smaller Java-like implementations also exist, but these involve compromises in Java semantics. This paper describes a JVM™ architecture designed for very small devices. It supports all the CLDC Java platform semantics, including exact garbage collection, dynamic class loading, and verification. For portability and ease of debugging, the entire system is written in the Java language, with key components automatically translated into C and compiled for the target device. The resulting system will run on the next generation of smart cards, and has performance comparable to the reference CLDC implementation available from Sun™.