The Ovm framework is a set of tools and components for building language runtimes. We present the intermediate representation and software design patterns used throughout the framework. One of the main themes in this work has been to support experimentation with new linguistic constructs and implementation techniques. To this end, framework components were designed to be parametric with respect to the instruction set on which they operate. We argue that our approach eases the task of writing new components without sacri cing eciency.

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Implementing a concurrent programming language such as Java by the means of a translator to an existing language is attractive as it provides portability over all platforms supported by the host language and reduces development time – as many low-level tasks can be delegated to the host compiler. The C and C++ programming languages are popular choices for many language implementations due to the availability of efficient compilers on a wide range of platforms. For garbage-collected languages, however, they are not a perfect match as no support is provided for accurately discovering pointers to heap-allocated data on thread stacks. We evaluate several previously published techniques, and propose a new mechanism, lazy pointer stacks, for performing accurate garbage collection in such uncooperative environments. We implemented the new technique in the Ovm Java virtual machine with our own Java-to-C/C++ compiler using GCC as a back-end compiler. Our extensive experimental results confirm that lazy pointer stacks outperform existing approaches: we provide a speed-up of 4.5 % over Henderson’s accurate collector with a 17 % increase in code size. Accurate collection is essential in the context of real-time systems, we thus validate our approach with the implementation of a real-time concurrent garbage collection algorithm. 1.

The power of high-level languages lies in their abstraction over hardware and software complexity, leading to greater security, better reliability, and lower development costs. However, opaque abstractions are often show-stoppers for systems programmers, forcing them to either break the abstraction, or more often, simply give up and use a different language. This paper addresses the challenge of opening up a high-level language to allow practical low-level programming without forsaking integrity or performance. The contribution of this paper is three-fold: 1) we draw together common threads in a diverse literature, 2) we identify a framework for extending high-level languages for low-level programming, and 3) we show the power of this approach through concrete case studies. Our framework leverages just three core ideas: extending semantics via intrinsic methods, extending types via unboxing and architectural-width primitives, and controlling semantics via scoped semantic regimes. We develop these ideas through the context of a rich literature and substantial practical experience. We show that they provide the power necessary to implement substantial artifacts such as a high-performance virtual machine, while preserving the software engineering benefits of the host language. The time has come for high-level low-level programming to be taken more seriously: 1) more projects now use high-level languages for systems programming, 2) increasing architectural heterogeneity and parallelism heighten the need for abstraction, and 3) a new generation of high-level languages are under development and ripe to be influenced.

By January 1998, only two years after the launch of the first Java virtual machine, almost all JVMs in use today had been architected. In the nine years since, technology has advanced enormously, with respect to the underlying hardware, language implementation, and in the application domain. Although JVM technology has moved forward in leaps and bounds, basic design decisions made in the 90’s has anchored JVM implementation. The Moxie project set out to explore the question: ‘How would we design a JVM from scratch knowing what we know today?’ Amid the mass of design questions we faced, the tension between performance and flexibility was pervasive, persistent and problematic. In this experience paper we describe the Moxie project and its lessons, a process which began with consulting experts from industry and academia, and ended with a fully working prototype.

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