Software systems have been using “just-in-time ” compilation (JIT) techniques since the 1960s. Broadly, JIT compilation includes any translation performed dynamically, after a program has started execution. We examine the motivation behind JIT compilation and constraints imposed on JIT compilation systems, and present a classification scheme for

Interpreters designed for efficiency execute a huge number of indirect branches and can spend more than half of the execution time in indirect branch mispredictions. Branch target buffers are the best widely available form of indirect branch prediction; however, their prediction accuracy for existing interpreters is only 2%–50%. In this paper we investigate two methods for improving the prediction accuracy of BTBs for interpreters: replicating virtual machine (VM) instructions and combining sequences of VM instructions into superinstructions. We investigate static (interpreter buildtime) and dynamic (interpreter run-time) variants of these techniques and compare them and several combinations of these techniques. These techniques can eliminate nearly all of the dispatch branch mispredictions, and have other benefits, resulting in speedups by a factor of up to 3.17 over efficient threaded-code interpreters, and speedups by a factor of up to 1.3 over techniques relying on superinstructions alone.

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SableVM is an open-source virtual machine for Java, intended as a r esearch framework for efficient execution of Java bytecode. The framework is essentially composed of an extensible bytecode interpreter using state-of-the-art and innovative techniques. Written in the C programming language, and assuming minimal system dependencies, the interpreter emphasizes high-level techniques to support efficient execution. In particular, we introduce new data layouts for classes, virtual tables and object instances that reduce the cost of interface method calls to that of normal virtual calls, allow ecient garbage collection and light synchronization, and make effective use of memory space.

This thesis addresses the challenges of building a software system for general-purpose runtime code manipulation. Modern applications, with dynamically-loaded modules and dynamicallygenerated code, are assembled at runtime. While it was once feasible at compile time to observe and manipulate every instruction — which is critical for program analysis, instrumentation, trace gathering, optimization, and similar tools — it can now only be done at runtime. Existing runtime tools are successful at inserting instrumentation calls, but no general framework has been developed for fine-grained and comprehensive code observation and modification without high overheads. This thesis demonstrates the feasibility of building such a system in software. We present DynamoRIO, a fully-implemented runtime code manipulation system that supports code transformations on any part of a program, while it executes. DynamoRIO uses code caching technology to provide efficient, transparent, and comprehensive manipulation of an unmodified application running on a stock operating system and commodity hardware. DynamoRIO executes large, complex, modern applications with dynamically-loaded, generated, or even modified code. Despite the

Virtual machines face significant performance challenges beyond those confronted by traditional static optimizers. First, portable program representations and dynamic language features, such as dynamic class loading, force the deferral of most optimizations until runtime, inducing runtime optimization overhead. Second, modular

Dynamic languages such as JavaScript are more difficult to compile than statically typed ones. Since no concrete type information is available, traditional compilers need to emit generic code that can handle all possible type combinations at runtime. We present an alternative compilation technique for dynamically-typed languages that identifies frequently executed loop traces at run-time and then generates machine code on the fly that is specialized for the actual dynamic types occurring on each path through the loop. Our method provides cheap inter-procedural type specialization, and an elegant and efficient way of incrementally compiling lazily discovered alternative paths through nested loops. We have implemented a dynamic compiler for JavaScript based on our technique and we have measured speedups of 10x and more for certain benchmark programs.

Abstract. OpenJIT is an open-ended, reflective JIT compiler framework for Java being researched and developed in a joint project by Tokyo Inst. Tech. and Fujitsu Ltd. Although in general self-descriptive systems have been studied in various contexts such as reflection and interpreter/compiler bootstrapping, OpenJIT is a first system we know to date that offers a stable, full-fledged Java JIT compiler that plugs into existing monolithic JVMs, and offer competitive performance to JITs typically written in C or C++. This is in contrast to previous work where compilation did not occur in the execution phase, customized VMs being developed ground-up, performance not competing with existing optimizing JIT compilers, and/or only a subset of the Java language being supported. The main contributions of this paper are, 1) we propose an architecture for a reflective JIT compiler on a monolithic VM, and identify the technical challenges as well as the techniques employed, 2) We define an API that adds to the existing JIT compiler APIs in “classic ” JVM to allow reflective JITs to be constructed, 3) We show detailed benchmarks of run-time behavior of OpenJIT to demonstrate that, while being competitive with existing JITs the time- and space-overheads of compiler metaobjects that exist in the heap are small and manageable. Being an object-oriented compiler framework, OpenJIT can be configured to be small and portable or fully-fledged optimizing compiler framework in the spirit of SUIF. It is fully JCK compliant, and runs all large Java applications we have tested to date including HotJava. We are currently distributing OpenJIT for free to foster further research into advanced compiler optimization, compile-time reflection, advanced run-time support for languages, as well as other areas such as embedded computing, metacomputing, and ubiquitous computing. 1

Virtual machines (VMs) are commonly used to distribute programs in an architecture-neutral format, which can easily be interpreted or compiled. A long-running question in the design of VMs is whether stack architecture or register architecture can be implemented more efficiently with an interpreter. We extend existing work on comparing virtual stack and virtual register architectures in two ways. Firstly, our translation from stack to register code is much more sophisticated. The result is that we eliminate an average of more than 47 % of executed VM instructions, with the register machine bytecode size only 25 % larger than that of the corresponding stack bytecode. Secondly we present an implementation of a register machine in a fully standardcompliant implementation of the Java VM. We find that, on the Pentium 4, the register architecture requires an average of 32.3 % less time to execute standard benchmarks if dispatch is performed using a C switch statement. Even if more efficient threaded dispatch is available (which requires labels as first class values), the reduction in running time is still approximately 26.5 % for the register architecture.

Abstract. Inline-threaded interpretation is a recent technique that improves performance by eliminating dispatch overhead within basic blocks for interpreters written in C [11]. The dynamic class loading, lazy class initialization, and multi-threading features of Java reduce the effectiveness of a straight-forward implementation of this technique within Java interpreters. In this paper, we introduce preparation sequences, a new technique that solves the particular challenge of effectively inline-threading Java. We have implemented our technique in the SableVM Java virtual machine, and our experimental results show that using our technique, inline-threaded interpretation of Java, on a set of benchmarks, achieves a speedup ranging from 1.20 to 2.41 over switch-based interpretation, and a speedup ranging from 1.15 to 2.14 over direct-threaded interpretation. 1