Abstract. Optimizing compilers for object-oriented languages apply static class analysis and other techniques to try to deduce precise information about the possible classes of the receivers of messages; if successful, dynamicallydispatched messages can be replaced with direct procedure calls and potentially further optimized through inline-expansion. By examining the complete inheritance graph of a program, which we call class hierarchy analysis, the compiler can improve the quality of static class information and thereby improve run-time performance. In this paper we present class hierarchy analysis and describe techniques for implementing this analysis effectively in both statically- and dynamically-typed languages and also in the presence of multi-methods. We also discuss how class hierarchy analysis can be supported in an interactive programming environment and, to some extent, in the presence of separate compilation. Finally, we assess the bottom-line performance improvement due to class hierarchy analysis alone and in combination with two other “competing ” optimizations, profileguided receiver class prediction and method specialization. 1

The JalapeÃño Dynamic Optimizing Compiler is a key component of the JalapeÃño Virtual Machine, a new Java Virtual Machine (JVM) designed to support efficient and scalable execution of Java applications on SMP server machines. This paper describes the design of the JalapeÃño Optimizing Compiler, and the implementation results that we have obtained thus far. To the best of our knowledge, this is the first dynamic optimizing compiler for Java that is being used in a JVM with a compile-only approach to program execution.

This paper addresses the problem of resolving virtual method and interface calls in Java bytecode. The main focus is on a new practical technique that can be used to analyze large applications. Our fundamental design goal was to develop a technique that can be solved with only one iteration, and thus scales linearly with the size of the program, while at the same time providing more accurate results than two popular existing linear techniques, class hierarchy analysis and rapid type analysis. We present two variations of our new technique, variable-type analysis and a coarser-grain version called declared-type analysis. Both of these analyses are inexpensive, easy to implement, and our experimental results show that they scale linearly in the size of the program. We have implemented our new analyses using the Soot framework, and we report on empirical results for seven benchmarks. We have used our techniques to build accurate call graphs for complete applications (including librarie...

Dynamic code generation is the creation of executable code at runtime. Such “on-the-fly” code generation is a powerful technique, enabling applications to use runtime information to improve performance by up to an order of magnitude [4, 8, 20, 22, 23]. Unfortunately, previous general-purpose dynamic code generation systems have been either inefficient or non-portable. We present VCODE, a retargetable, extensible, very fast dynamic code generation system. An important feature of VCODE is that it generates machine code “in-place ” without the use of intermediate data structures. Eliminating the need to construct and consume an intermediate representation at runtime makes VCODE both efficient and extensible. VCODE dynamically generates code at an approximate cost of six to ten instructions per generated instruction, making it over an order of magnitude faster than the most efficient general-purpose code generation system in the literature [10]. Dynamic code generation is relatively well known within the compiler community. However, due in large part to the lack of a publicly available dynamic code generation system, it has remained a curiosity rather than a widely used technique. A practical contribution of this work is the free, unrestricted distribution of the VCODE system, which currently runs on the MIPS, SPARC, and Alpha architectures.

Dynamic code generation allows specialized code sequences to be crafted using runtime information. Since this information is by definition not available statically, the use of dynamic code generation can achieve performance inherently beyond that of static code generation. Previous attempts to support dynamic code generation have been low-level, expensive, or machine-dependent. Despite the growing use of dynamic code generation, no mainstream language provides flexible, portable, and efficient support for it.

Concrete types and abstract types are different and serve different purposes. Concrete types, the focus of this paper, are essential to support compilation, application delivery, and debugging in object-oriented environments. Concrete types should not be obtained from explicit type declarations because their presence limits polymorphism unacceptably. This leaves us with type inference. Unfortunately, while polymorphism demands the use of type inference, it has also been the hardest challenge for type inference. We review previous type inference algorithms that analyze code with parametric polymorphism and then present a new one: the cartesian product algorithm. It improves precision and efficiency over previous algorithms and deals directly with inheritance, rather than relying on a preprocessor to expand it away. Last, but not least, it is conceptually simple. The cartesian product algorithm has been used in the Self system since late 1993. We present measurements to document its pe...

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The use of dynamically-dispatched procedure calls is a key mechanism for writing extensible and flexible code in object-oriented languages. Unfortunately, dynamic dispatching imposes a runtime performance penalty. Some recent implementations of pure object-oriented languages have utilized profile-guided receiver class prediction to reduce this performance penalty, and some researchers have argued for applying receiver class prediction in hybrid languages like C++. We performed a detailed examination of the dynamic profiles of eight large object-oriented applications written in C++ and Cecil, determining that the receiver class distributions are strongly peaked and stable across both inputs and program versions through time. We describe techniques for gathering and manipulating profile information at varying degrees of precision, particularly in the presence of optimizations such as inlining. Our implementation of profile-guided receiver class prediction in the Cecil compiler improves t...

The current trend in operating systems research is to allow applications to dynamically extend the kernel to improve application performance or extend functionality, but the most effective approach to extensibility remains unclear. Some systems use safe languages to permit code to be downloaded directly into the kernel; other systems provide in-kernel interpreters to execute extension code; still others use software techniques to ensure the safety of kernel extensions. The key characteristics that distinguish these systems are the philosophy behind extensibility and the technology used to implement extensibility. This paper presents a taxonomy of the types of extensions that might be desirable in an extensible operating system, evaluates the performance cost of various extension technologies currently being employed, and compares the cost of adding a kernel extension to the benefit of having the extension in the kernel. Our results show that compiled technologies (e.g. Modula-3 and sof...

Variables and instructions that have invariant or predictable values at run-time, but cannot be identified as such using compiler analysis, can benefit from value-based compiler optimizations. Value-based optimizations include all optimizations based on a predictable value or range of values for a variable or instruction at run-time. These include constant propagation, code specialization, optimizations assuming the value predictability of an instruction, continuous optimization, and partial evaluation. This paper explores...